ITERATIVE LEARNING CONTROL USING ADJOINT SYSTEMS FOR NONLINEAR NON-MINIMUM PHASE SYSTEMS
Conventional Iterative Learning Control usually use input modification given by causal inversion. But for non-minimum phase systems this input modification will <br /> <br /> <br /> make increasing of input exponentially due to its unstable zero dynamics. In this paper will be...
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| 主要作者: | |
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| 格式: | Final Project |
| 語言: | Indonesia |
| 在線閱讀: | https://digilib.itb.ac.id/gdl/view/18128 |
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| 總結: | Conventional Iterative Learning Control usually use input modification given by causal inversion. But for non-minimum phase systems this input modification will <br />
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make increasing of input exponentially due to its unstable zero dynamics. In this paper will be proposed a method called stable inversion to obtain that problem. <br />
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This method is non-causal and bounded in a certain interval. Many iterative methods were developed for developing stable inversion. In this paper will be told a simple iterative method which is obtained from optimal control. This paper make minimization of error as performance index in optimal control problem, than will be gotten the adjoint system used as a parameter for updating <br />
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the input. By using adjoint system, Iterative Learning Control give good result. The simulation study show that the result of tracking for desired trajectory is achieved <br />
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for nonlinear non-minimum phase system. |
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