Stabilization of inverted pendulum by the genetic algorithm

We consider the stabilization of an inverted pendulum which can be controlled by moving a cart in an intelligent way. Here, we adopt a PID control method to stabilize the pendulum. This controller requires the determination of PID control gains, but it is difficult to select the best gains theoretically. Thus, there have been many approaches to determine them empirically. Most of them are based on experience of operators and knowledge. Here, we propose a method to use a neural network to tune the PID gains such that human operators can tune the gains adaptively according to the environmental condition and systems specification. The tuning method is based on the error backpropagation method which 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 the initial values of PID gains. The experimental results show the effectiveness of the present approach