Adaptive Decentralized Tracking Control for Nonlinear Interconnected Systems With Input Quantization and Output Constraints

This paper addresses adaptive decentralized output tracking control for a class of nonlinear interconnected systems with input quantization and out constraints. An adaptive decentralized tracking control strategy is designed based on backstepping design method and neural network approximation characteristics for the known and unknown quantization parameters respectively. By defining the new unknown constant and nonlinear smooth function, an adaptive parameter estimation term is used to offset the impact of interconnected items on system performances. Furthermore, in the case when the quantization parameters are unknown, a new inequality is introduced to transform the input signal and a new adaptive compensation term is constructed to deal with the influence of quantization. Meanwhile, the Barrier Lyapunov function is introduced to ensure outputs do not violate the constraints. Compared with existing input quantization designs, the proposed method does not require unknown nonlinear terms to satisfy Lipschitz conditions, and quantization parameters can be unknown. The proposed method ensures that all the closed-loop signals are uniformly bounded, and the tracking errors can converge to the small neighborhood of the origin, while ensuring the output do not violate the constraints. Finally, the simulation results show that the proposed method yields better tracking performance. Copyright © 2021 Acta Automatica Sinica. All rights reserved.