Program overview. 21-Jun :00. Year 2009/2010 Mechanical, Maritime and Materials Engineering Master Mechanical Engineering

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Program overview

21-Jun-2016 21:00

Year

2009/2010

Organization

Mechanical, Maritime and Materials Engineering

Education

Master Mechanical Engineering

Code

Omschrijving

ECTS

p1 p2 p3 p4 p5

Specialisation Intelligent Mechanical Systems (ME-BMD-IMS)

Obligatory Courses ME-BMD-IMS

SC4070 Control Systems Lab 4

WB2436-05 Bio-Inspired Design 3

WB5414-08 Design of Machines and Mechanisms 4

WB5430-05 Engineering Informatics 3

WB5431-05 Life Cycle Engineering 3

Recommended Elective Courses ME-BMD-IMS

ET4245ME Elektromechanics in Mechatronic Systems 3

ID4125 Life Cycle Engineering and Design 6

IN4073TU Embedded Real-Time Systems 6

ME1120 Space Robotics 4

SC4060 Predictive and adaptive control 4

SC4081 Knowledge Based Control Systems 4

SC4091 Optimization in Systems and Control 4

SC4110 System Identification 5

SC4120 Special Topics in Signals, Systems & Control 3 SC4150 Fuzzy Logic and Engineering Applications 3

WB1310 Multibody Dynamics A 3

WB1406-07 Experimental Dynamics 3

WB1416 Numerical Methods for Dynamics 3

WB1418-07 Engineering Dynamics 4

WB1440 Eng. Optimization: Concept & Applications 3

WB1442-08 Microsystems 3

WB1444-07 Advanced Micro Electronic Packaging 3 WB1445-05 Mechanics of Micro Electronics and Microsystems 3 WB2301-5 System Identification and Parameter Estimation 7

WB2305 Digital Control 3

WB2414-09 Mechatronic System Design 4

WB2415 Robust Control 6

WB2427 Predictive Modelling 3

WB2428-03 Mechanical Design in Mechatronics 5

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1.

Year

2009/2010

Organization

Education

Specialisation Intelligent Mechanical Systems (ME-BMD-IMS)

Program Coordinator Dr.ir. M. Wisse

Introduction 1 Contemporary machines are required to deliver superior performance in terms of function, cost and quality at every stage in their lifecycle, not only in operations but also in maintenance and even at the end of their life. They must be easy to use, intelligent in their interaction with humans, robust and fault-tolerant, easy to maintain even if a fault happens, ready for reconfiguration to cope with increasingly changeable situations and sustainable from the environmental point of view. This requires machines to be designed and built based on innovative design principles, in particular by learning from biological systems and by embedding intelligence. Applications of these principles include industrial machinery, transportation machines, office equipment, home appliances or any mechatronics products. For example, we can think about a new generation of production robots with some basic intelligence to communicate with other robots and to decide for themselves what will be the best strategy to cope with demands on the production line. Students learn to structure data processing between multiple robots in order to optimise their "team performance", inspired by biological examples like ant colonies. Information acquisition, reasoning -based decision making and active control of intelligent mechanical systems are key features of this specialisation.

This can be extended to supervisory control of complex systems. This topic of human-machine interaction is part of the IMS specialisation.

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Year

2009/2010

Organization

Education

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SC4070

Control Systems Lab

4

Responsible Instructor Dr.ir. G. Schitter

Instructor Prof.dr. R. Babuska Contact Hours / Week

x/x/x/x

0/0/4/0

Education Period 3

Start Education 3

Exam Period Different, to be announced

Course Language English

Expected prior knowledge Control Systems (SC3020ET) or similar

Course Contents In this course, students have the opportunity to design and implement their own controllers for various laboratory systems (helicopter model, inverted pendulum, inverted wedge, gantry crane). In this way, they gain more insight in the use of control theory and gain experience with the practical implementation of computer-controlled systems. MATLAB and SIMULINK are used as the basic platform for the design, analysis, simulation and real-time implementation. The control design methods to be used include standard techniques (digital state feedback, output feedback, PID control) as well as more advanced methods (adaptive control, linear quadratic control, systems identification). In the beginning of the course, a refresher is given in which the essential topics from theoretical control courses are reviewed. See also: http://www.dcsc.tudelft.nl/~sc4070

Study Goals Main objective: make operational and apply in practice the knowledge from control theory and system identification courses. Gain hands-on experience with the design and implementation of a computer-controlled system.

After successfully completing the course, the student is able to:

* Implement in Matlab / Simulink a given mathematical model of a mechatronic laboratory system. Estimate unknown parameters in the model by using experimental data measured on the process. Validate the model against measured process data. * Linearize the model around an operating point. Assess the accuracy of the linearized model with respect to the nonlinear one and with respect to the real process. Identify limitations of the linearized model. Choose a suitable sampling period, discretize the linearized model.

* Define meaningful performance specifications for a control system to be designed for the given process. Selected a suitable type of controller. Compute the controller's parameters such that the above specifications are met. Verify the closed-loop performance in realistic simulations.

* Apply the controller to the process in real-time experiments. Evaluate the performance of the control system. Identify reasons for possible discrepancies between simulations and real-time results. Suggest possible improvements.

* Demonstrate proficiency in using Matlab and Simulink as the primary tool for the achievement of the above objectives. * Document the design steps, considerations, choices and the achieved control results effectively in a written report. Present and defend the results in an oral presentation.

Education Method Lectures, laboratory sessions

Literature and Study Materials

Book: Åström K.J. and Wittenmark B. Computer Controlled Systems Theory and Design (Third Edition). Prentice Hall, 1997.

Assessment Written report, presentation

Remarks Computer use: laboratory assignment. Design content (60%): control design.

Department 3mE Department Delft Center for Systems and Control

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WB2436-05

Bio-Inspired Design

3

Responsible Instructor Prof.dr.ir. P. Breedveld

Responsible Instructor Prof.dr.ir. J.L. Herder Responsible Instructor Dr. T. Tomiyama Contact Hours / Week

x/x/x/x

0/0/4/0

Exam Period 3

Expected prior knowledge Completed courses in mechanics and design

Course Contents The course Bio-Inspired Design gives an overview of non-conventional mechanical approaches in nature and shows how this knowledge can lead to more creativity in mechanical design and to better (simpler, smaller, more robust) solutions than with conventional technology. The course discusses a large number of biological organisms with smart constructions, unusual mechanisms or clever processing methods and gives a number of technical examples of bio-inspired instruments and machines. Examples of topics:

Strength at low weight, stiffness with soft structures, robustness with redundancy, simple laws for complex behaviour, storing energy in springs, energetically efficient muscle configurations, biological vibration systems, clamping with hands, claws, suction, glue or dry-adhesion, biological walking, swimming and crawling methods, locomotion of micro- and single-celled-organisms.

