26 research outputs found
Strong Practical Stability and Stabilization of Differential Linear Repetitive Processes
No full textInternational audienc
A Simplified Approach to Iterative Learning Control Based on Strong Practical Stability of Repetitive Processes
No full textStability and Stabilization of a Class of Ill-conditioned Second Order Differential Linear Repetitive
No full textLMI based Stability and Stabilization of Second-order Linear Repetitive Processes
No full textThis paper develops new results on the stability and control of a class of linear repetitive processes described by a second-order matrix discrete or differential equation. These are developed by transformation of the secondorder dynamics to those of an equivalent first-order descriptor state-space model, thus avoiding the need to invert a possibly ill-conditioned leading coefficient matrix in the original model
Stability and robustness of discrete linear repetitive processes in the finite frequency domain using the KYP lemma
No full textExperimentally verified iterative learning control based on repetitive process stability theory
No full textThis paper develops a new algorithm for the design of phase-lead Iterative Learning Control (ILC) schemes in a 2D systems setting. This algorithm enables control law design for error convergence and performance and are actuated by system output. Results from the experimental application of ILC designed by this new algorithm to a gantry robot are also given
Iterative Learning Control Based on Strong Practical Stability of Repetitive Processes
No full textThis paper develops significant new results on the design of Iterative Learning Control (ILC) schemes based on treating the problem within the framework of the stability/ control theory for linear repetitive processes. These processes propagate in two independent directions and arise in the modeling of a number of physical processes. The duration of information propagation in one of these two directions is finite, and this is a key link to ILC which has been developed as a technique for controlling systems which are required to repeat the same operation over a finite duration known as the trial length. Each execution of the operation is known as a trial and when it finishes the process resets and the next trial begins. The novel idea in ILC is to use information from previous trials to compute the input to the current one and thereby sequentially improve performance. Previous work has shown that linear model ILC can be described by certain repetitive process models and in this paper the starting point is so-called strong practical stability for these processes. In particular, it is shown how this stability property can be used to design ILC laws in the case when there are performance specifications that require control of the transient dynamics produced along the trials, in addition to trial-to-trial error convergence
Control of discrete linear repetitive processes using strong practical stability and H? disturbance attenuation
No full textRepetitive processes are a distinct class of 2D systems of both theoretical and practical interest. This paper develops algorithms for control law design to ensure stabilization and a prescribed level of disturbance attenuation as measured by an H? norm
