Iterative learning control — An optimization paradigm (original) (raw)
Related papers
Iterative Learning Control — Monotonicity and Optimization
International Journal of Applied Mathematics and Computer Science, 2008
The area if Iterative Learning Control (ILC) has great potential for applications to systems with a naturally repetitive action where the transfer of data from repetition (trial or iteration) can lead to substantial improvements in tracking performance. There are several serious issues arising from the "2D" structure of ILC and a number of new problems requiring new ways of thinking and design. This paper introduces some of these issues from the point of view of the research group at Sheffield University and concentrates on linear systems and the potential for the use of optimization methods and switching strategies to achieve effective control.
Using iterative learning control to get better performance of robot control systems
Many manipulators at work in factories today repeat their motions over and over in cycles and if there are errors in following the trajectory these errors will also be repeated cycle after cycle. The basic idea behind iterative learning control (ILC) is that the controller should learn from previous cycles and perform better every cycle. Iterative learning control is used in combination with conventional feedback and feedforward control, and it is shown that learning control signal can handle the e ects of unmodeled dynamics and friction. Convergence and disturbance e ects as well as the choice of lters in the updating scheme are also addressed.
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.
Control Engineering Practice, 2010
This paper considers iterative learning control law design for both trial-to-trial error convergence and along the trial performance. It is shown how a class of control laws can be designed using the theory of linear repetitive processes for this problem where the computations are in terms of linear matrix inequalities (LMIs). It is also shown how this setting extends to allow the design of robust control laws in the presence of uncertainty in the dynamics produced along the trials. Results from the experimental application of these laws on a gantry robot performing a pick and place operation are also given.
Robust Iterative Learning Control Design: Application to a Robot Manipulator
IEEE-ASME Transactions on Mechatronics, 2008
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.
Advanced Iterative Learning Controllers for Robotic Systems
2005
In this paper it is proposed an extended memory iterative learning technique. The knowledge of the iterative learning controller can be built by using the previous tasks of the iterative learning controller in tracking various desired trajectories in terms of a database of input and output data. For a new desired trajectory, iterative learning controller can predict the initial control input from this database and the tracking error converges to an acceptable level in less number of iterations.
Generalization of iterative learning control for multiple desired trajectories in robotic systems
2002
Iterative learning controllers are found to be effective for trajectory tracking tasks in the robotic systems especially when the system model is not known. One of the drawback of iterative learning control is its slow convergence and high tracking errors in the initial iterations because of zero knowledge about the system for each new desired trajectory. In this paper, importance of the initial control input in the convergence of error is highlighted. Experience of iterative learning controller for different desired trajectories is modelled using neural network. For a new desired trajectory, this neural network generates the initial control input which is used by the learning controller. This approach is proved to be very effective in improving the convergence of the tracking error. The proposed method is very general and applicable to most of the iterative learning controller without modifying their simple learning structures.
Experimental comparison of some classical iterative learning control algorithms
IEEE Transactions on Robotics and Automation, 2002
This paper gives an overview of classical Iterative Learning Control algorithms. The presented algorithms are also evaluated on a commercial industrial robot from ABB. The presentation covers implicit to explicit model based algorithms. The result from the evaluation of the algorithms is that performance can be achieved by having more system knowledge.