Output Information Based Iterative Learning Control Law Design With Experimental Verification

Abstract

This paper considers iterative learning control law design using the theory of linear re-petitive processes. This setting enables trial-to-trial error convergence and along-the-trial performance to be considered simultaneously in the design. It is also shown that this design extends naturally to include robustness to unmodeled plant dynamics. The results from experimental application of these laws to a gantry robot performing a pick and place operation are given, together with a discussion of the positioning of this approach relative to alternatives and possible further research. [DOI: 10.1115/1.4005038]

2D systems based robust iterative learning control using noncausal finite-time interval data

“…This form of output only control law without the moving window has been experimentally verified [9]. The new feature of the moving window has the dual purpose of reducing conservativeness and improving performance.…”

“…i.e., noncausal in p. This control law also requires all entries in the state vector to be available for measurement and [9] produces an alternative where the difference in the state vectors on two successive trials is replaced by the difference in the trial output vectors on the same two trials.…”

“…Repetitive processes [7] are another class of 2D systems where information propagation in p is over a finite duration and therefore a more obvious setting for analysis. The previous work on ILC laws designed using repetitive process control theory with experimental verification includes [8,9]. This setting also extends to differential dynamics and for robust control studies avoids products of matrices describing the uncertainty assumed.…”

Iterative learning control laws with full dynamics

“…Given the finite pass length, repetitive processes are a more natural setting for ILC design and such designs have also seen experimental benchmarking [6], [7], [8]. These results were based on the use of a static state feedback and pass profile feed forward controller with the extensive application of Linear Matrix Inequalities (LMIs) as a numerical to calculate the required controller parameters.…”

KYP lemma based stability and control law design for differential linear repetitive processes with applications

“…Examples include classes of iterative learning control schemes [2,3] and iterative algorithms for solving nonlinear dynamic optimal control problems based on the maximum principle [4]. Iterative learning control algorithms designed using a repetitive process setting have been experimentally tested [5,6]. Also, there has been recent work on the use of this setting for the analysis of OL-Nash games with a gas pipeline application [7].…”