Iterative learning control for continuous-time multi-agent differential inclusion systems with full learnability

Abstract

The learnability of systems plays a crucial role in the feasibility of control objective. This study explores the learnability and iterative learning-based consensus control for continuous-time multi-agent differential inclusion systems. Unlike discrete-time systems, analysis for the relationship between output space and realizable output space that used to explore learnability is more tough since involving continuous-time system output. Using measure theory, the systems exhibit full learnability if and only if the input–output coupling matrix is of full-row rank. Considering the possibility of controller power dissipation and demand for improving tracking performance, iterative learning control with state feedback and an efficiency factor is proposed. The consensus performance for the proposed control scheme is strictly explored. Simulation on unmanned vehicles in freeway demonstrates the validity of the theoretical results.