A Lyapunov-based hybrid model predictive control (LHMPC) method is proposed for the control of a vehicle hybrid logic dynamic system (MLD) that regulates vehicle height through asymmetric damping forces. This method addresses the limitations of traditional hybrid model predictive control (HMPC), including its inability to guarantee closed-loop stability, long prediction horizons, and excessive computational burden. The method incorporates the decreasing condition of the Lyapunov function as a contraction constraint mechanism, ensuring asymptotic stability throughout the control process. Additionally, by following the terminal constraint principle, the Lyapunov function is introduced as an inequality constraint set, replacing the terminal equality constraints typically used in traditional stability frameworks. This further guarantees the recursive feasibility and closed-loop stability of the MLD system optimization. Simulation results based on a seven-degree-of-freedom vehicle model demonstrate that the proposed LHMPC significantly outperforms conventional HMPC in terms of height tracking accuracy, convergence rate, vibration suppression, and real-time controller performance. Furthermore, the method can effectively harness the vehicle body’s vibrational energy while achieving coordinated control of vehicle height and posture, thereby reducing energy consumption during the height adjustment process.
Chen et al. (Fri,) studied this question.