Stabilization of inverted pendulum by the genetic algorithm

The authors consider stabilization of an inverted pendulum which can be controlled by moving a cart in an intelligent way. Here, the authors adopt a PID(proportional plus integral plus derivative) control method to stabilize the pendulum since the PID controller has been extensively used in the industrial world. 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, the authors propose a method using neural networks to tune the PID 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 (BP method) and hence, it may be trapped in a local minimum. In order to avoid the local minimum problem, the authors 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.