Optimization of Active Disturbance Rejection Control System for Vehicle Servo Platform Based on Artificial Intelligence Algorithm

The rapid growth of automotive intelligence and automation technology has made it difficult for traditional in vehicle servo systems to satisfy the demands of modern intelligent systems when facing complex problems such as external disturbances, nonlinearity, and parameter uncertainty. To improve the anti-interference ability and control accuracy of the system, this study proposes a joint control method of electronic mechanical braking control combined with anti-lock braking system. This method has developed a new type of actuator in the electronic mechanical brake control system, and introduced particle swarm optimization algorithm to optimize the parameters of the self disturbance rejection control system. At the same time, it combines adaptive inversion algorithm to optimize the anti-lock braking system. The results indicated that the speed variation of the developed actuator and the actual signal completely stopped at 1.9 seconds. During speed control and deceleration, the actuator could respond quickly and accurately to control commands as expected. On asphalt pavement, the maximum slip rate error of the optimized control method was 0.0428, while the original control method was 0.0492. The optimized method reduced the maximum error by about 12.9%. On icy and snowy roads, the maximum error of the optimization method was 0.0632, significantly lower than the original method's 0.1266. The optimization method could significantly reduce slip rate fluctuations under extreme road conditions. The proposed method can significantly improve the control performance of the vehicle mounted servo platform, reduce the sensitivity of the system to external disturbances, and has high practical value.