Finite-Time Consensus Tracking Neural Network FTC of Multi-Agent Systems

• Published in: IEEE transaction on neural networks and learning systems Vol. 32; no. 2; pp. 653 - 662
• Main Authors: Dong, Guowei, Li, Hongyi, Ma, Hui, Lu, Renquan
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.02.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Actuators; Adaptive neural network (NN) fault-tolerant control (FTC); Artificial neural networks; Circuit faults; Control stability; Fault tolerance; Fault tolerant systems; Feedback control; finite time; multi-agent systems (MASs); Multiagent systems; Neural networks; Nonlinear systems; output dead zones; Tracking control; Tracking errors; Tracking problem
• ISSN: 2162-237X; 2162-2388; 2162-2388
• DOI: 10.1109/TNNLS.2020.2978898

The finite-time consensus fault-tolerant control (FTC) tracking problem is studied for the nonlinear multi-agent systems (MASs) in the nonstrict feedback form. The MASs are subject to unknown symmetric output dead zones, actuator bias and gain faults, and unknown control coefficients. According to the properties of the neural network (NN), the unstructured uncertainties problem is solved. The Nussbaum function is used to address the output dead zones and unknown control directions problems. By introducing an arbitrarily small positive number, the "singularity" problem caused by combining the finite-time control and backstepping design is solved. According to the backstepping design and Lyapunov stability theory, a finite-time adaptive NN FTC controller is obtained, which guarantees that the tracking error converges to a small neighborhood of zero in a finite time, and all signals in the closed-loop system are bounded. Finally, the effectiveness of the proposed method is illustrated via a physical example.