Structure of the course: 1. Bioconstruction 1.1. Biostructure 1.2. Bioenergy

1.3. Bioreproduction & regeneration 1.4. Biomaintenance & repair 2. Biomotion 2.1. Bioclamping 2.2. Biopropulsion at macroscale 2.3. Biopropulsion at microscale 3. Bioprocessing 3.1. Biosensing 3.2. Biobehaving

Study Goals The student must be able to: 1.describe methods for creative design

2.identify mechanical working principles and phenomena of biological creatures explain their construction, motion, and/or processing mechanisms

formalize the essence of these mechanisms in models derive non-conventional design principles from these models 3.implement these design principles in innovative mechanical devices summarize the transition process from the biological to the mechanical domain present their design in drawings or preferably in working models

Education Method Lectures, assignment

Computer Use Not Applicable

Literature and Study Materials

Handouts

Assessment Final exam will take place in form of presentation during the exam period. After the presentation, students have to hand-in a written paper.

Remarks Students are subdivided in a number of groups. Each group gets a different assignment in which a biological solution for a technical problem has to be found. During the course, each group gives three presentations: one about the problem, one about the proposed solution and one about the final solution. The final mark is based on the final presentation and a written paper describing the biological solution of the problem.

Percentage of Design 100%

Design Content The course gives knowledge about innovative mechanical designs inspired by biological systems and phenomena, in addition to design exercises.

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WB5414-08

Design of Machines and Mechanisms

4

Responsible Instructor Dr. T. Tomiyama

Instructor Prof.dr.ir. J.L. Herder Contact Hours / Week

x/x/x/x 2/2/0/0 Education Period 1 2 Start Education 1 Exam Period 2

Course Contents 1. Introduction (Grouping, Assignments) 2. Conceptual Design of Machines (first quarter) - Design Methods

- Requirement Analysis

- Function Modeling and Function Decomposition - Generating Concepts

- Evaluation of Concepts - Selection of Solutions 3. Design of Mechanisms - Diagram of Motion - Diagram of Goal Functions - Available Mechanism Types

- Type- and Dimension Synthesis of Mechanisms 4. Presentation of Assignments

5. Industrial Application of Mechanization and Mechanisms (Factory Visit)

Study Goals The student must be able to:

1. describe the conceptual design process for systematic design perform requirement analysis and build function structure

derive physical phenomena necessary for achieving required function and combine different options to systematically develop different candidate solutions

compare different candidate solutions and choose the best solution 2. describe the basic design process of mechanisms

calculate the performance of various kinds of mechanisms (such as four bar link, cam, gear pairs, etc.) with software packages for mechanisms design

determine the dimensions and other design parameters of a mechanism

3. employ these design methods for a real industrial problem in a teamwork environment perform the design task at the both conceptual and basic design levels in a team present their design in drawings or as a CAD model

Education Method Project: Students will be divided into groups of 4 to 5 students and each group is given its assignment.

At every lecture, in principle, first half of lecture hours is used for presenting students homework and the other for instructions. During presentation of homework, students are expected to participate in discussions actively.

Computer Use Use of dedicated PC software. Software programs will become available for downloading from the blackboard.

Literature and Study Materials

Lecture notes wb5414 (in Dutch available from the blackboard).

Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H: Engineering Design, A Systematic Approach (Third Edition), Translated by K. Wallace and L. Blessing, Springer, London, ISBN: 978-1-84628-318-5, (2007). Available from TU Delft Library as an e-book. Other appropriate literature and software programs will be specified during the lectures and uploaded to the Blackboard.

Books Lecture notes wb5414 (in Dutch available from the blackboard).

Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H: Engineering Design, A Systematic Approach (Third Edition), Translated by K. Wallace and L. Blessing, Springer, London, ISBN: 978-1-84628-318-5, (2007). Available from TU Delft Library as an e-book.

Assessment Attendance (compulsory) including a factory visit scheduled at the end of the semester or the beginning of 2A: if you are absent twice, the end of the story.

Written reports (intermediate and final).

Final presentation (taking place during the exam period).

Enrolment / Application Since this course involves team working, good command of English is required. In particular, foreign students should make sure that their English level is high enough for intensive communication with teachers and other students.

While any specific knowledge about machine design is not required, it is desirable that students have some experiences of machine design (such as BSc mechanical engineering design courses and projects).

Remarks During the course, a real industrial design case will be assigned to a group of students. Attendance is obligatory, including a factory visit planned at the end of the lecture.

The project has two parts, conceptual design (largely following the Pahl & Beitz method) and mechanisms design (using various analysis and synthesis software).

Design Content Design of industrial machinery for discrete production (mechanization). Design aspects: technical and economical demands, conceptual design, finding mechanisms to perform the required motions (synthesis), analysis and evaluation of solutions.

Department 3mE Department Biomechanical Engineering

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WB5430-05

Engineering Informatics

3

Contact Hours / Week x/x/x/x

0/4/0/0

Exam Period 2

Required for Machine Intelligence (Wb 5435-05)

Expected prior knowledge Computer programming courses

Course Contents The aims of this course are twofold. One is to give fundamental knowledge about computer systems including both hardware and software. The other is to give theoretical foundations behind computer-based engineering tools and systems which play an increasingly important role in mechanical engineering.

The course comprises of lectures in a classroom and practices in the form of homework. It emphasizes homework (mostly programming) that will be included in the final evaluation. While no preference is given to a particular programming language, basic programming capabilities are needed.

Topics:

1. Fundamental Logic and the Definition of Engineering Tasks 2. Fundamentals of Semiconductors and Logic Gates 3. Fundamentals of Computer Architecture 4. Fundamentals of Operating Systems 5. Data Representation and Data Structures 6. Numerical Computation and Computational Errors 7. Computational Complexity

8. Object Representation and Reasoning 9. Databases Concepts

10. Constraint-based Problem Solving 11. Optimization and Search 12. Discrete Event Simulation 13. Geometric Modeling and CAD

14. Industrial Engineering Information Systems (PDM, ERP, SCM, LCM)

1.describe fundamental principles of computers systems including both hardware and software illustrate mechanisms for digital computers

explain software architecture and its working principles illustrate data representation methods and data structure analyze computational errors and computational complexity

2.describe theoretical foundations of modeling and computing behind computer-based engineering tools

explain such data modeling principles as object oriented representation and programming, relational data model, and entity-relationship data model

explain an appropriate computing algorithm for constraint-based problem solving, optimization, search, and discrete event simulation

explain fundamentals of geometric modeling

illustrate architecture and functionalities of industrial engineering information systems such as PDM (Product Data Modeling), ERP (Enterprise Resource Planning), SCM (Supply Chain Management), and LCM (Life Cycle Modeling)

Education Method Lectures (4 hours per week) plus regular homework assignments (around ten homeworks, individual work), final-homeworks (three final-homeworks, individual and creative self implementation of the techniques in programming enviroenments).

Computer Use Access to a programming environment (any language of your choice, such as C++, C, Visual Basic, Java, MATLAB, etc.) is necessary.

Literature and Study Materials

Benny Raphael, Ian F. C. Smith, Fundamentals of Computer Aided Engineering, ISBN: 0-471-48715-5, (2003), Wiley & Sons.

Assessment Assessment will be based on the three final-homework assignments and regular homework assigments.

In order to pass this course, students have to submit all homework assignments as well as the final ones. (If you miss one, you don't pass.) In case a student did not pass in the previous year, he/she needs to re-submit all homework assignments and final ones on time even if questions are the same. There is no automatic carry-over of grades from previous years.

