Distributed Adaptive FBC of Uncertain Nonaffine Multiagent Systems Preceded by Unknown Input Nonlinearities With Unknown Gain Sign

Abstract

An adaptive filtered backstepping control (FBC) is proposed for cooperative tracking control of uncertain nonaffine nonlinear multiagent systems preceded by input nonlinearities with unknown gain sign. The input nonlinearities can be backlash-like hysteresis and dead-zone. The gains of input nonlinearities are unknown nonlinear functions with unknown sign. To cope with unknown nonlinearities neural networks based on universal approximation theorem are utilized. The weights of neural networks are derived based on appropriate adaptive rules. Using the proposed adaptive FBC desirable steady-state and transient response can be achieved. Based on Lyapunov synthesis approach all the adaptive laws are extracted. It is proved that all the signals in the closed-loop network are semi-globally uniformly ultimately bounded and the outputs of the agents track the leader’s output asymptotically. Simulation results demonstrate the applicability and effectiveness of the proposed method.