Key Takeaways & Executive Findings
- •• Proposes a scenario-driven hybrid distributed model predictive control (DMPC) with variable topology for autonomous vehicle chassis, enhancing stability and trajectory tracking. • Introduces a control input fusion mechanism in the transition domain to reduce oscillation and jitter during control structure switching. • Validates the algorithm via Simulink/CarSim joint simulation and hardware-in-the-loop (HIL) tests, demonstrating improved multi-objective coordination. • Balances global optimality and computational efficiency by combining distributed and decentralized MPC approaches.
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 β −γ 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.
1. Introduction
With the continuous development of electronic and x-by-wire technology, active safety functions in vehicle chassis have gradually become indispensable. These functions use advanced electronic control systems to monitor the vehicle states in real-time and intervene automatically when necessary [1], helping drivers control the vehicle better and prevent accidents. Chassis active safety functions include active front steering (AFS) [2–4], direct yaw moment control (DYC) [5, 6], four-wheel steering (4WS) [7–9], and active suspension systems (ASS) [10–12].
AFS is a vehicle steering assistance system that automatically adjusts steering response based on vehicle speed and other driving conditions. At low speeds, it provides quicker steering responses for easier maneuvering, while at high speeds, it slows down the steering response to prevent oversteering and maintain vehicle stability. DYC generates the yaw moment of the car around the center of mass by distributing the longitudinal force of the tire to adjust the vehicle's yaw motion. This adjustment helps suppress tendencies of oversteering or understeering, enhancing the vehicle's stability and control. 4WS allows the front and rear wheels to steer to improve the vehicle's handling and stability. The rear wheels can steer in the same or opposite direction to the front wheels based on signals such as the front steering angle and vehicle speed. ASS can dynamically adjust the stiffness and damping characteristics of the suspension based on vehicle states and road conditions. This adaptation enhances the vehicle's handling performance and ride comfort under various driving conditions.
The above-mentioned active safety functions acting alone on the vehicle can produce better control effects on local targets. However, performance targets and control inputs often conflict among multiple functions and actuators. For instance, using longitudinal tire force by the DYC system can reduce the available lateral force, while the 4WS system can influence the DYC's control over the vehicle's yaw dynamics [13]. Therefore, multi-objective coordination across different systems is essential to chassis control. Model Predictive Control (MPC) algorithm has been widely applied in the field of chassis cooperative control due to its characteristics of rolling optimization, feedback correction, and optimal prediction [14, 15]. Jing et al. proposed an integrated control strategy based on MPC for stability and economy. This strategy uses model predictive control
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Yuxing Li, Yingfeng Cai, Yubo Lian, Xiaoqiang Sun, Long Chen (2025). Multi-agent System Cooperative Control of Autonomous Vehicle Chassis Based on Scenario-driven Hybrid-DMPC with Variable Topology. Chinese Journal of Mechanical Engineering. https://doi.org/10.1186/s10033-025-01191-0
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Frequently Asked Questions
What is the main contribution of this paper?
The paper proposes a scenario-driven hybrid distributed model predictive control (DMPC) algorithm with variable control topology for autonomous vehicle chassis, which improves vehicle stability and trajectory tracking while balancing global optimality and computational efficiency.
How does the proposed algorithm handle control structure switching?
It designs a control input fusion mechanism within the transition domain to mitigate system state oscillation and control input jitter caused by switching control structures.
What are the key components of the chassis active safety functions mentioned?
The key components include active front steering (AFS), direct yaw moment control (DYC), four-wheel steering (4WS), and active suspension systems (ASS).
How was the proposed algorithm validated?
The algorithm was validated through Simulink/CarSim joint simulation and hardware-in-the-loop (HIL) tests, demonstrating improved vehicle stability and trajectory tracking accuracy.
What is the significance of the hybrid-DMPC approach?
The hybrid-DMPC combines the advantages of distributed MPC and decentralized MPC, achieving a balance between approximating global optimal results and solution efficiency.
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