Neural network based terminal iterative learning control for tracking run-varying reference point

In this paper, a neural network based terminal iterative learning control (NNTILC) method is proposed for a class of discrete time linear run-to-run systems to track run-varying reference point with initial state disturbance. An iterative training radial basis function (RBF) neural network is developed to estimate the effect of initial state on terminal output and to learn the changes in initial state iteratively at the same time. By involving these information in the control scheme, the proposed NNTILC can drive the system to track run-varying reference point fast and precisely beyond the initial disturbance and reference change. Stability and convergence of this NNTILC method is proved and computer simulation results confirm its effectiveness further.