Top PDF Control Techniques for Robot Manipulator Systems with Modeling Uncertainties

Control Techniques for Robot Manipulator Systems with Modeling Uncertainties

Control Techniques for Robot Manipulator Systems with Modeling Uncertainties

in terms of a Cartesian coordinate frame attached to the robot end-effector with re- spect to the base frame (i.e., the so-called task-space variables). Hence, a mapping (i.e., the solution of the inverse kinematics) is required to convert the desired task- space trajectory into a form that can be utilized by the joint space controller. If there are uncertainties or singularities in the mapping, then this can result in degraded per- formance or unpredictable responses by the manipulator. Several parametrizations exist to describe orientation angles in the task-space to joint-space mapping, including three-parameter representations (e.g., Euler angles, Rodrigues parameters) and the four-parameter representation given by the unit quaternion. Three-parameter repre- sentations always exhibit singular orientations (i.e., the orientation Jacobian matrix in the kinematic equation is singular for some orientations), while the unit quater- nion represents the end-effector orientation without singularities. By utilizing the singularity free unit quaternion, the emphasis of chapter 2 is to develop a tracking controller that compensates for uncertainty throughout the kinematic and dynamic models. Some previous task-space control formulations based on the unit quaternion can be found in [1], [2], [3], [4], [5], and the references therein. A quaternion-based re- solved acceleration controller was presented in [2], and quaternion-based resolved rate and resolved acceleration task-space controllers were proposed in [5]. Output feed- back task-space controllers using quaternion feedback were presented in [3] for the regulation problem and in [1] for the tracking problem. Model-based and adaptive asymptotic full-state feedback controllers and an output feedback controller based on a model-based observer were developed in [4] using the quaternion parametrization.
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PSO based neuro fuzzy sliding mode control for a robot manipulator

PSO based neuro fuzzy sliding mode control for a robot manipulator

This non-linear controller provides acceptable control performance with stability and robustness for non-linear systems (Iordanov and Surgenor, 1997; Slotine and Li, 1991; Utkin, 1992; Harashima et al., 1986). However, conven- tional SMC used in wide range has certain disadvantages. Firstly, chattering problem, this can cause high frequency oscillations in the controller output, secondly sensitivity to input disturbances and parameter uncertainties. Chattering phenomenon can cause some problems such as saturation and heat in mechanical parts of robot manipulators. To reduce or eliminate the chattering, various papers has been presented by many researchers and classify it into two methods: boundary layer saturation method and estimated uncertain method (Ertugrul and Kaynak, 1998; Curk and Jenermik, 2001; Khalil, 2002).
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Modeling and Control of a Two-link Flexible Manipulator using Fuzzy Logic and Genetic Optimization Techniques

Modeling and Control of a Two-link Flexible Manipulator using Fuzzy Logic and Genetic Optimization Techniques

objectives that require prior knowledge of the system which may not be available. System performance objectives, such as, reduced overshoot; rise time, settling time, and structural vibration are often found in conflict with one another due to the construction and mode of operation of flexible structure systems. Several design objectives and associated goals as demanded by a practical application cannot be guaranteed by the weighted sum approach. Moreover, normalization is required if different objectives have different units; a common occurrence in practical systems. In such cases, the potential of multi-objective optimization[9] techniques can be explored. Moreover, in this two link flexible manipulator case, a coupled fuzzy control strategy would have been more effective solution. Future work will focus on the development of coupled fuzzy control strategy for more realistic control of the manipulator and command shaping technique for vibration control. The development of a neuro-fuzzy approach to control systems will also be explored.
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Neural Network Control with Disturbance Observer for Uncertain Robot Manipulator

Neural Network Control with Disturbance Observer for Uncertain Robot Manipulator

Abstract: Robot manipulators are subject to different types of uncertainties which may degrade the tracking control performance or even make the system unstable. In this paper, a neural network tracking controller with disturbance observer is developed to deal with both the external disturbance and the dynamic parametric uncertainties. First, RBF neural network is introduced to learn and approximate the uncertain robot dynamic by using adaptive algorithm. Next, a nonlinear disturbance observer is designed to estimate and compensate for external disturbances and remove the effect of the disturbance. Simulation results show that the proposed control scheme has good tracking performance, which can effectively suppress the uncertain dynamics and external disturbances of the manipulator systems.
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Experimental investigation of feedforward control schemes of a flexible robot manipulator system

Experimental investigation of feedforward control schemes of a flexible robot manipulator system

