Damping Force Optimal Control Strategy for Semi-Active Suspension System 2024-01-2286

Semi-active suspension system (SASS) could enhance the ride comfort of the vehicle across different operating conditions through adjusting damping characteristics. However, current SASS are often calibrated based on engineering experience when selecting parameters for its controller, which complicates the achievement of optimal performance and leads to a decline in ride comfort for the vehicle being controlled. Linear quadratic constrained optimal control is a crucial tool for enhancing the performance of semi-active suspensions. It considers various performance objectives, such as ride comfort, handling stability, and driving safety. This study presents a control strategy for determining optimal damping force in SASS to enhance driving comfort. First, we analyze the working principle of the SASS and construct a seven-degree-of-freedom model. Next, the damping force optimal control strategy is designed by comprising of the Genetic Algorithm (GA) and the Linear Quadratic Regulator (LQR). The cost function, which provides a quantitative evaluation of the controlled vehicle's driving comfort performance, is constructed by calculating the root mean square value of the body's vertical acceleration, suspension dynamic deflection, and wheel dynamic deformation. Based on this evaluation, we utilize the LQR to convert the damping force control problem of the SASS into a multi-objective optimal control problem. At the same time, the GA is used to comprehensively optimize weighted coefficients of the cost function, and successfully achieve the damping force optimal control for SASS. Finally, we constructed a joint simulation platform using CarSim and MATLAB/Simulink to evaluate and validate. The simulation results demonstrate the SASS’ optimal damping force control strategy could considerably enhance the vehicle ride comfort under various operating conditions comparing with the passive suspension.