Discrete adaptive fuzzy control for asymptotic tracking of robotic manipulators
TL;DR: A novel discrete adaptive fuzzy controller for electrically driven robot manipulators that is robust against all uncertainties associated with the robot manipulator and actuators and easy to implement since it requires only the joint position feedback.
Abstract: This paper presents a novel discrete adaptive fuzzy controller for electrically driven robot manipulators. It addresses how to overcome the nonlinearity, uncertainties, discretizing error and approximation error of the fuzzy system for asymptotic tracking control of robotic manipulators. The proposed controller is model-free in the form of discrete Mamdani fuzzy controller. The parameters of fuzzy controller are adaptively tuned using an adaptive mechanism derived by stability analysis. A robust control term is used to compensate the approximation error of the fuzzy system for asymptotic tracking of a desired trajectory. The controller is robust against all uncertainties associated with the robot manipulator and actuators. It is easy to implement since it requires only the joint position feedback. Compared with fuzzy controllers which employ all states to guarantee stability, the proposed controller is very simpler. Stability analysis and simulation results show its efficiency in the tracking control.
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TL;DR: In this article, the problem of adaptive iterative learning control (AILC) is considered for a class of multiple-input-multiple-output discrete-time nonlinear systems where the initial condition and reference trajectory could be randomly varying in the iteration domain.
Abstract: In this study, the problem of adaptive iterative learning control (AILC) is considered for a class of multiple-input-multiple-output discrete-time nonlinear systems where the initial condition and reference trajectory could be randomly varying in the iteration domain. It is assumed that the considered systems are subjected to time-iteration-varying unknown parameters. The iteration-varying parameters are generated by a known high-order internal model (HOIM) that is formulated as a polynomial between two consecutive iterations. By incorporating the HOIM into the controller design, the learning convergence of ILC is guaranteed through rigorous analysis under Lyapunov theory. The illustrative example is presented to demonstrate the effectiveness of AILC method.
23 citations
TL;DR: In this article, a screw drive in-pipe robot based on adaptive linkage mechanism is proposed, which allows the robot to move without motion interference in the straight and varied curved pipes by adjusting inclining angles of rollers self-adaptively.
Abstract: Most of the existing screw drive in-pipe robots cannot actively adjust the maximum traction capacity, which limits the adaptability to the wide range of variable environment resistance, especially in curved pipes. In order to solve this problem, a screw drive in-pipe robot based on adaptive linkage mechanism is proposed. The differential property of the adaptive linkage mechanism allows the robot to move without motion interference in the straight and varied curved pipes by adjusting inclining angles of rollers self-adaptively. The maximum traction capacity of the robot can be changed by actively adjusting the inclining angles of rollers. In order to improve the adaptability to the variable resistance, a torque control method based on the fuzzy controller is proposed. For the variable environment resistance, the proposed control method can not only ensure enough traction force, but also limit the output torque in a feasible region. In the simulations, the robot with the proposed control method is compared to the robot with fixed inclining angles of rollers. The results show that the combination of the torque control method and the proposed robot achieves the better adaptability to the variable resistance in the straight and curved pipes.
8 citations
TL;DR: The control design problem for under‐actuated manipulator systems is considered, which addresses both fuzzy and optimal characteristics, and the problem is completely solved.
7 citations
TL;DR: Simulation results illustrate that although the proposed approach is highly effective in providing a good tracking performance even in the face of external disturbance and uncertainty; however, it is more computational compared to other mentioned methods, as expected.
Abstract: Hyperchaotic systems have more complex dynamics than the low-dimensional chaotic system. Since hyperchaotic systems have many applications, controlling these systems for engineering applications is a new and attractive field. This paper investigates a design scheme for a class of nonlinear systems with uncertainties and unknown disturbances. In this regard, the idea of classical model reference control, feedback linearization, sliding mode control technique and interval type-2 fuzzy systems (IT2FLS) are combined to suggest a novel hybrid control scheme to resolve the model reference control problem and address the tracking problem for the novel uncertain hyperchaotic Lü system. The IT2FLS is used to approximate the unknown nonlinear terms in control law. The interval type-2 fuzzy adaptation law adjusts the consequent parameters of the rules based on a Lyapunov synthesis approach. It is further equipped with a novel PI type switching structure to attenuate the chattering of the switching law resulting from the fast and large bounded unknown disturbances. There are three major contributions worthy of emphasis. Firstly, the dynamics of the system need not to be known and just the relative degree should be known in advance, which is more flexible in the real implementations. Secondly, it can be applied to a wide range of chaotic or hyperchaotic systems, which is its unique feature. Finally, having applied the proposed approach, both the transient and steady state behavior are improved. Using the Lyapunov theory, the stability of the proposed controller is proved. Compared to conventional sliding mode control and type-1 fuzzy controller, simulation results illustrate that although the proposed approach is highly effective in providing a good tracking performance even in the face of external disturbance and uncertainty; however, it is more computational compared to other mentioned methods, as expected. 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
7 citations
Cites background from "Discrete adaptive fuzzy control for..."