Homework assignments (around ten homeworks, individual work), around 30%.

Final-homework (three final-homeworks, individual and creative self implementation of the techniques in programming environment), around 70%.

The ratio is variable year to year.

The students will need on average and approximately ten hours per homework and two hours per homework. The final-homeworks will test the practical and creative capabilities of implementation on computer; the final-homeworks will test the theoretical knowledge.

Remarks

Design Content Although the course does not directly aim at "design of software", it will nonetheless include principles of building engineering applications.

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WB5431-05

Life Cycle Engineering

3

Contact Hours / Week x/x/x/x

0/0/0/4

Exam Period 4

Course Contents This course deals with fundamentals and technology of life cycle engineering that require a systematic and holistic approaches to product life cycles, rather than just end-of-pipe technologies.

First, we will discuss the fundamental concepts of life cycle engineering, in particular, the relationships among environment, design, manufacturing, and economy. Second, we will look at details of life cycle stages including marketing, design, production, logistics, operation (use), maintenance, recovery, reuse, remanufacturing, and recycling. Third, we will discuss the motivation behind life cycle engineering and its philosophy. We will understand that in particular design has a big influence on any other aspect of product life cycle. Fourth, we will particularly highlight maintenance and remanufacturing. Fifth, we will look at design methodologies (Design for Environment) as a technology.

Homeworks and excersises are important part of evaluation. Contents

1. Introduction

2. Environment, Design, Manufacturing and Economy 3. Basic Concepts

4. Product Life Cycle Stages 5. Business and Environment

LCA, Tools (SCM, Green Purchase, ISO 14000 Series, Benchmarking) 6. DfX (Design for X), DfE (Design for Environment)

7. Maintenance and Self-Maintenance 8. Recycling

9. Remanufacturing and Reuse

10. Life Cycle Simulation and Life Cycle Design 11. Service Engineering and Product-Service Systems 12. Summary

1.describe fundamental principles and philosophy toward a sustainable society from the viewpoint of manufacturing explain the relationships among environment, design, manufacturing, and economy

classify and compare various strategies toward a sustainable society

explain various tools related to sustainability, such as LCA, Green Purchase, ISO 14000 series, etc.) 2.identify the motivation and background philosophy of life cycle engineering

3.illustrate details of product life cycle stages, including marketing, design, production, logistics, operation (use), maintenance, recovery, reuse, remanufacturing, and recycling

explain, among other things, the roles of design in a product life cycle explain, among other things, the roles of maintenance in a product life cycle

explain, among other things, the roles of remanufacturing, reuse, and recycling in a product life cycle 4.explain various methods of Design for Environment through concrete examples

Education Method Lectures (4 hours per week) including homework assignments (around 5) and a design for environment exercise (group work).

Literature and Study Materials

Powerpoint presentations. A copy of the presentaiton will be available through the Blackboard. Any other handouts.

Recommended Book: T.E. Graedel and B.R. Allenby: Industrial Ecology (2nd Edition), Pearson Education, Inc., New Jearsey (2003), ISBN 0-13-046713-8 (58 at Amazon)

Assessment Assessment includes three components.

1. Homework (individual, around 20%): Students need to submit all homework assignments on time. If you did not pass in previous years, you still need to re-submit homework assignments. If the question is the same, you can resubmit your old assignments.

2. Design for Environment exercise (group work, around 20%): Students will be given a DfE task and will need to present during the lecture and to submit a mini report.

3. Final exams (individual, around 60%): Written/Oral exams. The ratio is variable year to year.

Design Content A large portion of the course deals with sustainability issues in design.

3mE Department Precision & Microsystems Engineering

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Year

2009/2010

Organization

Education

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ET4245ME

Elektromechanics in Mechatronic Systems

3

Responsible Instructor Dr.ir. H. Polinder

Instructor Ir. J.W. Spronck Contact Hours / Week

x/x/x/x 0/0/0/3 Education Period 4 Start Education 4 Exam Period 4 5

Course Contents Electromechanics in mechatronic systems

Study Goals Students who have followed this course should be able to 1 Use the terminology of electromechanics.

2 Use the principles of electric and magnetic circuits to calculate voltages, currents, magnetic flux densities, magnetic fluxes, magnetic flux linkages, forces, torques, power, and (stored) energy.

3 Recognize different types of permanent-magnet machines, derive the voltage equations and the equivalent circuits, sketch the characteristic voltage and current waveforms and calculate forces and torques using the power balance.

4 Recognize magnetic bearings, explain their strength and weaknesses, calculate magnetic bearing forces from the power balance, explain that magnetic bearings can be linearised by making them double-sided, explain zero-stiffness and gravity compensation.

5 Explain the important limitations and characteristics of materials (magnets, iron, conductors), and machines (losses and heat dissipation, mechanical commutation, safe operating area, cogging, force density), indicate if there are methods to get around these limitations and do calculations on these limitations.

6 Distinguish between the different construction forms of permanent-magnet machines and explain their strengths and weaknesses.

7 Explain which criteria play an important role in choosing a machine and how these criteria influence the choice, explain which part of the safe operating area is most attractive and why, explain which criteria play a role in selecting transmissions and in selection motional profiles.

8 Explain why linear motor are used, recognize different types of linear permanent-magnet machines, derive the voltage equations and the equivalent circuits, calculate forces.

9 Recognise different types of amplifiers (analogue, switching, one quadrant, four quadrant, resonant) and know their strength and weaknesses, sketch current and voltage waveforms of switching amplifiers, explain how they can be used to control speed.

Education Method Lectures, assignments, demonstrations

Literature and Study Materials

J.C. Compter, 'Mechatronics, Introduction to Electromechanics', lecture notes

Assessment Written examination (closed book) or group assignment

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ID4125

Life Cycle Engineering and Design

6

Responsible Instructor Prof.dr.ing. R. Wever

Contact Hours / Week x/x/x/x 0/0/2/2 Education Period 3 4 Start Education 3 Exam Period 4 5

Course Contents The aim of Life Cycle Engineering and Design is to challenge students to think in life cycles instead of products. Several phases of a product life cycle will receive specific attention through lectures which will focus on the state-of-the-art of that field. Furthermore tools for designers to deal with life cycles, or phases thereof, will receive attention, as will the perspectives of different stakeholders related to the life cycle of products. Specific subjects can differ somewhat from year to year, but energy consumption of products in the use phase will receive explicit attention.

The knowledge in this course will provide a theoretical basis as well as input on how to make this basis operational in design projects, which could be the Project Advanced Products and graduation projects, but most importantly professional industrial design engineering careers.

Study Goals By the end of the course, the student will have acquired: Knowledge of the theory, concepts, practical approaches, methods and tools relevant to Life Cycle Engineering and Design; An insight into Life Cycle Engineering and Design

from the perspective of practical realization in the industry environment, including product and technology benchmarking;

An insight into the possibilities of, and the design rules for, the integration of emerging technologies (energy-, materials-, nano-technology etc.) into products;

An insight into the perspectives of different stakeholders and their power to protect/enforce their interests; Skills that contribute to the successful simulation,

prototyping and evaluation of improved and new products, making maximum use of new technologies and Life Cycle Engineering and Design concepts and tools.

