Stability of inverted pendulum by neuro-PID control with genetic algorithm

We consider stabilization of an inverted pendulum which can be controlled by moving a cart in an intelligent way. Here, we adopt a PID (proportional plus integral plus derivative) control method to stabilize the pendulum. We propose a method to use neural networks to tune the PID gains such that human operators can tune the gains adaptively according to the environmental condition and systems specification. In order to avoid the local minimum problem, we use the genetic algorithm to find the initial values of connection weights of the neural network.