Litcius/Paper detail

Semiglobal Suboptimal Output Regulation for Heterogeneous Multi-Agent Systems With Input Saturation via Adaptive Dynamic Programming

Bingjie Wang, Lei Xu, Xinlei Yi, Yao Jia, Tao Yang

2022IEEE Transactions on Neural Networks and Learning Systems23 citationsDOI

Abstract

This article considers the semiglobal cooperative suboptimal output regulation problem of heterogeneous multi-agent systems with unknown agent dynamics in the presence of input saturation. To solve the problem, we develop distributed suboptimal control strategies from two perspectives, namely, model-based and data-driven. For the model-based case, we design a suboptimal control strategy by using the low-gain technique and output regulation theory. Moreover, when the agents' dynamics are unknown, we design a data-driven algorithm to solve the problem. We show that proposed control strategies ensure each agent's output gradually follows the reference signal and achieves interference suppression while guaranteeing closed-loop stability. The theoretical results are illustrated by a numerical simulation example.

Topics & Concepts

Computer scienceControl theory (sociology)Dynamic programmingStability (learning theory)Multi-agent systemAdaptive controlControl (management)Mathematical optimizationSIGNAL (programming language)AlgorithmMathematicsArtificial intelligenceMachine learningProgramming languageAdaptive Dynamic Programming ControlAdvanced Control Systems OptimizationDistributed Control Multi-Agent Systems