Adaptive Tracking Control for a Class of Uncertain Nonlinear Multi-Agent Systems With Input Quantization Based on Neural Approach

This paper mainly concentrates on the distributed tracking control problem for a class of uncertain nonlinear multi-agent systems with quantized input signal. Unlike the previous results on quantized control for multi-agent systems, the nonlinearities in this research can be completely unknown. To cope with these unknown nonlinearities, adaptive neural networks technique is employed to approximate the unknown nonlinear functions. Meanwhile, backstepping approach is utilized to handle the control design issue. Based on an important property of nonlinear decomposition for quantizer, a distributed quantized feedback tracking control scheme is successfully proposed to ensure the stability of the whole systems and the implementation of synchronous tracking. Finally, a simulation example is used to illustrate the efficacy of the proposed control scheme.