Tracking control of ball and plate system using a improved PSO on-line training PID neural network

In this paper, an on-line training PIDNN controller using an improved DEPSO algorithm for trajectory tracking of the ball and plate system is proposed. Since the ball and plate system is a typical under-actuated system with inherent nonlinearity and coupling between its parameters, the accurate mathematical model is difficult to be derived, so that a lot of nonlinear control and intelligent control methods are used for the ball and plate system control. The control method using a PID neural network is one of the intelligent methods. In this paper, an improved particle swarm optimization method based on differential evolution algorithm (DEPSO) is used to train the weighting factors of multilayered forward neural network. This PIDNN control method based on DEPSO algorithm can overcome the shortcoming of the BP algorithm which is easy to get into local minimum. At the same time, the simulation results of tracking control for ball and plate system show that the proposed PIDNN controller has simple structure, nice static and dynamic characteristics.