Grey-Box Neural Network System Identification with Transfer Learning on Ball and Beam System

The present study investigates a friction fruit conveyor system development based on a traditional friction-less ball and beam system which share the commonalities of controlling object according to platform angle. Given that the ball and beam system is inherently open-loop unstable, a simple PID controller was designed to stabilize the ball to a predefined position on the beam. In most of the ball and beam literature, the system is assumed to be ideal, friction-free and usually linearized to a simplified model. The analytical model cannot be accurate in real life application. Subsequently, system identification is a standard procedure to estimate its corresponding model for optimal controller designs. With insight from an identified state-space model, parameters such as the number of tapped delay lines and hidden layers are designed. A grey-box neural network system identification with transfer learning is then proposed to identify a nonlinear friction ball and beam system. The identified model is adaptively based on a pre-trained neural network obtained from a linear friction-free BBS mathematical system. The performance of the grey-box identified neural network system model with transfer learning, is then compared with a model obtained from its black-box identified model with neural network structure. Subsequently, a similar procedure will be used to design a grey-box neural network model for fruit conveyor system based on this friction ball and beam. The results of simulation grey-box neural network with transfer learning based on the developed friction ball and beam model system model, is satisfactory.