Education Method The course is taught through a series of interactive

lectures. Students are encouraged to read upfront material provided by the lecturer. For approximately six selected lectures students are required to supply questions to guest- lecturers based on this reading material.

Students are required to hand in two dilemma assignments, focusing on 1) design dilemmas they are expected to be confronted with in their PAP project (at the beginning of the course), and 2) focusing on how they handled those dilemmas (at the end of the course).

In addition, students are required to do a Life-Cycle

Stakeholder Analysis Assignment, for which relevant information, material and money flows during the lifecycle of a product need to be identified, analyzed and

presented, both from an economical and an environmental perspective.

It is encouraged that all assignments are done in relation to the Project Advanced Products cases, but it is also possible to participate in Life Cycle Engineering & Design independent of PAP; in this case substitute assignments will be provided. Students are also required to fulfill a dimensional analysis project.

The course will be concluded by a written exam.

Literature and Study Materials

(Pre-lecture) reading material, lecture slides, and additional sources made available via the Blackboard.

Assessment Handing in questions 10% of grade Dilemma Assignments 10% of grade

Life Cycle Stakeholder Analysis Assignment 25% of grade Dimensional Analysis 15% of grade

Written Exam 40% of grade

The written exam will be closed-book, and will mainly consist of open questions.

Special Information Prof. dr. ir. J.C. Brezet / ir. R. Wever, Room: 1A-40, phone +31 (0)15 27 82120 E-mail: [email protected]

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IN4073TU

Embedded Real-Time Systems

6

Responsible Instructor Prof.dr.ir. A.J.C. van Gemund

Contact Hours / Week x/x/x/x

0/0/4/0 Pract.

Exam Period none

Expected prior knowledge equivalent to IN4024 Real-time Systems / C programming course / in2305-ii Emb. Prog.

Course Contents The course provides an introduction to embedded systems programming. The course is heavily based on a lab project where students ( in competing teams) will have to develop an embedded control unit for a tethered electrical model quad-rotor aerial vehicle, in order to provide stabilization such that it can (idealy) hover and (slowly) fly with only limited user control (one joystick). The control algorithm (which is given) must be mapped onto a Linux PC (C) in conjunction with an FPGA board (embedded C and/or VHDL) that communicates with the sensors and actuators on the quad-rotor. The students will be exposed to simple physics/mechanics, electronics, sensors (gyros, accelerometers), actuators (motors, servos), basic control principles, quad-rotor simulators, and, most importantly, embedded software (C, VHDL), most of which each team is required to develop themselves. The project work (including written report) covers the entire duration of the course period, and will take approximately 80 hours, of which 28 hours are spent at the HLO lab facilities.

Study Goals Student is acquanted with real-time programming in an embedded context, along with a basic understanding of embedded systems, real-time communication, sensor data processing, actuator control, control theory, and simulation. Moreover, the student has had exposure to integrating the various multidisciplinary aspects at the system level.

Education Method Lectures, lab work

Literature and Study Materials

Web

Assessment Lab. project (120 hours) + written report

Remarks http://www.st.ewi.tudelft.nl/~gemund/Courses/In4073/index.html

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ME1120

Space Robotics

4

Responsible Instructor Dr.ing. A. Schiele

Responsible Instructor Dr.ir. M. Wisse Contact Hours / Week

x/x/x/x

0/0/0/2

Course Contents Overview to space robotics systems, design and requirements. This course will set the foundation to design space robotic systems and to understand the requirements specifically imposed on robots by application in non-terrestrial environments. The lecture provides an overview to some relevant basics about robotic manipulators in general and then prepares the students to consider particular constraints posed by temp., radiation and space robotic systems. Focus will lie on manipulator type of robotic applications, but also typical mobile robotics scenarios will be outlined.

Lect. 1:Introduction Robots in space;

Manipulators, Mobile robotics;

Purpose, goals, difference w.r.t. terrestrial robotic systems Lect. 2:Basics I: Homogeneous coordinates

Concept of homogeneous transformations, linear & rotational transforms

(Euler angles, quaternions), Denavit-Hertenberg Convention, 6 DOF forward and inverse kinematics (Assignment) Lect. 3:Basics II: Link velocity

Link velocity and velocity propagation, Jacobians (analytical, geometrical, numerical,), construction of Jacobian, Lect. 4:Basics III: Link forces & Redundancy

Link force propagation, force transformations

Manipulator redundancy, Manipulator & operational space, null space, redundancy resolution strategies, redundant inverse kinematics

Lect. 5:Exercises (Basics I-III) Lect. 6:Space environmental effects

Temperature Environment (effects on mechanical Systems), radiation environment (effects on electronic systems), launch and landing environments (examples), planetary surface environments

Lect. 7:Tribology in space

Basic effects, overview of models, selection of appropriate lubricants Lect. 8:Robotic actuators in space

DC, stepper and brushless motors, bearing and bushing modification, qualified motors, selection of actuators. Lect. 9:Sensors for manipulators in space

Position/Velocity Sensing, force sensors, strain gauges (layout and design), sensor electronics, Lect. 10:Testing for space mechatronics

Introduction to applicable standards, mechanical, thermal and electrical testing. (I/F load calculation, thermal modeling approaches, EMC)

Lect. 11:Applications I: Robotic planetary missions

Mission operation, examples about mission control (MER, Nanokhod) Lect. 12:Applications II: Orbital robotics

Operational modes: human-machine interfaces, examples of ERA/SSRMS, introduction to Telecontrol and Tele-operation concepts

Lect. 13/14:Lab assignment (TBC):

A: SRMS/SSRMS interfaces joystick (trl. Of 7 dof. Manipulators (PA.10, LBR4) B: Nullspace motion, resolution of 7 dof redundancy on LBR4

(A+B = final assignment)

Study Goals The students are capable:

* To identify, define and analyse problems of robots, vehicles and other mechanical systems in space * To design and produce a sound solution to typical space robotics problems

The following exit qualifications serve to realise this goal: The students meet the following qualifications:

* Basic knowledge of the problems of mechanical systems in space, i.e. related to tribology, actuators, mechatronics, sensors, thermodynamics, etc.

* Ability to set up motion equations for 3D mechanisms applicable in space and in general, calculation of kinematics and dynamics using most often used methods.

* Knowledge about particular space environment requirements and testing methods. * Knowledge about the space mission operations and human interfacing requirements. * Analyze some basic problems in space robotic missions, and synthesize an adequate solution.

Education Method 14 lectures, 2 assignment

Prerequisites Basic understanding of: linear algebra, physics, analog electronics, digital & analog signal processing, mechanics (statics, kinetics, dynamics), linear control theory, Matlab, C.

