Variable Universe Fuzzy PID Control for Active Suspension System With Combination of Chaotic Particle Swarm Optimization and Road Recognition

The traditional active suspension system controlled by fuzzy PID fails to consider external road information adaptively, lending to low control precision. To solve this problem, this novel variable universe fuzzy PID control strategy, which combines road recognition and chaotic particle swarm optimization (CPSO), is proposed. Firstly, a dynamic model of four degree of freedom vehicle suspension is established based on the half-vehicle model. Secondly, the Back Propagation (BP) neural network is optimized by Tent Sparrow Search Algorithm (Tent-SSA) to construct a road recognition model. When the road recognition module is constructed, the suspension control system can convert the suspension vibration signal into road information, and dynamically adjust the scaling factors of the variable universe fuzzy controller based on the road information. Thirdly, a modified coefficient is added to adjust the parameters obtained from the road recognition model, and the CPSO algorithm is used to optimize it to enhance control precision. Passive suspension, FPID control, and this novel control are constructed and simulated in MATLAB. The results indicate that this novel control strategy has improved in comprehensive performance by 28.47% compared to fuzzy PID control strategies.