Top PDF Design of a Fuzzy Logic Controller for Skid Steer Mobile Robot

Design of a Fuzzy Logic Controller for Skid Steer Mobile Robot

Design of a Fuzzy Logic Controller for Skid Steer Mobile Robot

avoidance and go to predefined position can be subdivided into simple tasks which are easier to manage. This divide-and-conquer approach has been deployed in their work and proved to be a successful approach for it makes the system modular. Their work was inspired by several researchers who were previously working on behavior- based navigation approaches such as the use of reactive behaviors or motor schema [13], the subsumption architecture [11], a distributed architecture for mobile navigation (DAMN) [14] and the coordination behavior technique used in their work inspired by Seraji et al. [15]. For the evaluation of their proposed scheme, some typical cases were simulated in which a robot is to move from a given current position to a desired goal in various unknown environment. It was successfully tested, in which the robot managed to navigate its way towards the goal while avoiding obstacles.
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THE DESIGN OF FUZZY LOGIC CONTROLLER VIA COPYING A LINEAR CONTROLLER

THE DESIGN OF FUZZY LOGIC CONTROLLER VIA COPYING A LINEAR CONTROLLER

The design of the fuzzy logic controller via copying a Linear Quadratic Regulator (LQR) is presented. To synthesize a fuzzy controller, we pursued the idea of making it match the LQR for small inputs since the LQR was so successful [9] . Then we still have the added tuning flexibility with the fuzzy controller to shape the control surface so that for larger inputs, it can perform differently from the LQR. The 25 “If-Then” rules determined heuristically based on the knowledge of the plant dynamics were stored in the MATLAB workspace from where they were transferred into the fuzzy controller model ready to be used for simulation in MATLAB/Simulink environment.
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Fuzzy Logic Controller Design for Leader-Follower Robot Navigation

Fuzzy Logic Controller Design for Leader-Follower Robot Navigation

Abstract—Mobile robots are applied everywhere in the human’s life, starting from industries to domestics. This phenomenon makes it one of the most studied subjects in electronics engineering. Navigation is always an issue for this kind of robot, to ensure it can finish its task safely. Giving it a ”brain” is one of the ways to create an autonomous navigating robot. The Fuzzy logic controller is a good choice for the ”brain” since it does not need accurate mathematical modeling of the system. Only by utilizing the inputs from sensors are enough to design an effective controller. This paper presents an FLC design for leader-follower robot. This FLC design is the improvement of FLC application in a single two differential-driven mobile robot. The relation between leader and follower robot is modeled linearly as a spring-damper system. Simulation proves the feasibility of the proposed method in several environment setting, and this paper also shows that the method can be easily extended to one leader and more than one follower’s formation. The research in this paper has introduced in the classroom as the teaching-learning media to improve students’ involvement and interest in robotics and robotics related class. This paper is also part of our campaign and encouragement for teachers and students to use low-cost and open source software since not all the universities in developing country can afford the expensive high-end software.
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Fuzzy Logic Controller Design for A Robot Grasping System with Different Membership Functions

Fuzzy Logic Controller Design for A Robot Grasping System with Different Membership Functions

To demonstrate the results, three different cases of different fuzzy sets effects are observed to identify the Fuzzy Logic Controller influence in controlling the gripper. The analysis through MATLAB Simulink and SimMechanics Toolboxes virtually identifies the best selections of fuzzy sets design to be incorporated into the grasping system when a cube shape and rectangle shape objects are lifted. The selections are based on the time consumed during operation. Analysis through SimMechanics is still unavailable which considerably makes this investigation become interesting as designer could design the system with exact parameters before realizing the design. Hence, the design will improve the performance with less expenditure.
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Intelligent Lighting System Design With Fuzzy Logic Controller

Intelligent Lighting System Design With Fuzzy Logic Controller

Manually, on/off switching and discrete level dimmers can be used to control the amount of light provided in a space, however, there is not much timing accuracy using this technique. The main objective of this study was to design an intelligent lighting system based on fuzzy logic controller that uses white LEDs to produce light of the required luminance level in a room space considering energy efficiency requirements. The study combines the benefits of daylight harvesting techniques and artificial intelligence techniques of using fuzzy logic principles in automatic control
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Design and Implementation of Fuzzy Logic Controller for Dynamic System

