Active Suspension System Optimal Control Strategy Considering Vehicle Ride Comfort and Road Maintenance Capability

The suspension system could transmit and filter the forces between the body and road surface, which affects vehicle ride comfort and road maintenance capability. Compared to traditional passive and semi-active suspension, Active Suspension Systems(ASS) could automatically adjust the suspension stiffness, damping force, and body height according to changes in the vehicle's load distribution, travelling speed, and braking action through the addition of a power source such as a linear motor. Although the existing advanced control methods could help to effectively improve the driving quality of vehicles equipped with ASS, the conflict between ride comfort and road maintenance capacity is still a difficult problem to be solved. Therefore, an Active Suspension System optimal control strategy considering vehicle ride comfort and road maintenance capability is proposed in this paper. Firstly, a quarter ASS model and a road model are respectively developed based on the system dynamics relationship and the stochastic sinusoidal superposition method. Then, the model predictive control theory is applied to establish the active force optimal controller of ASS, which takes into account the sprung mass acceleration, suspension working space, dynamic tyre deformation, actuator constraints, and control consumption. Thirdly, the improved sparrow algorithm combining Cauchy mutation and opposition-based learning is designed to tune the built-in parameters of the ASS active force optimal controller. Simulation results show that the control strategy proposed in this paper could help to maintain good ride comfort and road maintenance capability for vehicles fitted with ASS under various road conditions.