Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.781705
Title: Explicit model predictive control for active suspension systems with preview
Author: Theunissen, Johan
ISNI:       0000 0004 7967 3231
Awarding Body: University of Surrey
Current Institution: University of Surrey
Date of Award: 2019
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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, the authors of this study propose an explicit model predictive controller (e-MPC) for an active suspension system with preview. The MPC optimization problem is an mp-QP problem and is run offline. The online controller is reduced to a function evaluation. To overcome the increased memory requirements of e-MPC, the presented controller uses the recently developed regionless e-MPC approach. The controller was assessed through simulations and experiments on a sport utility vehicle (SUV) 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 (RMS) value of the vertical acceleration of the sprung mass with respect to the same vehicle with a skyhook controller. The addition of preview improves the performance by a further 8% to 21% depending on the test.
Supervisor: Sorniotti, Aldo Sponsor: Tenneco Automotive Europe BVBA
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.781705  DOI:
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