...It is interesting to note that one of the main 118 concerns with using type-1 fuzzy controllers is the 119 existence of approximation error [37, 38]....
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TL;DR: This paper presents an ALC for MIMO uncertain feedback linearizable systems whose uncertainty is in their linear parameters, and learns the input gain parameters of the state equation as well as the internal parameters.
Abstract: Most of available results in adaptive learning controllers (ALCs) with input learning technique have considered the single-input single-output nonlinear systems. This paper presents an ALC for MIMO uncertain feedback linearizable systems whose uncertainty is in their linear parameters. Since only an output signal is available for measurement, a high gain observer is used to estimate the unmeasurable state. The estimated state is then utilized to implement the ALC. The proposed ALC learns the input gain parameters of the state equation as well as the internal parameters. In addition, the desired input is also learned using an input learning rule to track the whole command history. In the proposed ALC, the tracking errors are bounded and the mean-square tracking error is $$O(\epsilon )$$
as the task is repeated. Single-link and two-link manipulators are presented as simulation examples to confirm the feasibility and the performance of the proposed ALC.
7 citations
References
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Book•
20 Aug 19962,938 citations
Book•
01 Jan 1987
TL;DR: Discrete-time control systems, Discrete- time control systems , مرکز فناوری اطلاعات و ا�ل squares رسانی, کسورزی.
Abstract: Discrete-time control systems , Discrete-time control systems , مرکز فناوری اطلاعات و اطلاع رسانی کشاورزی
2,098 citations
TL;DR: It is shown that in both types of iterative learning algorithm a better performance is realized at every attempt of operation, provided a desired motion is given a priori and the actual motion can be measured at every operation.
Abstract: A new concept of learning control for the improvement of robot motions is proposed, which can be referred to a mathematical modelling of learning and generation of motor programmes in the central nervous system. It differs from conventional classical and modern control techniques. It stands for the repeatability of operating a given robotic system and the possibility of improving the command input on the basis of actual measurement data acquired at the previous operation. Hence adequate conditions on the repeatability and invariance of the system dynamics are assumed, but no precise description of the dynamics is required for construction of the learning algorithms. Two types of iterative learning algorithm are proposed: one uses a PD-type (proportional and differential) update of input commands and the other a PI-type (proportional and integral) update where velocity signals are regarded as outputs. It is shown that in both types a better performance is realized at every attempt of operation, provided a desired motion is given a priori and the actual motion (velocity signals) can be measured at every operation. Further, the robustness of such learning control algorithms with respect to the existence of perturbed errors of initialization of the robot, disturbances and measurement noise during operation is analysed in detail. It is shown that in PD-type learning laws such errors are neither amplified nor aggregated in later consecutive trials of operation. In the case of PI-type learning laws it is shown that such a robustness property is assured if a forgetting factor is adequately introduced into the repetitive learning law.
272 citations
Book•
01 Mar 1996
TL;DR: Bringing together the latest research in the field, Robust Tracking Control of Robot Manipulators is the first book to provide systematic methods for stabilizing unwanted flexible, uncertain, and unmodeled dynamics.
Abstract: From the Publisher:
Bringing together the latest research in the field, Robust Tracking Control of Robot Manipulators is the first book to provide systematic methods for stabilizing unwanted flexible, uncertain, and unmodeled dynamics. The authors present a thorough comparison of state-of-the-art and classical designs of all controls: linear or nonlinear (simple or complicated), conventional or robust, adaptive or learning. Focusing on control design and performance analysis, this book will help you to apply advanced controls effectively and to establish equivalent and different robustness. This book will be invaluable as a guide to researchers and practicing engineers in the field of robot control, automation, and robotic engineering. It will also serve as a useful reference for control of robot manipulators and mechanical-electrical systems.
235 citations
TL;DR: In this paper, a new design of sliding mode control based on an uncertainty and disturbance estimator (UDE) is given, which does not require the knowledge of bounds of uncertainties and disturbances and is continuous.
Abstract: A new design of sliding mode control based on an uncertainty and disturbance estimator (UDE) is given. The control proposed does not require the knowledge of bounds of uncertainties and disturbances and is continuous. Thus, two main difficulties in the design of sliding mode control are overcome. Furthermore, the method of UDE is extended to plants having significant uncertainty in the control input matrix and subjected to disturbances that nonlinearly depend on states.
107 citations