Flexible-link robotic manipulators are known by its advantages over conventional rigid robotic arms; use cheaper and lighter material, lower power consumption, higher manipulation speed, and more safer to operate. Nevertheless, due to highly non-linear and complexity of the system, it is much more challenging to achieve and maintain the accurate positioning. Several problems arise as to attain the precise positioning requirement, vibration due to system flexibility, the difficulty in obtaining accurate model of the system and non minimum phase characteristics of the system. Therefore, flexible manipulators have not been favoured in production industries, because of un-attained end-point positional accuracy requirements in response to input commands. In this respect, a control mechanism that accounts for both the rigid body and flexural motions of the system are required [1]. The vibration control for flexible manipulator systems can be classified as feedforward and feedback control schemes [2]. The fundamental problem with systems that vibrate is that the motion transient excites the vibration. Feedforward control techniques are based on the fact that the vibrations exhibited by most systems can be characterized by measuring one or more frequencies that are excited by the motion transient. Using this information, it is possible to generate a modified command signal that will move the system at the maximum rate possible, without exciting vibrations. This control techniques has been successfully used to reduce residual vibrations in numerous mechanical systems, such as coordinate measuring machines, experiments on board of the space shuttle Endeavor,
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Fuzzy logic control of a robot manipulator in 3D based on visual servoing

Fuzzy logic control of a robot manipulator in 3D based on visual servoing

Abstract: Visual servoing is a useful approach for robot control. It is specially attractive when the control objective can be stated directly in image coordinates. Fuzzy control is a practical alternative for a variety of challenging control applications since it provides a convenient method for constructing nonlinear controllers via the use of heuristic information, which for instance may come from an operator who has acted as a “human-in-the-loop” controller for a process. Fuzzy control strategy offers an alternative approach for many conventional systems, which has certain advantages over the other techniques. In this work, we proposed a control algorithm for a robot manipulator, which combines fuzzy logic with 3D visual servoing. For implementation only image coordinates are required. Simulation results show the good performance of the complete system.
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Adaptive dynamic surface control of a flexible-joint robot with parametric uncertainties

Adaptive dynamic surface control of a flexible-joint robot with parametric uncertainties

heavy computing burden [4,5]. To overcome this com- plexity, the DSC is investigated by introducing a rst- order lter in the backstepping procedure [6]. Because of the great approximation capability of nonlinear func- tions, the Neural Networks (NNs) are widely utilized in the control systems to compensate the uncertainties of parameter and are combined with the DSC and backstepping techniques to design the controller [7- 14]. In [14], NN adaptive backstepping controller was proposed, and Radial Basic Function (RBF) was used to approximate the nonlinear unknowns in the backstepping design. Simulation results showed good tracking performance.
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Cartesian Parallel Manipulator Modeling, Control and Simulation

Cartesian Parallel Manipulator Modeling, Control and Simulation

In recent years, parallel kinematics mechanisms have attracted a lot of attention from the academic and industrial communities due to potential applications not only as robot manipulators but also as machine tools. Generally, the criteria used to compare the performance of traditional serial robots and parallel robots are the workspace, the ratio between the payload and the robot mass, accuracy, and dynamic behaviour. In addition to the reduced coupling effect between joints, parallel robots bring the benefits of much higher payload-robot mass ratios, superior accuracy and greater stiffness; qualities which lead to better dynamic performance. The main drawback with parallel robots is the relatively small workspace. A great deal of research on parallel robots has been carried out worldwide, and a large number of parallel mechanism systems have been built for various applications, such as remote handling, machine tools, medical robots, simulators, micro-robots, and humanoid robots. This book opens a window to exceptional research and development work on parallel mechanisms contributed by authors from around the world. Through this window the reader can get a good view of current parallel robot research and applications.
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Sliding Mode Control with RBF Neural Network for Two Link Robot Manipulator

Sliding Mode Control with RBF Neural Network for Two Link Robot Manipulator

Sliding mode control deals with the problem of model uncertainties as these uncertainties can have strong adverse effects on nonlinear control systems. A major approach to deal with the model uncertainly is adaptive control and another approaches which can be used to solve the control problems includes the sliding mode techniques. These techniques are generating greater interest nowadays. Discrepancies may occur between the actual plant and the mathematical model established for the controller design. Various factors may be responsible for this mismatch. The engineer’s role is to ensure required performance levels for the system instead of such mismatches. A set of robust control methods have been developed to eliminate any error. One such method is sliding mode control methodology(SMC). This is a specific type of variable structure control system (VSCS). SMC has been used for several systems including nonlinear system, multi-input multi- output(MIMO) systems, discrete-time models, large-scale and infinite-dimension systems, and stochastic systems. SMC is completely insensitive to parametric imprecisions and external disturbances during sliding mode. VSC uses a high-speed switching control law to accomplish two objectives. Firstly, the nonlinear plant’s state trajectory is taken onto a specified and user-chosen surface in the state space which is called the sliding or switching surface. This is called as the switching surface because a control path has a unique gain if the state trajectory of the plant is “above” the surface and a different gain if the trajectory falls “below” the surface. Secondly, it keeps the plant’s state trajectory on this surface for all consequent times. During this process, the control system’s structure changes from one to another and thus given the name variable structure control(VSC). The control is also named as the sliding mode control to accentuate the importance of the sliding mode. Sliding mode controller can stabilize the trajectory of a system. Control structures are designed so as to ensure that trajectories will always move towards a switching condition. Therefore, the ultimate trajectory will not exist completely within one control structure. Instead, the ultimate trajectory will slide along the boundaries of the control structures. The motion of the system as it slides along these boundaries is termed as a sliding mode and the geometrical locus consisting of the boundaries is called the sliding (hyper) surface. The sliding mode phases are shown in figure 2
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Novel Adaptive PID Control of Flexible Joint Robot Manipulator withBounded Disturbances

