Data-driven Indirect Adaptive Iterative Learning Control Method

In this thesis,a series of indirect adaptive iterative learning control(i AILC)methods are proposed to deal with the three major problems of the existing indirect iterative learning control methods,i.e.,the structure of the set-point updating laws and the values of the learning gains are fixed,the existing indirect iterative learning methods are limited to linear systems,and there is no higher-order set-point updating law.For repetitive linear systems with exactly known model,i AILC with proportional(P),proportional-differential(PD)and proportional-differential-integral(PID)inner controllers are proposed,respectively.Firstly,an ideal nonlinear set-point iterative learning is proposed,then the nonlinear set-point learning law is transformed into a linear parameterized one by introducing the iterative dynamic linearization(IDL)technique.Then,an adaptive iterative estimation algorithm for the unknown learning gains is designed.Compared with the indirect iterative learning methods with fixed learning gains,the proposed methods performs better in the presence of uncertainties and disturbances by virtue of the introduction of the adaptive mechanism.The effectiveness of the proposed method is verified by mathematical analysis and simulation studies.For repetitive nonlinear nonaffine systems with no model information available,i AILC methods with P,PD and PID inner controllers are proposed,respectively.Using the IDL method,the nonlinear system is converted into an equivalent linear data model which facilitates the subsequent controller design and analysis.Both the design and the analysis of the proposed control methods are under the data-driven framework,i.e,only the measured input and output data are used without requiring any model information.Theoretical analysis and simulation results verify the usefulness of the proposed method.For repetitive nonlinear nonaffine systems with unknown model information,a datadriven high-order i AILC method is proposed.Compared with the lower-order ones,more control information of previous batches is utilized in the high-order i AILC method.Thus the control performance is further improved.The usefulness of this scheme is verified by mathematical analysis and simulation studies.