Regionless Explicit Model Predictive Control of Active Suspension Systems With Preview

Abstract

Latest advances in road profile sensors make the implementation of pre-emptive suspension control a viable option for production vehicles. From the control side, model predictive control (MPC) in combination with preview is a powerful solution for this application. However, the significant computational load associated with conventional implicit model predictive controllers (i-MPCs) is one of the limiting factors to the widespread industrial adoption of MPC. As an alternative, this paper proposes an explicit model predictive controller (e-MPC) for an active suspension system with preview. The MPC optimization is run offline, and the online controller is reduced to a function evaluation. To overcome the increased memory requirements, the controller uses the recently developed regionless e-MPC approach. The controller was assessed through simulations and experiments on a sport utility vehicle demonstrator with controllable hydraulic suspension actuators. For frequencies <4 Hz, the experimental results with the regionless e-MPC without preview show a ~10% reduction of the root mean square value of the vertical acceleration of the sprung mass with respect to the same vehicle with a skyhook controller. In the same frequency range, the addition of preview improves the heave and pitch acceleration performance by a further 8% to 21%.

Road Profile Estimation Using a 3D Sensor and Intelligent Vehicle

“…Model predictive control was chosen as the actuator-control method because it not only considers actuator limitations in the process of vehicle optimization, but can also effectively apply terrain information to improve control effect. Several model predictive controllers for vehicle active suspension systems have been proposed [21,28], but they did not give a detailed description of estimating elevation map. In this paper, we proposed the method for road profile estimation and model predictive control based on reference [27].…”

“…Result showed that the terrain profile information could be well estimated, and ride comfort and handling stability were also improved with the proposed method. Theunissen et al [28] proposed an explicit model predictive control method that used the road information obtained by LiDAR as the signal input to control the active suspension system. Experiment results showed that the heave and pitch acceleration of the sprung mass was reduced compared with the passive suspension system and skyhook controllers.…”

Gain‐scheduling control of dynamic lateral lane change for automated and connected vehicles based on the multipoint preview

“…To achieve the good effect of lateral lane change for ACVs, the dynamic planned trajectory must be accurately tracked using low control consumption and maintaining stable vehicle states. In [24, 25], the reference trajectory is fed to the controller through the multipoint preview operation. The control strategy is then designed by combining trajectory feed‐forward with states feedback to obtain good effects of the reference trajectory tracking.…”

“…As shown in Fig. 6, the references of longitudinal and lateral displacements can be obtained by the multi‐preview [24, 25, 34].…”

“…Most control strategies in the above studies are designed to track the instantaneous trajectory reference of vehicle lateral displacement or yaw angle. In this framework, a multipoint preview is feed‐forwarded to the control strategy to achieve good trajectory tracking performance with smaller fluctuation and control consumption [24, 25].…”