Composite Learning Adaptive Tracking Control for Full-State Constrained Multiagent Systems Without Using the Feasibility Condition

• Published in: IEEE transaction on neural networks and learning systems Vol. 35; no. 2; pp. 2460 - 2472
• Main Authors: Jiang, Yunbiao, Wang, Fuyong, Liu, Zhongxin, Chen, Zengqiang
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.02.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive control; Adaptive learning; Artificial neural networks; Backstepping; Closed loops; Consensus tracking; Constraints; Control systems design; Controllers; disturbance observer (DOB); Disturbance observers; Dynamic stability; Embedding; Feasibility; Feedback control; Graph theory; Learning; Lipschitz condition; Mathematical models; Multi-agent systems; Multiagent systems; neural network (NN); Neural networks; Nonlinear dynamical systems; nonlinear multiagent systems (MASs); Nonlinear systems; Stability analysis; state constraints; System dynamics; Tracking control; Uncertainty; Vehicle dynamics
• ISSN: 2162-237X; 2162-2388; 2162-2388
• DOI: 10.1109/TNNLS.2022.3190286

This article proposes a distributed consensus tracking controller for a class of nonlinear multiagent systems under a directed graph, in which all agents are subject to time-varying asymmetric full-state constraints, internal uncertainties, and external disturbances. The feasibility condition generally required in the existing constrained control is removed by using the proposed nonlinear mapping function (NMF)-based state reconstruction technology, and the Lipschitz condition usually needed in the consensus tracking is also canceled based on the adaptive command-filtered backstepping framework. The composite learning of the neural network-based function approximator (NN-FAP) and the finite-time smooth disturbance observer (DOB) provides a novel scheme for handling internal and external uncertainties simultaneously. One advantage of this scheme is that the use of online historical data of the closed-loop system strengthens the excitation of NN's learning. Another advantage is that the DOB with NN-FAP embedding realizes that the finite-time observation for external disturbance in the case of the system dynamics is unknown. A complete controller design, sufficient stability analysis, and numerical simulation are provided.