Decentralized prescribed performance adaptive output feedback control for nonlinear systems with unknown dead-zone

This paper is concerned with the problem of the centralized and decentralized adaptive output feedback control for a class of uncertain interconnected nonlinear systems with unknown dead-zone and prescribed performance. Radial basis function neural networks are used to approximate the unknown nonlinear functions. Decentralized observers are constructed to estimate the unmeasured states of each subsystems based on MT-filters. Adaptive prescribed performance dynamic surface control is developed by introducing a performance function and an output error transformation. By theoretical analysis, all the signals in the closed-loop system are shown to be semi-globally uniformly ultimately bounded. Simulation results illustrate the effectiveness of the proposed approach.