Novel Adaptive PID Control of Flexible Joint Robot Manipulator withBounded Disturbances

Since linear control methods are not suitable for strong coupled, nonlinear and time-varying flexible robot manipulator systems, many nonlinear control schemes based on conventional PID control theory have been proposed to improve the control performance. In [12], the global asymptotic stability of a class of nonlinear PD-type controllers for position and motion control of robot manipulators is analyzed, and a global regulator constrained to deliver torques within prescribed limits of the actuator's capabilities is proposed. However, it has been shown that although the PD controller is robust with respect to uncertainties on inertial parameters and the global asymptotic stability is guaranteed, uncertainties on the gravity parameters may lead to undesired steady state errors [13]. A PID control scheme can eliminate the steady-state errors, but it can only ensure local asymptotic stability. Moreover, to guarantee the stability, the gain matrices must satisfy complicated inequalities [14]. In [15], a new variable structure PID control scheme is designed for robot manipulators.
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Adaptive Neural Network Based Fuzzy Sliding Mode Control of Robot Manipulator

Adaptive Neural Network Based Fuzzy Sliding Mode Control of Robot Manipulator

advances and problems that still need to be considered for future improvements of robot success in worldwide frames. Each chapter addresses a specific area of modeling, design, and application of robots but with an eye to give an integrated view of what make a robot a unique modern system for many different uses and future potential applications. Main attention has been focused on design issues as thought challenging for improving capabilities and further possibilities of robots for new and old applications, as seen from today technologies and research programs. Thus, great attention has been addressed to control aspects that are strongly evolving also as function of the improvements in robot modeling, sensors, servo-power systems, and informatics. But even other aspects are considered as of fundamental challenge both in design and use of robots with improved performance and capabilities, like for example kinematic design, dynamics, vision integration.
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Dynamic modeling and parameter estimation of a hydraulic robot manipulator using a multi objective genetic algorithm

Dynamic modeling and parameter estimation of a hydraulic robot manipulator using a multi objective genetic algorithm

use of equation (1) would impose several limitations on the derivation of a dynamic model, including a serial link assumption, infinitely rigid links and joints, and backlash free joints (Mavroidis et al., 1998). Furthermore, for relatively high DOF, parametrisation of equation (1) becomes extremely complicated, even using symbolic software. To overcome these limitations, and to exploit existing Computer-Aided Design (CAD) models for the manipulator (see later), the present research instead relies on a numerical model to solve the equations of motion. This is achieved by modelling various components of the manipulator using numerical and experimental techniques, and subsequently integrating these into a unified simulation for the purpose of system identification, parameter estimation and the design of end- effector trajectories, as well as the wider control objectives. This process is usually referred to as Robot Calibration in the robotic literature (Calafiore & Indri, 2000).
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Self Structured Organizing Single Input CMAC Control for De icing Robot Manipulator

Self Structured Organizing Single Input CMAC Control for De icing Robot Manipulator

In general, robotic manipulators have to face various uncertainties in their dynamics, such as friction and ex- ternal disturbance. It is difficult to establish exactly mathematical model for the design of a model-based control system. In order to deal with this problem, the braches of current control theories are broad including classical control: neural networks (NNs) control [1-3], adaptive fuzzy logic control (FLCs) [4-6] or adaptive fuzzy-neural networks (FNNs) [7-9] etc. They are classi- fied as adaptive intelligent control based on conventional adaptive control techniques where fuzzy systems or neu- ral networks are utilized to approximate a nonlinear function of the dynamical systems. However, many adaptive approaches are rejected as being overly compu- tationally intensive because of the real-time parameter identification and required control design.
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NonLinear Control via Input-Output Feedback Linearization of a Robot Manipulator

