LMI-based robust model predictive control for a quarter car with series active variable geometry suspension
CoRR(2024)
摘要
This paper proposes a robust model predictive control-based solution for the
recently introduced series active variable geometry suspension (SAVGS) to
improve the ride comfort and road holding of a quarter car. In order to close
the gap between the nonlinear multi-body SAVGS model and its linear equivalent,
a new uncertain system characterization is proposed that captures unmodeled
dynamics, parameter variation, and external disturbances. Based on the newly
proposed linear uncertain model for the quarter car SAVGS system, a constrained
optimal control problem (OCP) is presented in the form of a linear matrix
inequality (LMI) optimization. More specifically, utilizing semidefinite
relaxation techniques a state-feedback robust model predictive control (RMPC)
scheme is presented and integrated with the nonlinear multi-body SAVGS model,
where state-feedback gain and control perturbation are computed online to
optimise performance, while physical and design constraints are preserved.
Numerical simulation results with different ISO-defined road events demonstrate
the robustness and significant performance improvement in terms of ride comfort
and road holding of the proposed approach, as compared to the conventional
passive suspension, as well as, to actively controlled SAVGS by a previously
developed conventional H-infinity control scheme.
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