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In this paper, a hybrid particle swarm optimization genetic algorithm LQR controller is used on a quarter car model with an active suspension system. The proposed control algorithm is utilized to overcome the shortcoming that the weight matrix Q and matrix R determined by experience in the traditional LQR control method. The proposed hybrid control method makes it possible to achieve the optimal control effect. A full-order state observer is proposed to observe the state of active suspension. A quarter car active suspension model and road input model are presented at first, and the LQR controller based on the hybrid particle swarm optimization genetic algorithm is utilized in the active suspension system control. Sprung mass acceleration, suspension deflection, and tire dynamic load are selected as the control effect evaluation index. Next, simulation results are presented. According to the results, compared with the passive suspension and active suspension with a traditional LQR control, there is an obvious reduction in the sprung mass acceleration, deflection, and tire dynamic load with an optimized controller under case 1 and case 2. Simultaneously, the system state fed back by the full-order state observer can effectively reflect the true state of the active suspension system.

Active suspension plays a pivotal role in modern vehicles. In this paper, an adaptive PID controller of active suspension systems based on RBF neural network (RBF-NN) is developed. A quarter-car suspension system with two degrees of freedom is demonstrated. The values of proportional, integral, and derivate components are obtained by using Ziegler-Nichols(Z-N) tuning method and RBF-NN methods. The suspension system is perturbed using the sine function. Simulated in the Simulink environment is the quarter-car model. Passive suspension systems, adaptive PID controller utilizing the Z-N tuning approach, and adaptive PID based on the RBF-NN method for active suspension systems are compared. The active suspension with PID control based on the RBF-NN outperformed the active suspension with PID control utilizing the Z-N tuning approach and passive suspension, according to simulation data. The comparison demonstrates the proposed control method’s superior features

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