Robust Iterative Learning Control Design: Application to a Robot Manipulator (original) (raw)
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Robust iterative learning control design via mu-synthesis: Application to a robot manipulator
This paper deals with robust iterative learning control design for uncertain single-input-single-output linear time-invariant systems. The design procedure is based upon solving the robust performance condition using the Youla parameterization and the µ-synthesis approachto obtain a feedback controller. Thereafter, a convergent iterative learning law is obtained by using the performance weighting function involved in the robust performance condition. Experimental results, on a CRS465 robot manipulator, are provided to illustrate the effectiveness of the proposed design method.
Robust iterative learning control design via μ-synthesis
2005
This paper deals with the robust iterative learning control (ILC) design for uncertain single input-single output (SISO) linear time invariant (LTI) systems. The design procedure is based upon solving the robust performance condition using the Youla parameterization and the mu-synthesis approach to obtain a feedback controller. Thereafter, a convergent iterative rule is obtained in a straightforward manner by using the performance weighting function involved in the robust performance condition. Experimental results on the first three links of a 6-degrees of freedom (6-DOF) robot manipulator are presented to illustrate the effectiveness of the proposed design method
IEEE Transactions on Automatic Control, 2003
This note demonstrates that the design of a robust iterative learning control is straightforward for uncertain linear time-invariant systems satisfying the robust performance condition. It is shown that once a controller is designed to satisfy the well-known robust performance condition, a convergent updating rule involving the performance weighting function can be directly obtained. It is also shown that for a particular choice of this weighting function, one can achieve a perfect tracking. In the case where this choice is not allowable, a sufficient condition ensuring that the least upper bound of the L2-norm of the final tracking error is less than the least upper bound of the L2-norm of the initial tracking error is provided. This sufficient condition also guarantees that the infinity-norm of the final tracking error is less than the infinity-norm of the initial tracking error.
A discrete-time design of robust iterative learning controllers
IEEE Transactions on Systems, Man, and Cybernetics
The authors propose a simple discrete-time design of robust iterative learning controllers taking account of the transient behavior as well as the uncertainty of a plant. Using the impulse response sequence of a plant, we give a simple finite dimensional formulation of the problem. Assuming that a nominal impulse response sequence is given, it is proposed that a design based on the minimization of a quadratic performance index that can be regarded as a measure for the transient performance. Then the effect of the error in the impulse response data is analyzed. It is shown that an excessively high order controller is not robust in the sense that the error severely deteriorates the transient Performance. To obtain a robust controller with a reasonable order, we proposed a design based on a probabilistic modeling of the error in the impulse response data. The controller is obtained by minimizing an averaged quadratic performance index. Simulation examples are presented to illustrate the effectiveness of the proposed methods. 0018-9472/92$03.00 0 1992 IEEE S. Kodama and N. Suda, "Matrix theory for systems and control," Soc. Instrum. Contr. Eng., 1978 (in Japanese). M. Brady et al., Robot Motion: Planning and Control.
IEEE Control Systems Letters, 2018
Feedback-based iterative learning control (ILC) has been proposed to improve the unacceptable transient performance (either in state or in output) in the iteration-domain. This letter addresses a special performance requirement of output constraints, which are motivated from the safety requirements in robotic manipulators. A barrier-function like Lyapunov function is used to design a new state feedback (or a proportionalderivative controller) to ensure that output constraints are satisfied in the finite time-domain. This state feedback is then combined with the standard feed-forward ILC to track the desired trajectory. With the help of composite energy function, it is shown that, for robotic manipulators, the proposed control method can achieve perfect tracking performance without violating output constraints in any iteration. Simulation results, which are based on the model of recently developed rehabilitation robot EMU, are presented to illustrate the effectiveness of the proposed controller.
Adaptive robust iterative learning control for uncertain robotic systems
2002
In this paper, the uncertain model of the robotic system is decomposed into repetitive and non-repetitive parts, and the norm model of the system is taken into account. By using the Lyapunov method, an adaptive robust iterative learning control scheme is presented for the robotic system with both structured and unstructured uncertainty, and the overall stability of the system in
Robust optimal design and convergence properties analysis of iterative learning control approaches
Automatica, 2002
In Iterative Learning Control design, convergence speed along the iteration domain is one of the most important performance factors. In this paper, we aim at achieving fastest convergence speed (time-optimal) for a variety of nonlinear nonaffine Single-Input-Single-Output (SISO) plants, and focus on the family of the linear type iterative learning controllers, including first-order ILC and higher-order ILC. The control objective can be formulated as a kind of robust optimization: optimizing the worst case performance in the presence of the interval uncertainties. To quantify convergence speed, a learning performance index -Q-factor -is employed. The optimal learning gain is then obtained by solving a Min-max problem. From the robust optimal design, we also reach the following conclusion: under the same interval uncertainty and applying the same min-max design which is robust and optimal, the Q-factor of ILC sequences of lower order ILC is always less than that of higher order ILC in terms of time-weighted norm. In the sequel, the first order ILC achieves the fastest convergence speed in the iteration domain.
On the iterative learning control theory for robotic manipulators
IEEE Journal on Robotics and Automation
A "high-gain feedback" point of view is considered within the iterative learning control theory for robotic manipulators. Basic results concerning the uniform boundedness of the trajectory errors are established, and a proof of convergence of the algorithm is given.
Further Results on Adaptive Iterative Learning Control of Robot Manipulators
Based on a combination of a PD controller and a switching type two-parameter compensation force, an iterative learning controller with a projection-free adaptive algorithm is presented in this paper for repetitive control of uncertain robot manipulators. The adaptive iterative learning controller is designed without any a priori knowledge of robot parameters under certain properties on the dynamics of robot manipulators with revolute joints only. This new adaptive algorithm uses a combined time-domain and iteration-domain adaptation law allowing to guarantee the boundedness of the tracking error and the control input, in the sense of the infinity norm, as well as the convergence of the tracking error to zero, without any a priori knowledge of robot parameters. Simulation results are provided to illustrate the effectiveness of the learning controller. ᭧
IFAC Proceedings Volumes, 1997
Some aspects of the use of learning control for improved performance in robot control systems are studied. The learning control signal is used in combination with conventional feedback and feed-forward control. The e ects of disturbances, unmodeled dynamics and friction are studied theoretically and in simulations of a simpli ed model of a robot arm. Convergence and robustness aspects of the choice of lters in the updating scheme of the learning control signal are studied.