High-order Internal Model Based Iterative Learning Control And Application

In the dissertation, new iterative learning control (ILC) design methods are studied to deal with several non-repetitiveness problems in nonlinear systems. Particularly, a class of non-repetitiveness in uncertain parameter is considered which is generated from a high-order internal model (HOIM). Meanwhile, non-repetitiveness phenomenons in reference trajectory, unknown time-varying input gain, input/output disturbance and initial state are also taken into consideration. Main works and contributions in the dissertation are summarized as follows.1. In virtue of internal model principle, a HOIM-based iterative learning controller is proposed to deal with non-repetitive reference trajectory tracking problem for general continuous-time non-parametric systems. When the reference trajectory is generated from a HOIM, bounded convergence of the tracking error is proved theoretically, and the convergence condition is given correspondingly.2. In virtue of internal model principle, a HOIM-based adaptive iterative learning control algorithm is proposed for a class of continuous-time nonlinear parametric systems to deal with non-repetitive parameter which is generated from a HOIM. It shows that the parallel updating scheme provides wider applicable scope comparing with the high-order updating scheme. Through rigorous analysis, asymptotical convergence of the tracking error in the iteration domain is guaranteed when proposed algorithm is used, even though the reference trajectory is arbitrarily iteration-varying. When mixed HOIMs in multiple parameters are considered, a mixed parallel adaptive iterative learning control algorithm is developed correspondingly to deal with more complicated non-repetitiveness.3. A robust design approach for HOIM-based adaptive iterative learning control is proposed to deal with iteration-varying bounded input/output disturbance and iteration-varying bounded initial condition in a class of continuous-time nonlinear systems with non-repetitive parameters. Bounded convergence of the tracking error in the iteration domain is derived using proposed robust design.4. In virtue of internal model principle, a Recursive-Least-Squares-based adaptive iterative learning control algorithm and a projection-based adaptive iterative learning control algorithm are proposed respectively for a class of discrete-time nonlinear parametric systems, to deal with non-repetitive parameter which is generated from a HOIM. The effectiveness of both algorithms is verified through rigorous analysis, respectively.5. The parallel adaptive iterative learning control algorithm is extended into the spatial parallel adaptive iterative learning control algorithm when finite space interval is involved. The new control algorithm is applied to speed trajectory tracking problem and time trajectory tracking problem of a train operation, respectively. The effectiveness of proposed algorithm is verified by theoretical proof and experimental simulation. It is a promising application of the adaptive iterative learning control into the field of automatic train control.