A new neural network-based adaptive ILC for nonlinear discrete-time systems with dead zone scheme

Abstract

By introducing a dead-zone scheme, a new neural network based adaptive iterative learning control (ILC) (NN-AILC) scheme is presented for nonlinear discrete-time systems, where the NN weights are time-varying. The most distinct contribution of the proposed NN-AILC is the relaxation of the identical conditions of initial state and reference trajectory, which are common requirements in traditional ILC problems. Convergence analysis indicates that the tracking error converges to a bounded ball, whose size is determined by the dead-zone nonlinearity. Computer simulations verify the theoretical results.