关键词: 主动悬架, 参数自适应粒子群算法, LQR控制

Abstract: A quarter-car active suspension model is built, and a fully parameterized adaptive particle swarm optimization (APSO) LQR control strategy is proposed to enhance vehicle handling stability. To remedy the difficulties in adjusting parameters in traditional LQR control strategies, the original particle swarm algorithm is adaptively improved with the consideration of multiple suspension performance indicators. By optimizing the objective function, the optimal weight matrix parameters are obtained to enhance control performance. Joint simulations are conducted on the Matlab/Simulink platform with various road input models, and the proposed APSO algorithm is compared with passive suspension and traditional LQR control. The results show APSO algorithm achieves higher convergence efficiency than the original algorithm. Compared with the traditional LQR active suspension controllers, the optimized control strategy markedly improves the performance indicators of the active suspension system. It achieves a marked improvement of over 40% in stability time and extrema, significantly enhancing ride comfort.