Ride Comfort Improvement Through Preview Active Suspension Control with Speed Planning

The development of vehicle intelligence and connectivity provides the opportunity to integrate more road information for vehicle control. This article proposes a preview active suspension control method based on model predictive control (MPC) with the aim of suppressing vertical vibration. A key challenge in implementing MPC lies in the heavy computational burden associated with solving Quadratic Programming (QP) problems. To address this, a QP algorithm based on non-negative least squares (NNLS) is utilized to accelerate the controller solving speed, thereby enhancing its practical feasibility. Additionally, to further improve ride comfort, a speed planning method is developed. This method addresses a multi-objective optimization problem (MOP) incorporating both travel time and comfort, then a feasible solution is chosen from the optimal solution set according to passenger preferences. Various experiment and simulation results are provided to validates the effectiveness of the controller, speed planning and the integrated design of both. Compared with traveling at a constant speed, the proposed speed planning method decreases the RMS value of body vertical acceleration by 17.3% while concurrently achieving shorter travel time. Note to Practitioners - Ride comfort is an essential performance criterion of autonomous vehicles. This paper proposes an integrated design of active suspension control and speed planning to improve ride comfort. Active suspension can effectively improve ride comfort through suppressing vertical vibration. However, the effect of the active suspension control is limited. As a result, speed planning is implemented to further improve ride comfort. In suspension control, an MPC preview method with adaptive prediction horizon is illustrated to cope with time-varying vehicle speed. Meanwhile, a fast QP algorithm is implemented to enhance the practicality of controller. In speed planning, a MOP considering both ride comfort and travel time is solved by a genetic algorithm. A Pareto optimal solution set is calculated by the genetic algorithm, taking advantage of this, a generalization operation enables the weighting coefficients to directly reflect the performance of their corresponding objectives.