基于动态惯性权重的电子节气门改进PSO-BP优化控制

Improved PSO-BP optimization control of electronic throttle based on dynamic inertia weight

针对汽车电子节气门系统存在的动态迟滞非线性问题,提出一种模糊神经网络PID控制器的设计方法。该控制器将动态调整惯性权重的粒子群优化算法和BP算法结合来优化模糊神经网络参数,修正模糊神经网络在寻优过程中收敛缓慢、易陷入局部最小值的不足。利用模糊神经网络的自学习能力,对PID控制器参数进行整定。仿真结果表明,经过优化后的模糊神经网络PID控制器相比于模糊PID控制器在响应时间、超调量和振荡次数等方面都有显着提升。在模拟气流扰动工况施加扰动信号后,该控制器表现出良好的抗干扰性能。在电子节气门响应试验中,节气门响应曲线存在轻微超调,但稳态误差较小,表明该控制方法下电子节气门具有良好的动态响应特性。

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.