Adaptive swarm control for high-order self-organized system with unknown heterogeneous nonlinear dynamics and unmeasured states

Abstract

In this paper, we propose an adaptive swarm controller for a kind of high-order self-organized system. There are always unknown heterogeneous nonlinear dynamics and unmeasured states in practical systems, which may lead to poor control effects and even system instability. To eliminate these possible problems, a radial basis function neural network approximator is designed to approximate unknown heterogeneous nonlinear dynamics, and a neural network high-gain state observer method is introduced to estimate the unmeasured states of intelligent units. Besides, a novel sliding mode switching approach law is designed to improve sliding mode control. Based on these works, an adaptive swarm controller is proposed to ensure trajectory tracking. With the designed adaptive swarm controller, the high-order self-organized system can achieve aggregation, dispersion, and formation switching in the process of swarm movement. Based on Lyapunov stability theory, we prove the stability of the proposed controller. Finally, according to numerical simulation, the effectiveness of the designed controller is proved.