Neural networks-based adaptive tracking control of multi-agent systems with output-constrained and unknown hysteresis - Guo, Zhihua; Xue, Hong; Pan, Yingnan (Neurocomputing (Amsterdam), 2021)
This paper investigates the problem of adaptive tracking control for multi-agent systems subject to output constraint and unknown hysteresis. Different from most existing results that use the barrier Lyapunov function and integral barrier Lyapunov function to solve the output constraint problem, the transformation technique is introduced in this paper, which can convert the original system to a constrained or completely unconstrained system. The difficulty caused by unknown Bouc-Wen hysteresis is solved via the Nussbaum gain technique. The continuous unknown function is estimated by using the radial basis function neural networks. The tracking differentiator is utilized to overcome the difficulty of “explosion of complexity”. By using the Lyapunov stability theory, it is indicated that all signals of the closed-loop system are semi-globally uniformly ultimately bounded, and the tracking error converges to a small neighborhood of the origin. Eventually, the feasibility of the designed approach is demonstrated by some simulation results.