Fuzzy Adaptive Swarm Control for the High-Order Self-organized System with Unknown Nonlinear Dynamics and Unmeasured States

In the paper, we study the swarm control problem for a high-order self-organized system with unknown nonlinear dynamics and unmeasured states. In previous work, swarm control, which can change the distance between agents when swarm moves, is discussed. However, in practical applications, the high-order states of agents are not easy to be measured, and the unknown nonlinear dynamics in the system are also not conducive to the efficient and reliable operation of the system. To solve the problem that the high-order states of agents are not available when designing the controller, a high-gain state observer is designed to estimate those unmeasurable states. And to overcome the negative impact of unknown nonlinear dynamics on system performance, the fuzzy logic system is introduced to approximate the unknown nonlinear dynamics. Based on the sliding mode control theory, we design a distributed fuzzy adaptive swarm controller. And the stability of the proposed controller is proved. Finally, the self-organized system based on mecanum wheeled omnidirectional vehicles is taken as an example for numerical simulation, and the simulation results show that the proposed fuzzy adaptive swarm control law is effective.