Assessment Assignment

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SC4060

Predictive and adaptive control

4

Responsible Instructor Dr.ir. A.J.J. van den Boom

Contact Hours / Week x/x/x/x 0/0/3/0 Education Period 3 Start Education 3 Exam Period 3 4

Expected prior knowledge Undergraduate curriculum

Course Contents The model predictive control (MPC) strategy yields the optimization of a performance index with respect to some future control sequence, using predictions of the output signal based on a process model, coping with amplitude constraints on inputs, outputs and states. The course presents an overview of the most important predictive control strategies, the theoretical aspects as well as the practical implications, that makes model predictive control so successful in many areas of industry, such as petro-chemical industry and chemical process industry. Hands-on experience is obtained by MATLAB exercises with academic examples and a industrial simulation of MPC on a two-product (binary) distillation column. Contents of the course: General introduction. Differences in models and model-structures, advantages and limitations. Prediction models in state-space setting. Standard predictive control scheme. Relation standard form with GPC, LQPC and other predictive control schemes. Finite/Infinite horizon MPC. Solution of the standard predictive control problem. Stability, robustness, initial and advanced tuning. Robust design in predictive control. See also: http://www.dcsc.tudelft.nl/~sc4060

Study Goals Study Goals:

The student should be able to

1. Explain how and why MPC has emerged from industry. 2. List the five basic items of MPC and discuss their role.

3. Identify, recognize and describe different type of models in MPC and explain when a type of model is suited for a specific application.

4. Show that all models can be transformed into a state-space model. 5. Understand the concept of prediction in MPC.

6. Make a prediction in the noiseless and the noisy case. 7. Explain why a standard formulation is desirable.

8. Transform any MPC problem into the standard MPC problem. 9. Derive the steady-state of a system.

10. Solve the finite and infinite horizon problem.

11. Derive the realization for the LTI-case and for the inequality constrained case. 12. Describe two ways to deal with infeasibility.

13. Discuss stability for the LTI case and in the inequality constrained case. 14. Describe the use of the end-point constraint and the infinite prediction horizon. 15. Give the relation of the MPC scheme and the IMC scheme.

16. Motivate the rules-of-thumb for initial tuning and use these rules for tuning an MPC controller. 17. Describe the concept of robustness in MPC.

18. Motivate and use the rules of robust tuning in MPC.

19. Derive an MPC controller for various academic and industrial examples using MATLAB.

Education Method Lectures 0/0/3/0

Literature and Study Materials

Course notes Model Predictive Control by Ton van den Boom (TU Delft) 2008.

Assessment Written exam and a homework assignment

Remarks Computer use: for the homework assignment, the use of MATLAB on PC is required. The assignment can be done either at home or at the DCSC laboratory.

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SC4081

Knowledge Based Control Systems

4

Responsible Instructor Prof.dr. R. Babuska

Course Contents Theory and applications of knowledge-based and intelligent control systems, including fuzzy logic control and artificial neural networks:

Introduction to intelligent control Fuzzy sets and systems

Intelligent data analysis and system identification Knowledge based fuzzy control (direct and supervisory) Artificial neural networks, learning algorithms Control based on fuzzy and neural models Reinforcement learning

Examples of real-world applications

Study Goals Main objective: understand and be able to apply 'intelligent control' techniques, namely fuzzy logic and artificial neural networks to both adaptive and non-adaptive control.

After successfully completing the course, the student is able to:

* Name the limitations of traditional linear control methods and state the motivation for intelligent control. Give examples of intelligent control techniques and their applications.

* Formulate the mathematical definitions of a fuzzy set and the associated concepts and properties (alpha-cut, support, convexity, normality, etc.), basic fuzzy set-theoretic operators, fuzzy relations and relational composition.

* Explain the notion of a fuzzy system and define the Mamdani, Takagi-Sugeno and singleton fuzzy model. State and apply the compositional rule of inference and the Mamdani algorithm. Define and apply the center of gravity and the mean of maxima defuzzification method.

* Describe how fuzzy models can be constructed from data, give examples of techniques for antecedent and consequent parameter estimation. Compute consequent parameters in Takagi-Sugeno fuzzy model by using the least-squares method. * Explain the difference between model-based and model-free fuzzy control design. Give the basic steps in knowledge-based fuzzy control design. Define a low-level and a high-level (supervisory) fuzzy controller, explain the differences.

* Explain the concept of an artificial neural network and a neuro-fuzzy network, give some examples and explain the differences. Define and apply the back-propagation training algorithm. Explain the difference between first-order and second-order gradient methods.

* Show how dynamics are incorporated into fuzzy models and neural networks, give examples. Discuss how dynamic models can be identified from data.

* Give block diagrams and explain the notions of inverse-model control, predictive control, internal model control, direct and indirect adaptive control. Explain the meaning of the variables and parameters in recursive least-squares estimation. * Explain the motivation and the basic elements of reinforcement learning. Define and explain the concepts of value function, Bellman equation, value iteration, actor-critic control scheme.

* Define hard, fuzzy and possibilistic partitions, explain the fuzzy c-means algorithm and its parameters.

* Implement and apply the above concepts to a simulated nonlinear process or a given data set, using Matlab and Simulink.

Education Method Lectures and two assignments - literature assignment and practical Matlab / Simulink assignment.

Literature and Study Materials

Lecture notes: R. Babuska. Knowledge-Based Control Systems. Overhead sheets and other course material (software, demos) can be downloaded from the course Website (www.dcsc.tudelft.nl/~sc4080) and handed out at the lectures.

Assessment Written exam, closed book. Assignments are graded and constitute 40% of the final mark (the literature assignment 20% and the practical Matlab / Simulink assignment also 20%). A mini-symposium is organized in order for the students to present the results of the literature assignment.

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SC4091

Optimization in Systems and Control

4

Responsible Instructor Prof.dr.ir. B.H.K. De Schutter

Expected prior knowledge Basic knowledge about linear state space models and stability, and basic experience with Matlab

Course Contents In this course we study numerical optimization methods, mainly from a user point of view, and we discuss several applications of optimization in systems and control. First we discuss the basic characteristics and properties of various optimization methods. We also provide guidelines to determine which algorithms are most suited for a given optimization problem. Next, the previously treated optimization methods are used in a multi-criteria controller design application. We also focus on the translation of the design constraints into mathematical constraints. Another important topic is the determination of good initial conditions. For more information, see: http://www.dcsc.tudelft.nl/~sc4091

Study Goals After this course the students should be able to select the most efficient and best suited optimization algorithm for a given optimization problem. They should also be able to reformulate an engineering problem into a (mathematical) optimization problem starting from the given specifications. They should be able to reduce the complexity of the problem using simplifications and/or approximations so as to augment the efficiency of the solution approach.

Education Method Lectures

Literature and Study Materials

Lecture notes "Optimization in systems and control" by T. van den Boom and B. De Schutter, Delft, 2009 + handouts

Assessment written examination (closed book) + report on the practical assignment

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SC4110

System Identification

5

Responsible Instructor Dr.ir. X.J.A. Bombois

Instructor Prof.dr.ir. P.M.J. van den Hof Contact Hours / Week

x/x/x/x

0/0/6/0

Exam Period Exam by appointment

Course Contents Experimental modelling of dynamic systems; methodology.

Discrete-time signal- and system-analysis. Identification of transferfunctions. Representations of linear models; black-box models.

