Robust dynamic game distribution model predictive control strategy for multi-agent car system with dynamic interaction in high-speed elevators

To address horizontal vibrations in high-speed elevators caused by track unevenness and guide rail joints excitations and considering the multi-variable coupling of high-speed elevator systems, a robust dynamic game distributed model predictive control strategy based on dynamic multi-agent interaction is proposed. First, the coupling constraints of the elevator system are analyzed, and a multi-agent dynamic interaction model is established. Then, dynamic game theory and distributed model predictive control are integrated to minimize the dynamic information interaction objective function, achieving cooperative optimal control for multi-agent distributed model prediction. Additionally, a terminal constraint-based Lyapunov function is constructed to ensure asymptotic stability, mitigating the impact of predictive interaction information on system stability. Robust state feedback and H∞ performance metrics are then designed to suppress disturbances in the dynamic game system. Simulation results show that the acceleration responses of the multi-agent dynamic interaction car system under two typical rail excitations outperform those under passive control and multi-agent model predictive control (MAMPC) in terms of peak values and A95, validating the effectiveness of the proposed vibration reduction strategy.