NonLinear Control via Input-Output Feedback Linearization of a Robot Manipulator

In recent years, Feedback linearization has been attracted a great deal of interesting research. It's an approach designed to the nonlinear control systems, which based on the idea of transforming nonlinear dynamics into a linear form. The base idea of this technique is to algebraically transform a nonlinear dynamics system into a totally or partially linear one, so that linear control techniques can be applied. This notion can be used for both stabilization and tracking control objectives of SISO or MIMO systems, and has been successfully applied to a number of practical nonlinear control problems such as [1-4].
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Position control of robotic manipulator using SMC under uncertainties

Position control of robotic manipulator using SMC under uncertainties

The paper is organized in the following sections. The dynamics of robot manipulator is given in section 2. In section 3, control techniques such as PID, sliding mode control with signum function, with saturation function and with hyperbolic tangent function are explained. In sections 4, simulation results for position control of two link robotic manipulator using control techniques discussed above are given, followed by conclusion in section 5.

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Decentralized sliding mode control for an electrohydraulic robot manipulator

Decentralized sliding mode control for an electrohydraulic robot manipulator

Therefore, control strategies for high speed robotic system are of great interest for both industrial and academic fields, whereby various advanced and sophisticated control techniques have been proposed by numerous researchers in providing the necessary tracking trajectory of robot manipulator and at the same time guaranteeing the stability of the system. In general, these strategies can be divided into two categories; the non-model based or usually known as Artifical Intelligence approaches and model based approaches. While for the structures of these controllers can be grouped into three categories; the centralized, decentralized, and multilevel hierarchical. For the model based approaches, among the major control approaches considered in the literature for the uncertainties nonlinear systems are the Adaptive Control method [13], Lyapunov based control and Variable Structure Control [14]. While for the non-model based approaches, where the knowledge of the mathematical model is not needed, Fuzzy Logic system, Genetic Algorithm and Neural Networks controls have become important research topics [15].
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Modeling And Simulation Of 3 Degree Of Freedom Of Robot Manipulator

Modeling And Simulation Of 3 Degree Of Freedom Of Robot Manipulator

This project is the fundamental research that will be very important for future research on developing a vibration rejection simulator robotic arm. Hence the data of in this project is very helpful for the future analysis. By having the model of the robot arm, they could easily manipulate the parameters of the robot arm and hence create a design of robot arm which can be used in the further research.

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Encoderless Position Control of a Two-Link Robot Manipulator

Encoderless Position Control of a Two-Link Robot Manipulator

In these experiments, the EnRoCo controller is given a desired reference position that needs to be reached with the robot’s end-effector. Four experiments of this type are shown in Fig. 4. In each experiment, the EnRoCo controller starts from a blank state (i.e. without any prior knowledge about the robot) and performs a brief exploratory behavior produced by the generator in step 5 of Fig. 2. Since the given reference position is very far from the current end-effector position, the EnRoCo controller generates intermediate reference/target positions that lie on a straight line between the end-effector and the ultimate target position. This is done at step 2 of Fig. 2. Each intermediate target is generated to be not too far from the current end-effector position, bounded above by a constant maximum distance (in these experiments 0.04 [m] was used). The reason for this is to limit the effect of each primitive to a relatively small neighborhood around the current end-effector position, because the robot kinodynamics is only known locally around this position to EnRoCo. As the EnRoCo controller explores bigger parts of the reachable state space of the robot, this maximum distance bound can be relaxed (i.e. increased). The specific value of this bound is not critical, because if the effect of a
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End-point control of long reach manipulator systems

End-point control of long reach manipulator systems

In end-point control, the position and orientation in inertial space of the end-effector of the small manipulator, is controlled in spite of the supporting structure vibrations. The end-effector position feedback comes by either direct measurements of the position of the manipulator end- effector or by direct measurements of the position of the mounting point of the manipulator on its structure and then using the system kinematics to deduce the end-effector position. In both cases vision, laser or ultrasonic sensors have been used for the position measurements 12-17 . Clearly, the direct measurement of the six motions (three translations and three rotations) of the base of a manipulator or of its end-effector with these sensors, in many field applications such as in space, would be either not feasible or very difficult.
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Modeling and gait control of a quadruped robot

Modeling and gait control of a quadruped robot

The interaction with the oor is important for walking. The stability and speed of the walking pattern is inuenced by the oor, depending on how uneven, sticky or soft the material is. For modeling the oor, the characteristics should be dened and the contact area of the Aibo with the oor should be specied. In the RoboCup the Aibo walks on a carpet, but the characteristics of the oor are not specied and dier per event. Every team has its own use of the legs, which gives a wide variety of contact areas on the feet. Some teams use the front legs of the Aibo at on the oor to maintain stability and the rear legs to move around, others have all four feet at on the oor. Sony uses the legs upright, which is more unstable and looks more doglike. These qualities should be taken into account when designing a dynamical model for making new walking patterns.
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