# Hybrid Model Predictive Control of Semiactive Suspension in Electric Vehicle with Hub-Motor
## 1 Introduction
[16] proposed a multiobjective optimization control method for active suspension, using a particle swarm algorithm to generate the optimal parameters of the active suspension and the simulation results show that the optimized active suspension can effectively reduce the unbalanced electromagnetic force while simultaneously turning the motor eccentricity so it is kept within a reasonable range. [PERSON] et al. [17] proposed an \(H_{2}\) active suspension control scheme with good robustness, and the simulation results show that the suspension system has a satisfactory control effect under complex electromagnetic force excitation and dynamic perturbation. [PERSON] et al. [18] proposed a hybrid controller consisting of an H\({}_{\infty}\) suspension controller and a switched reluctance motor controller, which consists of current chopping control and pulse width modulation control, and simulation results show that the proposed control method can effectively reduce the unbalanced electromagnetic force and air gap eccentricity. [PERSON] et al. [19] proposed a two-stage optimization method based on the mathematical model of a quarter vehicle with active suspension equipped with a dynamic vibration absorber. Firstly, an LQR controller was designed to suppress the vibration of the wheel motor. Then, a controller with finite frequency H\({}_{\infty}\) was designed to improve the ride comfort of the vehicle. Although the active suspension exhibits an excellent performance, its structure is complex and expensive, limiting the promotion of its application. The emergence and mature development of controllable dampers has led to a greater focus on semiactive suspension. [PERSON] et al. [20] proposed a hybrid controller based on the hybrid acceleration drive damping algorithm and used the multiobjective optimization method to determine the parameters of the controller, and finally the simulation was carried out under the random road excitation and bump excitation simulation; the results show that the proposed control method can effectively reduce the vibration of the sprung mass and motor. [PERSON] et al. [21] used four different semiactive suspension controllers in an HM-EV and evaluated and compared the whole vehicle to show that it is feasible to use four different semiactive suspension controllers for the vehicles. From the available research, it can be seen that there are few studies on semiactive suspension in HM-EVs. This research proposes a damping control method for the HM-EV semiactive suspension to improve the ride comfort, handling stability and reduce the eccentricity between the stator and rotor. The primary contributions of this study are as follows: (1) developed a hub motor direct drive-air suspension cooperative system; (2) designed a hybrid model predictive controller to suppress the vibration of the entire system; (3) designed a Kalman filter to provide state variables that cannot be measured for the hybrid model predictive controller. The rest of this article is organized as follows: In the second section, the establishment of the 1/4 cooperative system model is introduced. Then, the control problem is proposed in the third section. To address this problem, a hybrid model predictive controller and a Kalman filter are designed. In the fifth section, a numerical simulation is performed to verify the effectiveness of the proposed method and summarize the conclusions.