| Summary: | 碩士 === 國立成功大學 === 電機工程學系碩博士班 === 93 === A linear quadratic optimal learning control solution to the problem of finding a finite-time optimal control history for a nonlinear system is proposed in my thesis. This mechanism without the detailed information of the system that is influenced by unknown but repetitive disturbances yields the learning to achieve optimization. But for the linear systems, it is restricted to the range of eigenspectrums, the absolute eigenvalues of the linear systems. In order to improve the mechanism for more linear systems, the technique of pole placement is used. In the other hand, optimal linearization technique is used for the nonlinear systems. Before starting every iteration, we produce the relative optimal linear model at the operating point. Then, we design the controller of nonlinear systems to track the designed trajectories with linear quadratic learning control that is modified by pole placement. Illustrative examples and the results of simulation are proposed in my thesis to illustrate the mechanism and show how the improvement about the idea is.
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