Multi-agent System Cooperative Control of Autonomous Vehicle Chassis Based on Scenario-driven Hybrid-DMPC with Variable Topology

Abstract The development of chassis active safety control technology has improved vehicle stability under extreme conditions. However, its cross-system and multi-functional characteristics make the controller difficult to achieve cooperative goals. In addition, the chassis system, which has high complexity, numerous subsystems, and strong coupling, will also lead to low computing efficiency and poor control effect of the controller. Therefore, this paper proposes a scenario-driven hybrid distributed model predictive control algorithm with variable control topology. This algorithm divides multiple stability regions based on the vehicle's $$\beta - \gamma$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>β</mml:mi> <mml:mo>-</mml:mo> <mml:mi>γ</mml:mi> </mml:mrow> </mml:math> phase plane, forming a mapping relationship between the control structure and the vehicle's state. A control input fusion mechanism within the transition domain is designed to mitigate the problems of system state oscillation and control input jitter caused by switching control structures. Then, a distributed state-space equation with state coupling and input coupling characteristics is constructed, and a weighted local agent cost function in quadratic programming is derived. Through cost coupling, local agents can coordinate global performance goals. Finally, through Simulink/CarSim joint simulation and hardware-in-the-loop (HIL) test, the proposed algorithm is validated to improve vehicle stability while ensuring trajectory tracking accuracy and has good applicability for multi-objective coordinated control. This paper combines the advantages of distributed MPC and decentralized MPC, achieving a balance between approximating the global optimal results and the solution’s efficiency.