High-Order Model-Free Adaptive Iterative Learning Control of Pneumatic Artificial Muscle With Enhanced Convergence

Pneumatic artificial muscles (PAMs) have been widely used in actuation of medical devices due to their intrinsic compliance and high power-to-weight ratio features. However, the nonlinearity and time-varying nature of PAMs make it challenging to maintain high-performance tracking control. In this article, a high-order pseudopartial derivative-based model-free adaptive iterative learning controller (HOPPD-MFAILC) is proposed to achieve fast convergence speed. The dynamics of PAM is converted into a dynamic linearization model during iterations; meanwhile, a high-order estimation algorithm is designed to estimate the pseudopartial derivative component of the linearization model by only utilizing the input and output data in previous iterations. The stability and convergence performance of the controller are verified through theoretical analysis. Simulation and experimental results on PAM demonstrate that the proposed HOPPD-MFAILC can track the desired trajectory with improved convergence and tracking performance

Repetitive process based higher-order iterative learning control law design

Iterative learning control has been developed for processes or systems that complete the same finite duration task over and over again. The mode of operation is that after each execution is complete the system resets to the starting location, the next execution is completed and so on. Each execution is known as a trial and its duration is termed the trial length. Once each trial is complete the information generated is available for use in computing the control input for next trial.This thesis uses the repetitive process setting to develop new results on the design of higher-order ILC control laws. The basic idea of higher-order ILC is to use information from a finite number of previous trials, as opposed to just the previous trial, to update the control input to be applied on next trial, with the basic objective of improving the error convergence performance. The first set of new results in this thesis develops theory that shows how this improvement can be achieved together with a measure of the improvement available over a non-higher order law.The repetitive process setting for analysis is known to require attenuation of the frequency content of the previous trial error from trial-to-trial over the complete spectrum. However, in many cases performance specifications will only be required over finite frequency ranges. Hence the possibility that the performance specifications could be too stringent. The second set of new results in this thesis develop design algorithms that allow different frequency specifications over finite frequency ranges.As in other areas, model uncertainties arise in applications. This motivates the development of a robust control theory and associated design algorithms. These constitute the third set of new results. Unlike alternatives, the repetitive process setting avoids the appearance of product terms between matrices of the nominal system dynamics statespace model and those used to describe the uncertainty set. Finally, detailed simulation results support the new designs, based on one axis of a gantry robot executing a pick and place operation to which iterative learning control is especially suited