The Design Of Iterative Learning Control Under Nonstandard Conditions

Iterative learning control (ILC) has been widely recognized by its unique capability in improving the robust performances of a control system, which usually based on the notion of repetition and learning. But good tracking performance employing conventional ILC strategy has largely relied on the assumptions and initial conditions, such as no structure uncertainties and disturbances, same or similar desired trajectories and so on, which largely limited its industry implementations. So how to apply the ILC strategy under nonstandard conditions concerns the present and furore of ILC.In this dissertation a robust iterative learning control strategy is proposed for against the disturbances, perturbation, noises and so on. For the issue of unknown but limited disturbances, one separated design scheme is presented for the open-and-close-loop structure. Then it is resolved as a standard H∞problem. For structured uncertainties and disturbances related issues, a synthesis method is utilized to choose systematically the parameters of learning and feedback controllers while achieving the optimization between robustness and tracking performance. In the presence of changeable tracking trajectories, a locally weighted method is employed for restructuring plant inversion which helps to estimate the optimized initial input.The main achievements are listed as followed.The first part mainly concerned with robust performance issues while a linear system is perturbed by unknown disturbances. By adding the ILC open-loop along to the original control system, it can improve the tracking accuracy while not destroying original system robustness. The design is then resolved as a comparable mold-match problem and a standard H∞method is employed for parameters design. The effectiveness of the method is demonstrated by the simulation of rigid manipulators.In the following part a two-degree-of-freedom architecture is employed for dealing with the issues of unstructured uncertainties, disturbances and so on. This synthesis method can systematically design the whole open-and-close-loop simultaneously which in return achieve the optimization between the robust stabilities and performances. The method of structured singular value andμsynthesis is employed for deriving the final parameters. A simulation on servo motor control system is conducted last for demonstration.The last part focused on the issues of changeable tracking trajectories. The importance of the selection of initial control input in error convergence is highlighted. The locally weighted theory is introduced for restructuring the linearized plant inversion while estimating the optimized initial input. This method is so general that it can be applied to most of ILC algorithms, including nonlinear ones. The computer simulation is also conducted on the servo motor control system for discussion.