Design and Implementation of Fuzzy Logic Controller for Dynamic System

It is quite often the case that we have to design the control system for a process before the process has been constructed. In such a case we need a representation of the process in order to study its dynamic behavior. This representation is usually given in terms of a set of mathematical equations whose solution gives the dynamic or static behavior of the process. The process considered is the spherical tank in which the level of the liquid is desired to be maintained at a constant value. This can be achieved by controlling the input flow into the tank. The spherical tank is shown in Fig.1. Using the law of mass, Rate of accumulation of mass in the tank = Rate of mass flow in – Rate of mass flow out.
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Design of Brushless DC Motor with Fuzzy Logic Controller

Design of Brushless DC Motor with Fuzzy Logic Controller

Abstract - Actually brushless DC motor is the alternate motor for traditional motors and also comparatively brushless DC motor has improved performance in speed, torque, efficiency and electromagnetic torque. In this paper the three phase brushless DC motor model is designed with fuzzy logic controller and tested in MATLAB software. The Fuzzy logic controller is used to control the speed of the brushless DC motor. On the other hand parameters like Back EMF, current, speed and torque are evaluated for the designed models of BLDC motor.
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Fuzzy logic controller design for inverted pendulum system

Fuzzy logic controller design for inverted pendulum system

Since the 1950s, the inverted pendulum, especially the cart version, was used for teaching linear feedback control theory [8] and it also one of an example studied that had been used as [r]

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Design of Photovoltaic System using Fuzzy Logic Controller

Design of Photovoltaic System using Fuzzy Logic Controller

A fuzzy set can be adapted and tailored towards any application and that is due to the fuzzy rules that are embedded in each of the sets. A fuzzy rule is a statement developed by the user of the logic based on the knowledge of the industry experts to determine the output based on the input variables. These rules are formulated as conditional statements that utilize the “if” and “then” arguments along with some logic gates as well such as “and”, “not” and “or” [4]. The rules were built in such a way to mimic the day-to-day human reasoning that is being done naturally such as “IF room temperature is low THEN switch the air-conditioning to low.” In this conditioning statement, the “if” and “then” argument was used to figure out the output and input. In this case, the output is the air-conditioning fan level and the input is the room temperature. The final elements of the rule are the fuzzy sets that represent both the input and the output values, which in this case are both called low. A general fuzzy rule should look like Equation 1:
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Design Of Sugeno Fuzzy Logic Controller For Resistance Furnace

Design Of Sugeno Fuzzy Logic Controller For Resistance Furnace

L.A Zadeh gave a mathematical model for expressing a linguistic value with a “fuzzy set” and using a “membership function” to determine how much a piece belongs to a set. Along with the mathematical operations on the fuzzy sets extended from the classical ones, L.A. Zadeh proposed the basis of mathematical theory in 1965 for the first time [1]. It is a mathematical model that allows representation and calculation on linguistic values and process of approximate reasoning processes. In the set of approximate reasoning problems, there is an application in the field of cybernetics, the fuzzy control problem [2].
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Development and implementation of a methodology for fuzzy logic controller design

Development and implementation of a methodology for fuzzy logic controller design

Fuzzy logic conttol is a fact in modem conttol applications. Since the early days of its use the technology has attracted a ttemendous amount of attention and review. It brought to rea[r]

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Optimal Fuzzy Logic Controller Design for Robot Arm Control

Optimal Fuzzy Logic Controller Design for Robot Arm Control

In this work, genetic-fuzzy onlineforward controller is structured. Theplant parameters of are identified byforward genetic fuzzy identificationmodel and then used by the controller tocontrol this plant.The identification process iscontinuing in the normal conditions ordisturbance conditions. The workidentifier in the normal conditions tomake the tracking is more accurate aswell as for the small variations in someplant parameters. The objective function was optimized using for GA-FLC, ICA-FLC and PSO-based FLC. The simulation results showed the suitability of the proposed artificial intelligence based methods to control the robot arm joints accurately.
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Collision Free Mobile Robot navigation using Fuzzy Logic Approach

