Iterative Learning Control for Nonlinear Systems: A Bounded-Error Algorithm (original) (raw)
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International Journal of Control, 2002
This paper presents a nonlinear iterative learning control (NILC) for nonlinear time-varying systems. An algorithm of a new strategy for the NILC implementation is proposed. This algorithm ensures that trajectory-tracking errors of the proposed NILC, when implemented, are bounded by a given error norm bound. A special feature of the algorithm is that the trial-time interval is finite but not fixed as it is for the other iterative learning algorithms. A sufficient condition for convergence and robustness of the bounded-error learning procedure is derived. With respect to the bounded-error and standard learning processes applied to a virtual robot, simulation results are presented in order to verify maximal tracking errors, convergence and applicability of the proposed learning control.
Simulation-based design of monotonically convergent iterative learning control for nonlinear systems
Archives of Control Sciences, 2012
This paper deals with a simulation-based design of model-based iterative learning control (ILC) for multi-input, multi-output nonlinear time-varying systems. The main problem of the implementation of the nonlinear ILC in practice is possible inadmissible transient growth of the tracking error due to a non-monotonic convergence of the learning process. A model-based nonlinear closed-loop iterative learning control for robot manipulators is synthesized and its tuning depends on only four positive gains of both controllers - the feedback one and the learning one. A simulation-based approach for tuning the learning and feedback controllers is proposed to achieve fast and monotonic convergence of the presented ILC. In the case of excessive growth of transient errors this approach is the only way for learning gains tuning by using classical engineering techniques for practical online tuning of feedback gains
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.
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.
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.
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.
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.
Iterative Learning Control Utilizing the Error Prediction Method
Journal of Intelligent & Robotic Systems, 1999
Abstract. In this paper, iterative learning control utilizing the error prediction method is proposed for a class of linear time varying systems subjected to disturbances. Prediction of the error is done by identifying the system time varying parameters. Convergence of the proposed ...
Analysis and experiments of iterative learning-control system with uncertain dynamics
The International Journal of Advanced Manufacturing Technology, 2005
An iterative error compensation approach is proposed in this article to improve the accuracies of high speed, computercontrolled machining processes. It is well known that the highspeed computer-numerically-controlled (CNC) machines are extremely useful in terms of manufacturing mass-produced parts. The proposed method uses an iterative learning technique that adopts the servo commands and cutting error experienced in previous maneuvers as references to current compensative actions. Moreover, non-repetitive disturbances and nonlinear dynamics of the cutting processes, and servo systems of the CNC machine that greatly affect the convergence of the learning-control systems were also studied in this research. State feedback and output feedback techniques were adopted in the proposed controller design. In addition to the stability analysis, a 1 degree-of-freedom servo positioning system is constructed to evaluate the performance of our proposed learning control approach. Both the simulation and experimental results verify the effectiveness of our approach.
2010
This paper deals with iterative learning control (ILC) design for nonlinear systems with repeatable and non-repeatable uncertainties and performing repetitive tasks to follow a reference model (also called desired system). This desired system does not necessarily have the same structure, nor the same parameters as the real systems (there is no dependence between the reference model system and the real system). For this purpose, two ILC schemes are considered and analysed. The first controller assures the asymptotic stability with a simple condition to verify, whereas the second assures this stability without condition to verify. The λ-norm is adopted as the topological measure in our proof of the asymptotic stability of the closed loop system over the whole finite time interval when the iteration number tends to infinity. Finally, two simulation results on nonlinear system are provided to illustrate the effectiveness of the proposed controllers.