BP Neural Network-based Parameter Self-Learning Control

Parameter self-learning control based on BP neural network implements the following algorithm flow: (1) First, define the BP network architecture by specifying the number of input layer nodes (M) and hidden layer nodes (Q). Initialize weight coefficients for each layer and select appropriate learning rate and momentum factor parameters. Set time step k=1. (2) Sample the reference input signal rin(k) and system output yout(k), then compute the current error as error(k)=rin(k)-yout(k). (3) Calculate the input and output values for each layer of the neural network (NN). The outputs from the NN's output layer correspond to the three adjustable parameters of the PID controller. (4) Compute the PID controller's output u(k) using formula (3.34), which typically combines proportional, integral and derivative terms with the NN-optimized parameters. (5) Perform neural network training through backpropagation to adjust weight coefficients online, enabling adaptive tuning of PID control parameters. (6) Increment k by 1 and return to step (1) for continuous real-time adaptation. Key implementation notes: The BP network typically uses sigmoid activation functions for hidden layers. Weight updates employ gradient descent with momentum for stable convergence. The PID output calculation (step 4) integrates NN-generated parameters into the standard PID control law.