Collision Free Mobile Robot navigation using Fuzzy Logic Approach

Autonomous mobile robots’ navigation has become a very popular and interesting topic of computer science and robotics in the last decade. Many algorithms have been developed for robot motion control in an unknown (indoor/outdoor) and in various environments (static/dynamic). Fuzzy logic control techniques are an important algorithm developed for robot navigation problems. The aim of this research is to design and develop a fuzzy logic controller that enables the mobile robot to navigate to a target in an unknown environment, using WEBOTS commercial mobile robot simulation and MATLAB software. The algorithm is divided into two stages; In the first stage, the mobile robot was made to go to the goal, and in the second stage, obstacle avoidance was realized. Robot position information (x, y, Ø) was used to move the robot to the target and six sensors data were used during the obstacle avoidance phase. The used mobile robot (E_PUCK) is equipped with 12 IR sensors to measure the distance to the obstacles. The fuzzy control system is composed of six inputs grouped in doubles which are left, front and right distance sensors two outputs which are the mobile robot’s left and right wheel speeds. To check the simulation result for proposed methodology, WEBOTS simulator and MATLAB software were used. To modeling the environment in different complexity and design, this simulator was used. The experimental results have shown that the proposed architecture provides an efficient and flexible solution for autonomous mobile robots and the objective of this research has been successfully achieved. This research also indicated that WEBOT and MATLAB are suitable tools that could be used to develop and simulate mobile robot navigation system.
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Design and analysis of Intelligent Navigational

controller for Mobile Robot

Design and analysis of Intelligent Navigational controller for Mobile Robot

Choi et al. [73] solved the navigation problem in a simple way. He has described whenever a robot challenges large, non-convex or dispersed obstacles as well as to find appropriate local minimum points within this area, always difficulties appear. Accordingly, he suggested algorithm, which covers two layer hierarchical systems to solve the problem and provide the name of the layer as, lower layer for avoiding or approaching and upper layer to combine this logic. Silva et al. [74] has proposed work for navigation of mobile robot using fuzzy logic. In this paper researchers describe how a robot uses its local information to control the steering and velocity while moving inside unknown environment. The proposed method is direct and effective and uses sensory data in order to design the fuzzy logic controller. Park and Zhang [75] developed behavior based dual fuzzy approach to navigate the mobile robot in unknown environment. Eight ultrasonic sensors, a GPS sensor and two fuzzy logic controllers with separate ‘81’ rules were used to realize this navigation system. Here two fuzzy control algorithms is used one for navigation and other for avoiding obstacle and edge detection. Qian and Song [76] have presented a research article based on sonar ring and its implementation for autonomous navigation. The local trap problem describe in this paper and uses sonar sensor to obtain the environmental information.
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Modeling and control of 6 DOF industrial robot using fuzzy logic controller

Modeling and control of 6 DOF industrial robot using fuzzy logic controller

In robot simulation, system analysis needs to be done, such as the kinematics analysis where its purpose is to carry through the study of the movements of each part of the robot mechanism and its relations between itself. Kinematics analysis is essential for robotic design and control. The kinematics analysis is divided into forward and inverse analysis. Industrial robot kinematics mathematical model is using Denavit-Hartenberg (D-H) method [9]. This method was introduced by Jacques Denavit and Richard S. Hartenberg [2]. In D-H algorithm, coordinate frames are attached to the joints between two links such that one transformation is associated with the joint, and the second is associated with the link. The coordinate transformations along a serial robot consisting of n links form the kinematics equations of the robot.
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Target Tracking in Mobile Robot under Uncertain Environment using Fuzzy Logic Controller

Target Tracking in Mobile Robot under Uncertain Environment using Fuzzy Logic Controller

Artificial Intelligent (AI) as one of the computer science branches can improve the performance of mobile robots. It can handle optimization (PSO), data mining (GA), classification (SVM, NN), decision (fuzzy, expert system), and so on. In this research, it utilizes a fuzzy system in order to handle the navigation of mobile robots in an unknown environment. The trajectory of them is based on the received information from attached sensors [6]. There are a lot works use fuzzy logic as the intelligent system [7-9]. It's due to fuzzy logic does not require exact mathematical modelling but rather works on the idea of range between 'zero' and 'one' value. A fuzzy logic controller is a control design where decisions are made by applying a fuzzy interference system based on rule or knowledge containing strings of fuzzy if-then rules [10]. This heuristic knowledge will develop perception-
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Use of human gestures for controlling a mobile robot via adaptive CMAC network and fuzzy logic controller

