基于BPNN在线学习预测模型的扭矩实时跟踪控制

Real-time torque tracking control based on BPNN online learning prediction model

基于大量的台架试验数据,利用反向传播神经网络(BPNN)设计了燃油喷射在线学习预测模型,结合PID反馈完成扭矩跟踪的实时控制。其中,燃油喷射BPNN预测模型采用一种实时的简化离散模型,模型的阈值可以在线学习更新,具有参数自适应性。台架试验表明,相比于固定参数的BPNN模型,提出的阈值在线学习的BPNN模型具有更高的预测精度;提出的可变阈值VTBPNN预测前馈加PID反馈控制器能够满足扭矩跟踪的实时性需求,并且相比于普通可变参数VPPID控制器,在瞬态工况干扰下鲁棒性更强,跟踪误差更小。

An online learning prediction model for fuel injection based on a large amount of experimental data is designed by utilizing back propagation neural network(BPNN),and a real-time torque tracking controller consisting of the BPNN predictive feedforward and a PID feedback is proposed.A simplified and discretized real-time model is adopted in the BPNN prediction model.The threshold value of the BPNN prediction model with parameter adaptability can be learned and updated online.Several experimental results show that the BPNN prediction model with online variable threshold proposed in this paper has higher prediction accuracy comparing with the model with fixed threshold,and that the torque tracking controller proposed in this paper can satisfy the real-time control requirement and has smaller error rate under the transient condition comparing with the PID controller.