Adaptive Bipartite Tracking Control of Nonlinear Multiagent Systems With Input Quantization

This article studies the bipartite tracking control problem of distributed nonlinear multiagent systems with input quantization, external disturbances, and actuator faults. We use the radial basis function (RBF) neural networks (NNs) to model unknown nonlinearities. Due to the fact that the upper bounds of disturbances and the number of actuator faults are unknown, an intermediate control law is designed based on a backstepping strategy, where a compensation term is introduced to eliminate external disturbances and actuator faults. Meanwhile, a novel smooth function is incorporated into the real distributed controller to reduce the effect of quantization on the virtual controller. The proposed distributed controller not only realizes the bipartite tracking control but also ensures that all signals are bounded in the closed-loop systems and the outputs of all followers converge to a neighborhood of the leader output. Finally, simulation results demonstrate the effectiveness of the proposed control algorithm.

Automated summary

This article addresses the problem of bipartite tracking control in distributed nonlinear multiagent systems with input quantization, external disturbances, and actuator faults. The use of radial basis function neural networks is proposed to model unknown nonlinearities. A compensation term is introduced in the intermediate control law to eliminate the effects of disturbances and faults, and a novel smooth function is incorporated to reduce the impact of quantization on the virtual controller. The proposed distributed controller not only achieves bipartite tracking control but also ensures bounded signals in the closed-loop systems and convergence of follower outputs to the leader output.