Adaptive NN-Based Consensus for a Class of Nonlinear Multiagent Systems With Actuator Faults and Faulty Networks

• Published in: IEEE transaction on neural networks and learning systems Vol. 33; no. 8; pp. 3474 - 3486
• Main Authors: Jin, Xiaozheng, Lu, Shaoyu, Yu, Jiguo
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.08.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Actuators; Adaptive control; Adaptive neural networks (NNs); adaptive observers; Artificial neural networks; Dynamical systems; Fault tolerance; Fault tolerant systems; fault-tolerant consensus control; Faults; faulty networks; Multi-agent systems; Multiagent systems; Neural networks; Nonlinear dynamical systems; Nonlinear dynamics; nonlinear multiagent systems; Nonlinear systems; Observers
• ISSN: 2162-237X; 2162-2388; 2162-2388
• DOI: 10.1109/TNNLS.2021.3053112

This article addresses the problem of fault-tolerant consensus control of a general nonlinear multiagent system subject to actuator faults and disturbed and faulty networks. By using neural network (NN) and adaptive control techniques, estimations of unknown state-dependent boundaries of nonlinear dynamics and actuator faults, which can reflect the worst impacts on the system, are first developed. A novel NN-based adaptive observer is designed for the observation of faulty transformation signals in networks. On the basis of the NN-based observer and adaptive control strategies, fault-tolerant consensus control schemes are designed to guarantee the bounded consensus of the closed-loop multiagent system with disturbed and faulty networks and actuator faults. The validity of the proposed adaptively distributed consensus control schemes is demonstrated by a multiagent system composed of five nonlinear forced pendulums.