scholarly journals Regionless Explicit Model Predictive Control of Active Suspension Systems With Preview

2020 ◽  
Vol 67 (6) ◽  
pp. 4877-4888 ◽  
Author(s):  
Johan Theunissen ◽  
Aldo Sorniotti ◽  
Patrick Gruber ◽  
Saber Fallah ◽  
Marco Ricco ◽  
...  
2019 ◽  
Vol 39 (3) ◽  
pp. 772-786 ◽  
Author(s):  
Zhang Houzhong ◽  
Liang Jiasheng ◽  
Yuan Chaochun ◽  
Sun Xiaoqiang ◽  
Cai Yingfeng

The vehicle semi-active suspension is a typical multiple-input multiple-output system with strong couplings, actuator constraints and fast dynamics. This paper addresses the damping force regulation of shock-absorber in vehicle semi-active suspensions using an explicit model predictive control (EMPC) approach, which allows minimizing the system control objective function while satisfying the actuator constraints. The main advantage of the proposed approach is that the control law computation requirement is low, and thus the EMPC system is suitable for implementation in a standard automotive microcontroller. The design of the EMPC system consists of mathematical modeling, objective function determination, controller formulation and simulation validation. Presented simulation results verify that a superior control performance of the vehicle semi-active suspension system is achieved by the proposed EMPC control approach compared with the performance obtained using conventional control method.


2020 ◽  
Vol 142 (12) ◽  
Author(s):  
Siyang Song ◽  
Junmin Wang

Abstract In preview-based vehicle suspension applications, the preview of the road profile is highly dependent on the preview sensors. In some scenarios such as heavy traffic situations, the preview of road profile can only be estimated by other vehicles because the view of the preview sensors may be blocked by other vehicles. The estimated preview road information can contain errors, which thus requires the controller to have a good robust performance. In this paper, an incremental model predictive control (MPC) strategy for active suspension systems along with a road profile estimator using preview information from a lead vehicle is proposed. The efficacy of the proposed strategy is experimentally validated on two scaled-down active suspension stations with comparison to two conventional active suspension control approaches.


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