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Research on the Delay Suspension System Based on Different Learning Rules

Track irregularity is one of the most important aspects of the suspension control performance impact. Due to the delay phenomenon of suspension system, the traditional PID control is unable to accurately track irregularity of track changes. Based on the qualitative analysis to delay suspension system, through modifying conventional PID controller, the article establishes four kinds of Neural Network adaptive PID Controllers based on different learning rules. The control strategy combines by the simple structure of the PID Control, the self-organizing ability of the Neural Network Control and the self-learning ability of the learning rules, adjusts PID parameters on-line, adapts to the change of track irregularity, and has strong robustness. However, the different learning rules have different learning ability of the interferences of track irregularity for the delay phenomenon of the suspension system. Simulation result shows that the supervised Delta learning rules is confirmed more effectively to realize tracking compared with other learning rules, faster response speed, more simple structure, easier operation for the track interference and load disturbance of the delay suspension system. This provides a very good way to solve the interference problem of orbit of the delay suspension system.