Identification of prediction-error-methods; least squares-method. Approximation modelling; algorithms. Experiment design and data-analysis. Identification in time- and frequency-domain; closed-loop identification; model validation; Matlab toolbox; laboratory assignment.

Study Goals General learning objectives

System identification deduces and subsequently validates mathematical models of real-life dynamical systems (industrial processes, mechanical servo-systems, ) based on experimental data collected from those systems. This course can be considered as a follow up of the course Sc4010 Filtering and Identification where different solutions to identify a model are presented (note nevertheless that Sc4010 is in no way a prerequisite for this course). The course Sc4110 selects two widely-used linear identification methodologies: Empirical Transfer Function Estimate (ETFE) and Prediction Error Identification (PEI) and provides the students with engineering and theoretical skills to perform the identification in a suitable way. In particular, after this course, the students are able to set up an experiment, identify a nominal model, assess the accuracy/precision of this model, and make appropriate design choices to arrive at a validated model.

Detailed learning objectives:

1)Based on time-domain input-output data collected on the true system in open loop, the student is able to deduce a frequency-domain model of a system using the ETFE identification method

2)The student is able to specify the bias and variance properties of models identified by the ETFE identification method. 3)For the ETFE identification method, the student is able to interpret the bias and variance properties of identified models, and knows how these properties can be influenced by input signal design and by applying windowing techniques.

4)The student is able to specify different linear model structures, and to characterize their computational and statistical properties in prediction error identification.

5)The student masters the statistical properties (bias, variance, consistency) of prediction error estimators both for the situation of exact plant and noise model sets, and for the situation of exact plant model sets only.

6)The student can interpret estimated models as approximations of an underlying physical systems, through the specification of well-defined approximation criteria in the frequency domain, and is able to select design variables so as to arrive at identified models that have prechosen approximative properties.

7)The student is able to specify how experiment design and signal to noise ratio affect estimated models. This includes mastering the concept of sufficiently exciting input signals, and the design of appropriate input signals.

8)The student is able to apply and interpret correlation-based model structure validation tests, and to draw conclusions on the (in)validity of model structures, distinguishing between plant models and noise models.

9)For both ETFE and PE identification methods, the student is able to appropriately acquire digital data from a real-life system (choice of sampling frequency, data processing).

Required level for the assignment

1)the student is able to explain in details the presented theory, to demonstrate important properties and to make links and comparisons between the different parts of the course

2)the student is able to use the presented tools in practice on a laboratory setup and to interpret his/her result with a critical attitude

Education Method Lectures and project 0/0/6/0

Assignment form: final project on a laboratory setup followed by an oral examination

Literature and Study Materials

lecture notes and slides

Prerequisites Basics in linear algebra and signal theory

Assessment Oral and project

Assignment form: final project on a laboratory setup followed by an oral examination

Remarks Course load: 14 theory courses, 3 exercise sessions and 2 computer sessions

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SC4120

Special Topics in Signals, Systems & Control

3

Responsible Instructor Prof.dr. C.W. Scherer

Instructor Prof.dr.ir. P.M.J. van den Hof Contact Hours / Week

x/x/x/x

0/0/0/2

Course Contents The lecture has a changing content, directed towards the current developments in signal analysis, system identification and control engineering. It either consists of contributions from different lecturers, and is sometimes organized in the form of a seminar sequence with active participation of students.

Please notice that the course is not offered every year. Check Blackboard for details.

Study Goals Acquire competence to report on a particularly chosen scientific development within signal analysis, system identification or control

Identify essentials in an advanced scientific article or book chapter about signals, systems or control Compose a summary with a balanced exposition of generic aspects, details, examples

Orally report on results of investigation, including an educated evaluation of the subject Defend presentation and evaluation in a scientific discussion with audience

Enter a scientific dispute about the particular topic of specialization of a fellow-student

Education Method Lecture 0/0/0/2

Literature and Study Materials

Lecture notes or book to be announced

Assessment Assignment

SC4150

Fuzzy Logic and Engineering Applications

3

Responsible Instructor Prof.dr.ir. J. Hellendoorn Contact Hours / Week

x/x/x/x 3/0/0/0 Education Period 1 Start Education 1 Exam Period 1 2

Required for Core curriculum

Course Contents Fuzzy logic techniques can be applied in various engineering domains, mainly in fields where reasoning under uncertainty plays an important role. This course provides background in fuzzy set theory, fuzzy logic and related soft-computing techniques with applications in control, information and data processing, artificial intelligence and decision making. See also:

http:/www.dcsc.tudelft.nl/~sc4150.

Study Goals Main objective: understand fuzzy logic, fuzzy decision making and fuzzy control, and be able to translate linguistic expressions into fuzzy sets and derive conclusions.

Understand the difference between fuzziness, probability and possibility. Understand characteristic functions, operations on fuzzy sets and fuzzy relations. Apply the Compositional Rule of Inference and the Generalized Modus Ponens. Analyze the defuzzification procedure.

Know fuzzy data bases.

Apply Mamdani and Gödel inference for fuzzy control.

Understand look-up tables for fuzzy controllers, stability and robustness. Apply sliding mode fuzzy control.

Synthesize fuzzy decision making.

Know subjectivity and single-step, single-person decision making. Apply measures, weights, and criteria-criteria dependency. Analyze decision operators.

Education Method Lectures

Literature and Study Materials

Course notes (sold online via Blackboard)

Assessment Written, open book

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WB1310

Multibody Dynamics A

3

Responsible Instructor Dr.ir. A.L. Schwab

Required for wb1413

Expected prior knowledge wb1113wb, wb1216

Course Contents Multibody Dynamics is about the analysis of the motion of complex mechanical systems as in a robot arm, a railway bogie or a gantry crane. In this course you will learn about the fundamentals of Multibody Dynamics: the description of the orientation of a rigid body in space, the Newton-Euler equations of motion for a 3D rigid body, how to add constraints to the equations of motion, and how to solve such a system of coupled equations. Next you will spend most of the time (80%) in doing the assignments with the ADAMS Software.

Study Goals The student is able to make a complex computer model of a realistic 3-D mechanical system in a standard software package for multibody system dynamics (currently MSC.ADAMS), to perform a dynamic analysis on the model, to draw some conclusions from this analysis, and to identify the limitations of the model.

More specifically, the student must be able to:

1.apply the Newton-Euler equations of motion to a single 3D rigid body

2.describe the orientation of a rigid body in 3-D space by means of Euler angles and derive expressions for the angular velocities in terms of the Euler angles and their time derivatives

3.construct a computer model of a complex mechanical system by selecting the appropriate number of rigid bodies, and number and type of constraints

4.make approximate dynamic calculations for a complex computer model in order to determine for instance the stiffness and the damping of individual components

5.make approximate dynamic calculations for a vehicle system model in order to verify for instance the eigenfrequencies and the equilibrium state in steady motion

6.explain the difference between the results from a dynamic analysis on the model and the behaviour of the real system, identify the limitations of the model

7.explain the finite accuracy of the results from a dynamic analysis due to the finite accuracy of the numerical integration together with the constraint violations

Education Method Lectures (2 hours per week), computer assignment.

