Neural Network PID Control: Utilizing Neural Networks as the Controller Rather Than for PID Parameter Tuning

Traditional PID control relies on precise tuning of the proportional, integral, and derivative parameters. In contrast, neural network-based PID control adopts a fundamentally different approach—it does not use the neural network simply as a parameter tuning tool but employs the neural network directly as the controller.

In neural network PID control, the neural network learns the dynamic characteristics of the system and directly outputs the control signal, effectively replacing the conventional PID controller structure. The weights of the neural network are continuously adjusted during the training process, enabling the system to adapt to complex, nonlinear, or time-varying control environments. Compared to traditional PID parameter tuning, this method handles uncertainties and nonlinearities more flexibly.

Thanks to the neural network's strong approximation ability and self-learning properties, it can adjust its weights online to indirectly optimize control performance, without the need for explicit PID parameter tuning. This approach is particularly suitable for scenarios where high-performance control is difficult to achieve using conventional PID tuning methods.