Automatic Control of Automobile Engine Based on Neural Network Combined with PID

The automotive engine automatic control system is very complex and has high-order strong non-linear characteristics. The traditional PID controller used in automobile engine, whose parameters are fixed in the whole process of operation, leads to the problem of state change and uncertainty of system parameters in the actual operation process of automobile, and it is difficult to achieve the best control effect. Artificial neural network system is an Abstract and simplified simulation system for the basic characteristics of human brain function. It has the characteristics of flexibility and accuracy, good non-linear processing and fault tolerance, and has been widely used in modern industry. Using neural network to optimize PID control system can effectively solve the uncertain and non-linear problems existing in the operation of traditional automotive engines. Therefore, the automotive engine automatic control system is studied and designed based on the neural network optimization PID control system, and the control ability of the automotive engine automatic control system optimized by the neural network in the running process is verified by simulation experiments. The experimental results show that the system can effectively solve the non-linear problems existing in the traditional engine, and can still guarantee the sTable operation of the vehicle under harsh conditions and failures.