Computer Use The course and the course/lab work are fully computer-oriented. The Lab assignment consists of a number of practical problems that have to be worked out with the software package ADAMS. Your findings are to be put down in a Lab Report.

Literature and Study Materials

Course material:

Lecture Notes and M.Wisse, Introduction to ADAMS, Delft, 1999. References from literature:

A.A.Shabana, ' Dynamics of multibody systems', Wiley, New York, 1998.

E.J.Haug, ' Computer aided kinematics and dynamics of mechanical systems, Volume I: Basic methods', Allyn and Bacon, Boston, 1989.

P.E.Nikravesh, ' Computer-aided analysis of mechanical systems', Prentice-Hall, Englewood Cliffs, 1988.

M. Géradin, A. Cardano, ' Flexible multibody dynamics: A finite element approach', J. Wiley, Chichester, New York, 2001.

Assessment Written exam + assignment report

Remarks The written exam is of the open book type and has the form of a questionnaire about the findings as written down in your Lab Report. This report serves as reference material for your exam. At the end of the exam the questionnaire together with the Lab Report are to be handed over, The grading is on both items.

Checkout the wb1310 home-page at http://tam.cornell.edu/~als93/ for up-to-date information.

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WB1406-07

Experimental Dynamics

3

Responsible Instructor D. de Klerk

Contact Hours / Week x/x/x/x

0/0/2/2

Education Period 3 4

Parts The course consists of two parts: - part A Classes

- part B Laboratory experiments (four in total)

Course Contents Part A: Theory

- How does a modern measurement system work?? In specific how does it minimize desturbances and does it cope with filter effects?

- Pitfalls in Frequency Analysis: Descrete algorithms, Leakage, Aliasing. Know it or you'll mess up your experements. - The power of Transfer and Frequency Response Functions (FRF); why are the so usefull?

- Experimental Modal Analysis: Does and don't, pitfalls & challenges in practice.

- Harmonic excitation (with frequency stepping), impulsive excitation, stochastic excitation. - Sensors, how do they work, what is important when using them.

- Rotoranalysis, operational system analysis. - Latest advances in measurement technology.

Moto: In theory, theory and practice are the same... In practice they are not.

This course concentrates on pointing where those differences orignate from, valuable for any who'll perform measurements, needs to analyse measurements or who tries to match his / her simulation to the experiment.

Part B: Experimental analysis

The second part of the course involves working on assigments meant to illustrate concepts described in Part A and to deepen insight.

Teams of three students each, carry out multi´ple experiments. Last year students got to simulate in Matlab a measurement system as a first assignment. Their final project involved analyzing measurement data measured by them selves on my car on the Rotterdamsestraatweg. Can it be more exciting? Yes, maybe you have always wanted to analyze a different product like a boat, train, motorbike, music instrument, etc. maybe we can come up with that exciting experiment in this year's course!

Study Goals In general the student is able to perform dynamic measurements, being aware of possible pitfalls. More specifically, the student must be able to:

1. describe the effects of Quantization, Leakage, Aliasing in measurements and measurement equipment.

2. explain the principle of extracting modal parameters (resonance frequency, spring constant, damping ratio) from system response both in the time domain and in the frequency domain

3. explain the principle of extracting modal parameters (modal frequencies, modal gains, modal damping ratios) from system response both in the time domain and in the frequency domain

4. discuss relative merits of different excitation techniques (shaker with frequency sweep, impact hammer, shaker with random excitation)

5. discuss the prinicples and the elative merits of different sensing techniques (strain gauge, seismic mass, piezo crystal, electromagnetic induction, laser vibrometer)

6. carry out dynamic experiments, analyze the data, and report and discuss his findings.

Education Method Classes followed by laboratory projects.

Computer Use Matlab Word LaTeX PowerPoint

Literature and Study Materials

Course material: - Part A: Course notes

- Part B: Laboratory assignments manual References from literature:

- see the reference list in the Course notes.

Assessment Written report, and oral discussion of experiment activities and of report.

Department 3mE Department Precision & Microsystems Engineering

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WB1416

Numerical Methods for Dynamics

3

Responsible Instructor Prof. D.J. Rixen

Instructor Prof.dr.ir. A. van Keulen Contact Hours / Week

x/x/x/x

0/0/2/2

Expected prior knowledge Statics and Strength of materials, Dynamics (e.g. wb1418, wb1419), Linear Algebra, Numerical Analysis (e.g. wi3097wb), Finite Elements (e.g. wb1212-1214)

Course Contents Using engineering tools as black boxes can be dangerous and inefficient. This is especially true when performing dynamic analysis of structures in a finite element package. Choosing the right finite element types and the suitable solution procedure is critical to get accurate results and to compute solutions efficiently. In order to discuss basic principles of numerical methods for dynamics and to explain fundamental concepts related to dynamic analysis, the course will cover the following topics: - Elastodynamic equations for a continuous media (short recap)

- Discretization techniques: Rayleigh-Ritz and Finite elements (bar, beam) - Linear solvers, storage techniques and singular systems

- Free vibration modes, mode superposition techniques and eigensolvers for large systems - Accuracy of modal superposition, modal acceleration, system excited through support - model reduction, including dynamic substructuring

- time-integration of linear and non-linear systems

- computing senstitivity of modes and eigenfrequency to design parameters, model updating - Parallel computing techniques for fast solvers

Some topics might be dropped depending on students background. Specific topics might also be discussed if time permits. In this courses emphasis will be put on understanding fundamental concepts of numerical methods and how they relate to the mechanics of structures. Therefore, the oral (open book) exam will concentrate on the mastering of concepts rather than on formulation details. A computational project will be included (using Matlab pre-cooked routines and/or Ansys-Nastran).

Study Goals The student is able to grasp the basic numerical concepts underlying the methods used to perform the analysis of models in engineering statics and dynamics. He can choose the appropriate methods in specific applications and analyse the reasons why methods can result in erroneous solutions. He is aware of computational and programming issues relative to specific numerical techniques and implementations.

1. understand the assumption underlying the discretization process and the associated limitations in terms of spatial and frequential accuracy

2.describe the solutions steps needed to solve linear systems and choose the proper algorithm according to the problem (LU, Cholesky, LDLT) including storage techniques

3.identify singular matrices arising from mechanical systems and compute a generalized inverse of a singular matrix and its nullspace

4.use the concept of eigenmodes to write the dynamic solution as a modal superposition and the system matrices in the form of spectral expansions

5.choose the proper eigensolvers and implement standard techniques from the family of the power iteration including shifting 6.evaluate the approximations inherent to modal truncation in the mode displacement method and apply the mode acceleration method to correct for the static truncated part

7.solve by mode superposition the dynamics of systems excited by their support and apply the technique of additional mass to replace imposed displacements

8.describe the concept of effective modal mass and explain how it can be used to evaluate the contribution of modes to the approximation by modal series of the response of systems excited by the support

9.describe the concept of model reduction and write the reduced equations and write the reduced dynamic equations according to the static Guyan-Iron reduction

10.outline the idea of substructuring and derive the substructure approximation in the Craig-Bampton method, derive the associated reduced matrices and describe how accurate the Craig-Bampton approximation is in practice

11.solve the normal equations using Laplace transforms and put the solution procedure of the normal equations in a recursive matrix

12.discuss the concepts of consistency, stability and accuracy for simple implicit and explicit direct time-integration schemes 13.derive the time-integration formulas belonging to the Newmark family and discuss the stability limits and the accuracy of the Newmark schemes

14.write the explicit and implicit time-integration algorithms for non-linear systems 15.write the sensitivity of eigenmodes and eigenfrequencies of dynamic systems

16.describe the basic principles of parallel computing and explain the concept of domain decomposition and write the decomposed problem in a dual and primal interface problem suitable for parallel computing

17.write a small program (in Matlab for instance) to perform a dynamic analysis according to the Finite Element method, and implement the proper numerical techniques

Education Method Lectures, computer use (16 hours)

Computer Use Use of ANSYS and/or Matlab for assignment and illustration.

