Characteristic models and AILC design of time-varying nonlinear systems

This paper presents a characteristic modeling method for continuous/discrete time-varying nonlinear systems, where the model, the first-order time-varying differential equation, is a unified one. Learning identification algorithms are suggested for the purpose of parameter estimation, and the adaptive iterative learning control strategy is proposed for achieving the perfect tracking of the desired trajectory over a pre-specified finite-time interval. The proposed control scheme is applied on a permanent-magnet synchronous motor, where the least squares/gradient learning algorithms with a forgetting factor are applied, respectively, and the experimental results are presented to demonstrate the effectiveness of the learning control schemes.