Opposition-based differential evolution for synchronized control of multi-agent systems with uncertain nonlinear dynamics

Abstract

Multi-agent systems (MAS) have gained increasing research attention in recent years due to their wide applications in cooperative control of autonomous vehicles, sensor networks, robotics, etc. However, designing effective distributed control strategies for MAS with complex dynamics remains challenging. This paper proposes an improved differential evolution (DE) based cooperative control strategy to stabilize and synchronize MAS with uncertain nonlinear dynamics. Specifically, we develop a new mutation strategy inspired by opposition-based learning to enhance the exploration ability and adopt an adaptive parameter mechanism to balance exploration and exploitation. Moreover, a distributed sliding mode term is incorporated in the control input to counteract uncertainties and external disturbances. Rigorous stability analysis shows that the closed-loop MAS can achieve synchronization under the proposed control strategy. Extensive simulations demonstrate that compared to other state-of-the-art DE algorithms and cooperative control methods, the proposed strategy achieves lower synchronization error, control cost, higher convergence speed, and higher accuracy for MAS with different scales, dynamics, and uncertainties.