Study of Semiactive Adaptive Control Algorithms with Magneto-Rheological Seat Suspension

Author(s):  
Xubin Song ◽  
Mehdi Ahmadian
Author(s):  
Xubin Song ◽  
Mehdi Ahmadian ◽  
Steve Southfield ◽  
Lane Miller

This paper focuses on laboratory implementation of a semiactive seat suspension with application of magneto-rheological (MR) dampers. We firstly introduce the nonlinear dynamics phenomena induced with the skyhook control that is now widely applied from structural vibration suppression to commercialized vehicle suspensions. However, superharmonic dynamics has not been clearly addressed in such vibration control systems. This paper tries to explain how superharmonics are created with skyhook controls through testing data analysis. Furthermore, in order to avoid this dynamics issue, this study implements a nonlinear model-based adaptive control into this MR damper based seat suspension. Based on a nonparametric MR damper model, the adaptive algorithm is expanded mathematically, and the system stability is discussed. Then in the following sections, this paper describes implementation procedures such as modeling simplification and validation, and testing results. Through the laboratory testing, the adaptive suspension is compared to two passive suspensions: hard-damping (stiff) suspension with max current of 1A to the MR damper, and low-damping (soft) suspension with minimum of 0A, while broadband random excitations are applied with respect to the seat suspension resonant frequency in order to test the adaptability of the adaptive control. Furthermore, mass and spring rate are assumed known and unknown for this adaptive controller to investigate the capability of this algorithm with the simplified model, respectively. Finally the comparison of testing results is presented to show the effectiveness and feasibility of the proposed adaptive algorithm to eliminate the superharmonics from the MR seat suspension.


2019 ◽  
Vol 9 (16) ◽  
pp. 3326 ◽  
Author(s):  
Zhao ◽  
Wang

As a major device for reducing vibration and protecting passengers, the low-frequency vibration control performance of commercial vehicle seating systems has become an attractive research topic in recent years. This article reviews the recent developments in active seat suspensions for vehicles. The features of active seat suspension actuators and the related control algorithms are described and discussed in detail. In addition, the vibration control and reduction performance of active seat suspension systems are also reviewed. The article also discusses the prospects of the application of machine learning, including artificial neural network (ANN) control algorithms, in the development of active seat suspension systems for vibration control.


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