scholarly journals Research on Robust Model Predictive Control for Electro-Hydraulic Servo Active Suspension Systems

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 3231-3240 ◽  
Author(s):  
Dazhuang Wang ◽  
Dingxuan Zhao ◽  
Mingde Gong ◽  
Bin Yang
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Active Suspension ◽  
Robust Model Predictive Control ◽  
Suspension Systems ◽  
Active Suspension Systems ◽  
Robust Model ◽  
Hydraulic Servo

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 ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Active Suspension ◽  
Explicit Model ◽  
Suspension Systems ◽  
Active Suspension Systems ◽  
Explicit Model Predictive Control

Incremental Model Predictive Control of Active Suspensions With Estimated Road Preview Information From a Lead Vehicle

2020 ◽  
Vol 142 (12) ◽  
Author(s):  
Siyang Song ◽  
Junmin Wang
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Heavy Traffic ◽  
Active Suspension ◽  
The Road ◽  
Suspension Systems ◽  
Incremental Model ◽  
Active Suspension Systems ◽  
Road Profile ◽  
Lead Vehicle

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.

An offline LMI-based robust model predictive control of vehicle active suspension system with parameter uncertainty

2018 ◽  
Vol 41 (6) ◽  
pp. 1699-1711 ◽  
Author(s):  
Shahab M Moradi ◽  
Ahmad Akbari ◽  
Mehdi Mirzaei
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Optimization Problem ◽  
Active Suspension ◽  
Suspension System ◽  
Performance Criteria ◽  
Parameter Uncertainties ◽  
Robust Model Predictive Control ◽  
Robust Model ◽  
Simulation Results

In vehicle active suspension design, it is desirable to improve performance criteria, such as ride comfort and road holding, subject to constraints on some states and control input. To tackle this constrained optimization problem, an offline robust model predictive control (RMPC) using linear matrix inequalities (LMIs) is proposed. In conventional model predictive control (MPC), an optimization problem is solved at each sampling interval, which might lead to task overrun and hence could prevent its real-time implementation. The proposed offline RMPC approach overcomes the problem by offline optimizations prior to implementation. Moreover, it is extended to take account of parameter uncertainties. To evaluate the effectiveness of the proposed approach, it is applied to a quarter-car suspension system with structured bounded uncertainties. Comparative simulation results show that the presented offline RMPC is much faster than both online RMPC and classic MPC approaches, yet with a competitive robust performance. In addition, simulation results with different road profiles endorse independence of the proposed offline RMPC from road excitations, as well as its efficiency to deal with shocks and vibrations.

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