Adaptive Tube-based Model Predictive Control for Vehicle Active Suspension System

Most vehicle active suspension control systems assume that the dynamic system model descriptions are accurate. However, there may exist modeling error and external disturbances for real world applications. While extensive research in robust model predictive control has been considered to handle such issues, the control performance may degrade due to the conservation of the prior uncertainty set. In this work, a vehicle active suspension control problem with modeling error and external disturbances is studied. We propose an adaptive tube-based model predictive controller to identify parameter uncertainty set and optimize reformulated quadratic optimization problem (QOP) for increasing control performance. The recursive feasibility and stability analysis of the proposed method is presented, and simulation results are demonstrated to indicate the effectiveness of the proposed algorithm.