Design of and Research into a Multiple-Fuzzy PID Suspension Control System Based on Road Recognition

• Published in: Processes Vol. 9; no. 12; p. 2190
• Main Authors: Ding, Xinkai, Li, Ruichuan, Cheng, Yi, Liu, Qi, Liu, Jilu
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.12.2021
• Subjects: Algorithms; Back propagation networks; Control systems; Controllers; Design; Dynamic loads; Fuzzy control; Fuzzy logic; Genetic algorithms; Mathematical models; Neural networks; Optimization; Parameter estimation; Parameters; Proportional integral derivative; Recognition; Road conditions; Simulation; Suspension systems; Vehicles
• ISSN: 2227-9717; 2227-9717
• DOI: 10.3390/pr9122190

By analyzing the shortcomings of the traditional fuzzy PID(Abbreviation for Proportional, Integral and Differential) control system (FPID), a multiple fuzzy PID suspension control system based on road recognition (MFRR) is proposed. Compared with the traditional fuzzy PID control system, the multiple fuzzy control system can identify the road grade and take changes in road conditions into account. Based on changes in road conditions and the variable universe and secondary adjustment of the control parameters of the PID controller were carried out, which makes up for the disadvantage of having too many single input parameters in the traditional fuzzy PID control system. A two degree of freedom 1/4 vehicle model was established. Based on the suspension dynamic parameters, a road elevation algorithm was designed. Road grade recognition was carried out based on a BP neural network algorithm. The experimental results showed that the sprung mass acceleration (SMA) of the MFRR was much smaller than that of the passive suspension system (PS) and the FPID on single-bump and sinusoidal roads. The SMA, suspension dynamic deflection (SDD) and tire dynamic load (TDL) of the MFRR were significantly less than those of the other two systems on roads of each grade. Taking grade B road as an example, compared with the PS, the reductions in the SMA, SDD and TDL of the MFRR were 40.01%, 34.28% and 32.64%, respectively. The control system showed a good control performance.