Use of human gestures for controlling a mobile robot via adaptive CMAC network and fuzzy logic controller

few existing conventional approaches support multiple velocities for mobile robots. In contrast, the proposed work applies practical driving gestures for normal vehicles to control the mobile robot. Thus, the robot’s velocities are determined by the current gear state and accelerator state. Consequently, the users can conveniently and flexibly control the velocity and direction of the mobile robot. Third, the proposed approach adopts the CMAC network and a fuzzy controller to implement the overall velocity control, which benefits both the self-adaption and learning abilities of the CMAC, and the running efficiency of fuzzy inference systems in implementing real-time control. As a result, the proposed system is able to generate new gesture commands, if required, by sampling new human gestures. In contrast, in order to inte- grate new commands, other robot systems usually require extra design and development stage of complex mathematical modelling [3, 4, 1, 28].
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MATLAB Simulation for Mobile Robot Navigation with Hurdles in Cluttered Environment Using Minimum Rule based Fuzzy Logic Controller

MATLAB Simulation for Mobile Robot Navigation with Hurdles in Cluttered Environment Using Minimum Rule based Fuzzy Logic Controller

Our aim is to design a fuzzy controller to guide the robot safely autonomous without any collision in cluttered environment from start point to goal point. The robot will have to take action such as changing its heading (steering) angle. These actions are taken by determining or controlling the values of variable heading angle is called output variable [4]. To calculate the value of output variable it is possible to determine the change of input variables such as the front, left, and right distance of the robot from hurdles. A fuzzy logic minimum rule based real-time navigation controller in cluttered environment is described below.
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Reactive Fuzzy Logic Controller Simulation for Robot Motion Planning

Reactive Fuzzy Logic Controller Simulation for Robot Motion Planning

D. Shi et al. [1] presents Robot Navigation in Cluttered 3D environments using preference-based fuzzy behaviors. K. Tanaka [2] describes an introduction to fuzzy logic for practical applications. X. Yang et al. [3] present a layered goal-oriented planning strategy for mobile robot navigation. P.G. Zavlangas et al. [4] present industrial robot navigation and obstacle avoidance employing fuzzy logic. Also author took the support of Lab VIEW PID Controller Toolkit User Manual [5], National Instruments Corporation, Austin. P. F. Muir et al. [6] presents kinematic modeling of wheeled mobile robots. E. L. Hall et al. [7] describes motion planning using fuzzy logic controller in Robotics: A User-Friendly Introduction. Z. L. Cao et al. [8] presents dynamic omni- directional vision for mobile robots. Z .L. Cao, Y. Y. Huang, and E. L. Hall [9] presents region filling operations with random obstacle avoidance for mobile robots. S. J. Oh et al. [10] presents calibration of an omni-directional vision navigation system using an industrial robot. Kazuo Tanaka [11] presents design of model-based fuzzy controller using Lyapunov‟s stability approach and its application to trajectory. C. V. Altrock et al. [12] presents advanced fuzzy logic control technologies in automotive applications. B. M. Bhairat et al. [13] describes implementation of crisp logic for robot control. B. M. Bhairat et al. [14] presents mathematical model for trajectory control using fuzzy logic. B. M. Bhairat et al. [15] presents steering mobile robot using fuzzy logic approach.
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Dead reckoning of a skid steer mobile robot using fuzzy

Dead reckoning of a skid steer mobile robot using fuzzy

Dead reckoning is the most widely used technique for estimating the position of a mobile robot, taking into account prior position and amount of distance travelled. Using geometric equations [13], it is straight forward to compute the momentary position of the vehicle to a known starting position. In [14], encoders are usually attached directly to motors or wheels but this strategy proved unreliable to be used in skid-steer configurations. When wheels are over accelerated, encoders lose current information that can cause inaccurate readings for actual position. For this reason, in [7], their idea to use the design of a basic encoder trailer was introduced. However, there is no guarantee of achieving good accuracy even if it has been calibrated and the idea itself is not practical to be implemented.
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