Short-Sighted Robust LPV Model Predictive Control: Application to Semi-Active Suspension Systems

Abstract

This paper develops a novel Linear Parameter Varying (LPV) Model Predictive Control (MPC) algorithm for Semi-Active Suspension systems. The current state-of-the-art comprises two possible implementations: a) to consider the future variations of the LPV scheduling variables as uncertainties, thereby solving a robust optimization, which is usually time-consuming; or b) to estimate the future scheduling variables and solve a sub-optimal quadratic program, which can be evaluated rapidly. This paper proposes a control paradigm in between these paths, considering a robust min-max procedure with small predictions horizons, being implementable within the short 5ms sampling period of the suspension system. The method includes terminal ingredients, derived via LMIs, that ensure input-to-state stability and recursive feasibility. Realistic simulations show the effectiveness of the proposed method, when compared against a nonlinear MPC and a sub-optimal LPV MPC. The results show that the method is indeed able to run in real-time (in the order of milliseconds), almost as fast as the sub-optimal MPC, while still guaranteeing good safety and comfort performances for the vehicle.