Development of data-based model predictive control for continuous damping and air spring suspension system

This paper presents the development of a data-based model predictive control method for a semi-active suspension system with air springs and continuous damping. A continuous damping controller (CDC) has been devised for the system to alter its damping coefficient in real-time, capable of reducing the impact of external road disturbances. In this research, the damping force has been split up into a nominal force and a controllable additional force, allowing the system to be modelled as a linear time-invariant system, despite the inherent nonlinearity of air spring suspension systems. In consideration of such constraints given by the damper, a model predictive controller (MPC) has been devised with the goal of improving ride comfort. Additionally, Gaussian process regression (GPR) has been used to compensate for output estimation errors arising from model parameter uncertainties. The semi-active suspension system also featured a multichamber air spring with three available modes of stiffness. Hence, a stiffness controller has been designed to select an appropriate mode based on the predicted vehicle states given by the MPC using road preview information and a reduced full-car model. The proposed algorithm has been verified using computer simulations. The results showed that compensations for model errors made with GPR significantly improves ride comfort even in the presence of parameter uncertainties. Additionally, both the damping controller and stiffness mode selector were successfully implemented into an actual test vehicle. Vehicle test results showed the proposed algorithm to be robust and effective in enhancing ride comfort and reducing vehicle pitch motion.

Automated summary

This paper presents a data-based model predictive control method for a semi-active suspension system. The method utilizes a continuous damping controller and a stiffness controller to improve ride comfort and reduce vehicle pitch motion. Gaussian process regression is also used to compensate for model parameter uncertainties. The algorithm has been verified through computer simulations and vehicle tests, demonstrating its effectiveness and robustness.