Work

Composite inverse model ILC for discrete time linear systems: zero error in finite steps and uncertain initial conditions

Year: 2025

Type: article

Abstract: This paper develops iterative learning control strategies for discrete-time linear systems. An explicit state-space formula for the inverse-model-based ILC algorithm is derived under general conditions, which simplifies further when matrices B and C are invertible, providing a foundation for MIMO non-square systems. Building upon this formulation, a class of composite inverse-model algorithms is proposed, ensuring zero tracking error after a finite number of iterations while maintaining approximately minimal transient errors, and serving as a flexible framework to integrate optimization or greedy strategies. Robustness analysis is conducted for the multi-iteration inverse-model algorithms under model uncertainties, demonstrating uniform convergence properties. Furthermore, the framework is extended to systems with uncertain initial conditions, achieving zero-error convergence without requiring the initial state to converge to a fixed value, overcoming limitations in existing literature. Numerical simulations validate the effectiveness and practicality of the proposed methods.

Source: Systems Science & Control Engineering

DOI: 10.1080/21642583.2025.2567881

Authors: Jian Tan, Senping Tian, Jiaqi Xin

Institutions: Guangdong Mechanical & Electrical Polytechnic, South China University of Technology

Language: English

Topic: Iterative Learning Control Systems

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,165

Funder: National Natural Science Foundation of China