基于QPSO改进LSTM发动机怠速预测的FPID控制OACSTPCD

Taking Beijing Hyundai Elantra G4GD engine as the testing bench,and the electronic control system fault as the experimental variable,the engine idle speed when the dual sensor combination fails within the specified time is measured,and six combined idle speed faults that are difficult to control by the original vehicle ECU are selected for the analysis.Based on the quantum particle swarm optimization(QPSO)algorithm,the hidden layer nodes,training times and learning rate of the long-term and short-term memory neural network(LSTM)are optimized and predicted.The prediction results are compared with the results of various neural networks,and judged by means of the evaluation indicators such as root mean square error(RMSE).The predicted output results are numerically fitted by means of Origin data fitting and input into Matlab Simulink to build the control unit model.The fuzzy constant-integral-differential(FUZZYPID,referred to as FPID)controller is used to control the idle speed of the output results.The results show that the improved LSTM based on QPSO has the best prediction effect.The FPID controller can effectively shorten the control time of the electronic control unit(ECU)for idle speed control,without overshoot,and can be effectively adjusted to the specified idle speed.