Convergence analysis for iterative learning control of impulsive linear discrete delay systems
Xianghua Jin, JinRong Wang, Dong Shen
Abstract
This paper addresses convergence of iterative learning control for impulsive linear discrete delay systems with randomly varying trial lengths when coefficient matrices of systems are permutable. With the aid of the explicit representation of solutions expressed in discrete matrix delayed exponential, we provide two sufficient conditions of convergence to guarantee that tracking errors uniformly converge to zero in the sense of expectation for the above impulsive controlled systems by designing two proper update learning laws with the modified tracking errors. Finally, two illustrative examples are given to verify the theoretical results.
Topics & Concepts
Iterative learning controlMathematicsConvergence (economics)Control theory (sociology)Linear systemRepresentation (politics)Applied mathematicsControl (management)Computer scienceMathematical analysisEconomic growthEconomicsPolitical scienceArtificial intelligencePoliticsLawIterative Learning Control SystemsAdvanced Adaptive Filtering TechniquesPiezoelectric Actuators and Control