Parameter optimization of fuzzy PID control system for anti-lock system based on Beluga Whale Optimization

Automotive anti-lock braking system (ABS) can automatically adjust the braking torque to prevent wheel locking, but the complex braking process requires high precision and stability of the control system. In this paper, a fuzzy PID controller for ABS was designed and its parameters were optimized with the Beluga Whale Optimization (BWO) algorithm to improve braking performance. And the quantized factors and the proportional factors of the controller were optimized according to the theory of the BWO algorithm and the slip rate error, which eliminated the subjectivity of setting the parameters based on experience. The optimized fuzzy PID controller can stably control the slip rate around 0.2 with higher accuracy, stability, and robustness. Based on fuzzy PID control and logic threshold control strategy, joint simulation models of vehicle ABS braking control system and dynamic system were established based on Chinese automaker Chery Tiggo 5 model parameters with software Carsim and Simulink. Four different braking conditions were designed in the simulation, which were (1) straight road braking with a single adhesion coefficient, (2) steering road braking, (3) bisectional pavement braking, and (4) joint pavement braking. Under the four braking conditions, the braking distance and braking time of the ABS with the optimized fuzzy PID controller are shortened by 1.72 m and 0.11 s, 1.89 m and 0.15 s, 4.12 m and 0.23 s, and 2.98 m and 0.17 s, respectively, compared with those of the ABS with the logic threshold control. The braking results showed that the ABS fuzzy PID controller optimized with the BWO algorithm improves the braking performance both on complex road surfaces and during vehicle steering. The designed ABS fuzzy PID controller with effectively optimized parameters can improve the braking ability.