Finite-Time Prescribed Performance Time-Varying Formation Control for Second-Order Multi-Agent Systems with Non-Strict Feedback Based on a Neural Network Observer

• Published in: IEEE/CAA journal of automatica sinica Vol. 11; no. 4; pp. 1039 - 1050
• Main Authors: Ma, Chi, Dong, Dianbiao
• Format: Journal Article
• Language: English
• Published: Piscataway Chinese Association of Automation (CAA) 01.04.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, China
• Subjects: Adaptive control; Algorithms; Artificial neural networks; Closed loops; Feedback control; Finite-time control; Formation control; Multi-agent systems; Multiagent systems; neural network; Neural networks; Nonlinear systems; Nonlinearity; Observers; Output feedback; prescribed performance control; State observers; time-varying formation control; Time-varying systems; Finite-time control; multi-agent systems; neural net-work; prescribed performance control; time-varying formation con-trol
• ISSN: 2329-9266; 2329-9274
• DOI: 10.1109/JAS.2023.123615

This paper studies the problem of time-varying formation control with finite-time prescribed performance for non-strict feedback second-order multi-agent systems with unmeasured states and unknown nonlinearities. To eliminate nonlinearities, neural networks are applied to approximate the inherent dynamics of the system. In addition, due to the limitations of the actual working conditions, each follower agent can only obtain the locally measurable partial state information of the leader agent. To address this problem, a neural network state observer based on the leader state information is designed. Then, a finite-time prescribed performance adaptive output feedback control strategy is proposed by restricting the sliding mode surface to a prescribed region, which ensures that the closed-loop system has practical finite-time stability and that formation errors of the multi-agent systems converge to the prescribed performance bound in finite time. Finally, a numerical simulation is provided to demonstrate the practicality and effectiveness of the developed algorithm.