Self-tuning neuro-PID control and applications

In this paper, we propose a method to use the neural networks to tune the PID (proportional plus integral plus derivative) gains such that human operators tune the gains adaptively according to the environmental condition and systems specification. The tuning method is based on the error backpropagation method and hence it may be trapped in a local minimum. In order to avoid the local minimum problem, we use the genetic algorithm to find the initial values of the connection weights of the neural network and initial values of PID gains. The experimental results show the effectiveness of the present approach