Study On Iterative Learning Control For Harnessing The Nonrepetitive Disturbance

Iterative learning control (ILC) is a new intelligent control method developed in recent 20 years. According to some kind of repeatable characteristics existed in many complex industrial processes, current control information is revised in ILC by using the former control experience and the output errors of the system, the system output is convergent as far as possible to the expect value. But the nonrepetitiveness always existed in the actual system that traditional iterative learning control algorithm cannot harness it well. The new iterative learning control algorithm is established by the research on the nonrepetitiveness, which can be harnessed by the algorithm and the application field of iterative learning control will be broaden, iterative learning control will be better used into actual production by the new iterative learning control algorithm.The main contents and results of this thesis are summarized as follows:(1) The basic description framework of the iterative learning control is introduced; the development process, the development both here and abroad and the existing problems of iterative learning control are analyzed, also.(2) General description of ILC based super-vector is presented and theory of kalman filter is introduced. Convergence condition of kalman filter in iterative aix is analyzed and gained theoretically.(3) The Basic descriptions and the important characters of internal model control are introduced to the framework of the iterative learning control. Feedback filter is added to get new high-order ILC algorithm with better robustness.(4) Theory of adaptive inverse control is used based on internal model control. Thecapacity of harness the disturbance is improved (5) Above iterative learning control algorithms are established by Matlab software and simulation examples are presented to illustrate the effectiveness of the proposed method.