关键词: 动态惯性权重, 电子节气门, 迟滞非线性, 改进粒子群优化算法, 模糊神经网络

Abstract: Aiming at the dynamic hysteresis nonlinear problem of automotive electronic throttle system,a design method of fuzzy neural network PID controller was proposed.The controller combines the particle swarm optimization algorithm which adjusts the inertia weight dynamically with BP algorithm to optimize the parameters of fuzzy neural network,and corrects the shortcomings of slow convergence and easy to fall into the local minimum in the optimization process of fuzzy neural network.Using the self-learning ability of fuzzy neural network,the PID controller parameters were adjusted.The simulation results show that the optimized fuzzy neural network PID controller has a significant improvement in response time,overshoot and oscillation times compared with the fuzzy PID controller.After the disturbance signal was applied to simulate the airflow disturbance condition,the controller shows good anti-interference performance.In the electronic throttle response experiment,the throttle response curve has a slight overshoot,but the steady state error is small,which indicates that the electronic throttle has good dynamic response characteristics under this control method.

Key words: dynamic inertia weight, electronic throttle, hysteretic nonlinearity, improved particle swarm optimization algorithm, fuzzy neural network