Prescribed Performance Adaptive Containment Control for Full-State Constrained Nonlinear Multiagent Systems: A Disturbance Observer-Based Design Strategy

This paper focuses on the prescribed performance adaptive containment control problem for a class of nonlinear nonstrict-feedback multiagent systems (MASs) with unknown disturbances and full-state constraints. First, the radial basis function neural networks (RBF NNs) technology is employed to approximate the unknown nonlinear functions in the system, and the problem of "explosion of complexity" caused by repeated derivation of virtual controls is solved by using the dynamic surface control (DSC) technology. Then, the nonlinear disturbance observers are designed to estimate the external disturbance, and the barrier Lyapunov functions (BLFs) and the prescribed performance function (PPF) are combined to achieve the control objective of prescribed performance without violating the full-state constraints. The theoretical result shows that all signals in the closed-loop system are semiglobally uniformly ultimately bounded (SGUUB), and the local neighborhood containment errors can converge to the specified boundary. Finally, two simulation examples show the effectiveness of the proposed method.