Literature and Study Materials

Course material:

Lecture notes (available through blackboard) References from literature:

Mechanical Vibrations, Theory and Application to Structural Dynamics, M. Géradin and D. Rixen, Wiley, 1997. The Finite Element Method: Linear Static and Dynamic Finite Element Analysis, T.J.R. Hughes Prentice-Hall, 1987. Finite Element Procedures, K.J. Bathe, Prentice-Hall, 1996

Structural Dynamics: an introduction to computer methods, R.R. Craig, Wiley, 1981, ISBN 0-471-04499-7 Matrix Computation, G.H. Golub and C.F. Van Loan, Johns Hopkins University Press, 1996.

Assessment Oral exam

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WB1418-07

Engineering Dynamics

4

Responsible Instructor Prof. D.J. Rixen

Instructor Dr.ir. A.L. Schwab Contact Hours / Week

x/x/x/x

2/2/0/0

Required for Engineering Dynamics and Mechanicsms (wb1419, extension of wb1418), Multibody Dynamics A (wb1310), Multibody Dynamics B (wb1413), Numerical Methods in Dynamics (wb1416), Non-Linear Vibrations (wb1412).

Expected prior knowledge Statics and Strength of materials (e.g. wb1214), Dynamics (e.g. wb1311), Linear Algebra

Course Contents The dynamic behavior of structures (and systems in general) plays an essential role in engineering mechanics and in particular in the design of controllers. In this master course, we will discuss how the equations describing the dynamical behavior of a structure and of a mechatronical system can be set up. Fundamental concepts in dynamics such as equilibrium, stability, linearization and vibration modes are discussed. If time permits, also an introduction to discretization techniques to approximate continuous systems is proposed.

The course will discuss the following topics:

- Review of the virtual work principle and Lagrange equations - linearization around an equilibrium position: vibrations - elastodynamics in a solid and continuous systems

- discretization techniques (Rayleigh-Ritz and Finite Elements) - Free vibration modes and modal superposition

- Forced harmonic response of non-damped and damped structures

Study Goals The student is able to select different ways of setting up the dynamic equations of mechanical systems, to perform an analysis of the system in terms of linear stability and vibration modes and to properly use mode superposition techniques for computing transient and harmonic responses. He also understands the concept of displacement approximation techniques for discretizing continuous dynamic systems.

1. explain the relations between the principle of virtual work and the Lagrange equations for dynamics to the basic Newton laws 2. describe the concept of kinematic constraints (holonomic/non-holonomic, scleronomic/rheonomic) and choose a proper set of degrees of freedom to describe a dynamic system

3. write the Lagrange equations for a minimum set of degrees of freedom and extend it to systems with additional constraints (Lagrange multiplier method)

4. linearize the dynamic equations by considering the different contributions of the kinetic and potential energies (both for system with and without overall motion imposed by scleronomic constraints)

5. analyze the linear stability of dynamic systems (damped and undamped) according to their state space formulation if necessary 6. explain and use the concept of free vibration modes and frequencies

7. interpret and apply the orthogonality properties of modes to describe the transient and harmonic dynamic response of damped and undamped systems

8. evaluate the approximations introduced when using truncated modal series (spatial and spectral) 9. explain how mode superposition can be used to identify the eigenparamters of linear dynamic systems

Education Method Lecture

Computer Use The assignement will require using Matlab-like software.

Literature and Study Materials

Course material:

Lecture notes (available through blackboard) References from literature:

Mechanical Vibrations, Theory and Application to Structural Dynamics, M. Géradin and D. Rixen, Wiley, 1997. Applied Dynamics, with application to multibody and mechatronic systems, F.C. Moon, Wiley, 1998, isbn 0-471-13828-2. Engineering vibration, D.J. Inman, Prentice Hall, 2001, isbn 0-13-726142-X

The Finite Element Method: Linear Static and Dynamic Finite Element Analysis, T.J.R. Hughes Prentice-Hall, 1987. Structural Dynamics in Aeronautical Engineering, M.N. Bismark-Nasr, AIAA education series, 1999, isbn 1-56347-323-2

Assessment Oral exam + assignment

Remarks An assignment will be given which will make up part of the final mark. SInce the enphasis of the lectures will be on understanding concepts in dynamics more than memorizing formulas, the oral exam will be open book to evaluate your understanding of the concepts.

Department 3mE Department Precision & Microsystems Engineering

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WB1440

Eng. Optimization: Concept & Applications

3

Responsible Instructor Prof.dr.ir. A. van Keulen

Instructor Dr.ir. M. Langelaar Contact Hours / Week

x/x/x/x

0/0/2/2

Required for wb1441

Expected prior knowledge Basic knowledge of mechanical engineering and mathematics

Course Contents Formulation of optimization problems Typical characteristics of optimization problems Minimization without constraints

Constrained minimization Simple optimization algorithms Discrete design variables Approximation concepts Sensitivity analysis

Study Goals The student is able to formulate a proper optimization problem in order to solve a given design problem, and is able to select a suitable approach for solving this problem numerically. Furthermore, he is able to interpret results of completed optimization procedures.

1.formulate an optimization model for various design problems

2.identify optimization model properties such as monotonicity, (non-)convexity and (non-) linearity

3.identify optimization problem properties such as constraint dominance, constraint activity, well boundedness and convexity 4.apply Monotonicity Analysis to optimization problems using the First Monotonicity Principle

5.perform the conversion of constrained problems into unconstrained problems using penalty or barrier methods 6.compute and interpret the Karush-Kuhn-Tucker optimality conditions for constrained optimization problems 7.describe the complications associated with the use of computational models in optimization

8.illustrate the use of compact modeling and response surface techniques for dealing with computationally expensive and noisy optimization models

9.perform design sensitivity analysis using variational, discrete, semi-analytical and finite difference methods 10.identify a suitable optimization algorithm given a certain optimization problem

11.perform design optimization using the optimization routines implemented in the Matlab Optimization Toolbox

12.derive a linearized approximate problem for a given constrained optimization problem, and solve the original problem using a sequence of linear approximations

13.describe the basic concepts used in structural topology optimization

Education Method Lectures (2 hours per week), exercises

Computer Use MATLAB is used for exercises.

Literature and Study Materials

Course material: P.Y. Pa