For the complete trajectory tracking of uncertain dynamic systems, iterative learning control methods are proposed and sufficient conditions for convergency are provided. Dynamic systems that are considered in the dissertation are linear systems, a class of nonlinear systems and large scale interconnected systems, and, suitable methods are applied according to the types of dynamic systems.
For a class of linear continuous-time dynamic systems with unknown but periodic parameters, a method of iterative leaning control algorithm incorporating a system parameter estimator is presented, and a similar algorithm is also presented for a class of linear discrete-time dynamic systems. The proposed sufficient conditions are easier to check and more specific than previous works.
For a class of nonlinear continuous-time dynamic systems, a method is proposed, in which a nonlinear system model is used. The presented sufficient condition is related only to the input matrix. The method is shown to be applicable to the continuous-path control of a robot manipulator. It is also shown that the method for the linear discrete-time dynamic systems can be extended to nonlinear dynamic systems.
Finally, decentralized iterative learning control methods are presented for a class of large scale interconnected linear dynamic systems, in which the iterative learning controller in each subsystem operates in its local subsystem exclusively with no exchange of information between subsystems. Sufficient conditions for convergence of the algorithms are given. In particular, the algorithms are useful for the systems having large uncertainty of interconnect terms.
The effectiveness of the proposed methods is shown via computer simulations.