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Article Dans Une Revue IET Control Theory and Applications Année : 2020

Sub‐optimal recursively feasible Linear Parameter‐Varying predictive algorithm for semi‐active suspension control

Résumé

This paper proposed a control algorithm to enhance the comfort of passengers in a vehicle equipped with Semi-Active Suspension systems, manipulating the force delivered by the Semi-Active damper. For this goal, the vertical dynamics of the car are represented through a quasi-Linear Parameter Varying (qLPV) model. Then, the algorithm resides in solving a set-constrained Model Predictive Control (MPC) problem, embedding a comfort performance index to the MPC optimization function. The sub-optimality of this algorithm resides in the fact that the MPC is synthesized considering a frozen guess for the evolution of the qLPV scheduling parameters along the prediction horizon. Assuming bounds on the variation rates of the qLPV scheduling parameters, the method enables a replacement of the original complex nonlinear LPV MPC optimization by a much simpler Quadratic Program (QP). This QP includes a Lyapunov-decreasing stage cost and embeds set-based terminal ingredients, which guarantee that the domain of attraction of the controller is enlarged and that recursive feasibility can be maintained despite the non-exact model scheduling (and suboptimality). The control structure is tested and compared to other optimal control approaches, such as a clipped Linear Quadratic Regulator. The paper ends with successful realistic nonlinear simulations of a 1/5-scaled car with Electro-Rheological suspensions, which illustrate the overall good operation and behaviour achieved with the proposed regulation algorithm. With the proposed method, the comfort of the passengers is substantially improved.
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Dates et versions

hal-02924234 , version 1 (01-09-2020)

Identifiants

Citer

Marcelo Menezes Morato, Julio E Normey-Rico, Olivier Sename. Sub‐optimal recursively feasible Linear Parameter‐Varying predictive algorithm for semi‐active suspension control. IET Control Theory and Applications, 2020, 14 (18), pp.2764-2775. ⟨10.1049/iet-cta.2020.0592⟩. ⟨hal-02924234⟩
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