Four-wheel driving/four-wheel steering vehicle has the advantages of high-speed stability, steering response, and safety, making them perform excellently under various driving conditions. However, it also faces challenges due to complex dynamic couplings, multiple control inputs, and computational burden. To address these problems, this paper proposes a multi-agent cooperative control architecture based on Cooperative Distributed Model Predictive Control (Co-DMPC). The paper explains the coupling mechanisms between agents by establishing distributed state equations and reduces the control inputs of each controller. It defines predicted trajectories, assumed trajectories, and optimal trajectories for states and control inputs, laying the foundation for the interaction of coupled information between agents. The impact of different agents on global performance indicators is measured by solving the cost coupling optimization problem. Furthermore, the iterative solution approach and the termination conditions address the balance between solution accuracy and computational efficiency. The Lyapunov function direct method is used to prove the asymptotic stability of both local and global systems. This paper quantitatively analyzes the influence of weight coefficients on control effects and presents an adaptive weight selection method based on road adhesion coefficient. Compared with centralized control, the proposed method has excellent tracking accuracy, response speed, and stability. Furthermore, the hardware-in-loop (HIL) test also demonstrate the performance of the proposed controller.

To solve the problems of centralized control for vehicle chassis, such as a high computational load, the difficulty of calculating a global optimal solution, and low safety redundancy, this article proposes a distributed cooperative control method for autonomous vehicle chassis based on a multiagent system (MAS) and graph theory. The control inputs of a single agent are calculated using distributed model predictive control (DMPC), which reduces the controller’s computational load. Aiming at the unknown DMPC terminal control and status sequences, this article uses the local static feedback method to predict the terminal sequence by solving the algebraic Riccati equation. In addition, the DMPC cost function is designed rationally, and the theory of consistency control is introduced. The Laplacian matrix determines the weights between various agents, simultaneously converging adjacent agents’ errors to zero and preventing system oscillation. Finally, the simulation results indicate that the proposed method has superior trajectory tracking performance and stability compared to the centralized control method. If a single agent fails, the distributed control vehicle still maintains a specific capability for trajectory tracking, there is no system failure, and safety redundancy is high.

As a critical technology to improve vehicle safety and handling stability, chassis coordinated control technology faces challenges in multi-system coordination, multi-variable solutions, and multi-task allocation. This paper combines the advantages of decentralized and distributed architectures and proposes a scenario-driven extension Hybrid-DMPC algorithm with variable topology and distributed modeling. Firstly, a two-dimensional extension coordinate is constructed with the $\beta - \dot \beta $ phase plane and LTR as feature states. The correlation function is then solved to divide the vehicle states into classical domain, extension domain, and non-domain, forming a mapping relationship with the control architecture. Secondly, the distributed state space equations with state coupling and input coupling are constructed. The proposed algorithm is designed to employ decentralized, distributed, and hybrid architectures in the classical domain, non-domain, and extension domain, respectively. The correlation function determines the fusion rules for multiple control inputs in the extension domain. Thirdly, a cost-coupled weighted optimization function is designed, enabling local agents to coordinate global performance goals through iterative optimization. Finally, co-simulation and HIL test results verified that the proposed algorithm effectively coordinates multiple control objectives, achieving high-precision trajectory tracking, excellent handling stability, and anti-roll performance.

Considering the rules of Formula Student Racing regarding functional and safety requirements of an electric vehicle’s power system, a reliability control model of accelerator and brake pedal is developed in the Matlab/Simulink platform. Voltage signals of the accelerator pedal are filtered. An amplitude limit module and consistency module check the filtered voltage signals of the accelerator pedal. The voltage signal of the brake pedal is processed to achieve the accurate position of the accelerator pedal and avoid the influence of incorrect signals and mis-operation on safety. The control model need not change the structure of the pedal and increase the sensors. Simulation and experimental results verified that the model can effectively avoid influences of pedal vibration on the voltage signal, identify the faulty signals and cut power output when the accelerator and brake pedal are depressed simultaneously.

The early fatigue damage in the van-body of the semi-trailer is often caused by the unique mechanical characteristics and the dynamic impact of the loads.The traditional finite element method with static strength analysis cannot support the fatigue design of van-body;thus,the dynamics analysis should be adopted for the endurance performance.The accurate dynamics model to describe the transient impacts of all kinds of uneven road and the proper system transfer functions to calculate the load transfer effects from tire to van-body are two critical factors for transient dynamics analysis.In order to evaluate the dynamic performance,the dynamics model of the trailer with the air suspension is brought forward.Then the analysis method of the power spectral density (PSD) is set up to study the transient responses of the road dynamic impacts.The transient responses transferred from axles to van-body are calculated,such as dynamic stress,dynamic RMS acceleration,and dynamic load factors.Based on the above dynamic responses,the fatigue life of van-body is predicted with the finite element analysis (FEA) method.Applying the test parameters of the trailer with air suspension,the simulation system with Matlab/Simulink is constructed to describe the dynamic responses of the impacts of the tested PSD of the vehicle axles,and then the fatigue life is predicted with FEA method.The simulated results show that the vibration level of the van-body with air suspension is reduced and the fatigue life is improved.The real vehicle tests on different roads are carried out,and the test results validate the accuracy of the simulation system.The proposed fatigue life prediction method is effective for the virtual design of auto-body.