本文關(guān)鍵詞：自適應終端迭代學習控制的關(guān)鍵問題研究　出處：《青島科技大學》2017年碩士論文　論文類型：學位論文

  更多相關(guān)文章： 初始條件變化 參考軌跡變化 終端迭代學習控制 高階內(nèi)模

【摘要】：本論文主要是對自適應終端迭代學習控制中存在的初始條件變化、參考軌跡變化等問題進行了進一步的研究,提出了一系列自適應終端迭代學習控制的新方法。論文的主要創(chuàng)新點及貢獻可總結(jié)如下:第一,針對一類非線性單入單出(SISO)離散時間系統(tǒng),通過在控制器中加入遺忘因子,以達到提高控制性能的目的,并對其進行了嚴格的收斂性分析,證明了未知參數(shù)和輸入輸出的有界性,以及跟蹤誤差的漸進收斂性。同時,仿真結(jié)果也驗證了加入遺忘因子的控制器具有更好的控制性能。第二,針對一般的多入多出(MIMO)線性時變系統(tǒng),提出了隨機高階內(nèi)模的方法來處理迭代變化的初始條件。研究中,期望參考點也是隨迭代變化的。從而,所提出的方法克服了傳統(tǒng)的迭代學習控制中對相同初始條件和目標軌跡嚴格相同的限制。嚴格的理論分析證明了方案的可行性,在仿真中與以前基于相同初始條件提出的控制方法進行比較,有效地驗證了所提的基于高階內(nèi)模的自適應終端迭代學習控制方法的優(yōu)勢。第三,進一步,將基于高階內(nèi)模的方法推廣到非線性系統(tǒng)終端迭代學習控制中。高階內(nèi)模用于對被控非線性系統(tǒng)的間接描述,以此設(shè)計自適應迭代學習控制器。該控制器中不包含被控系統(tǒng)模型的信息,而是只利用可測的輸入輸出數(shù)據(jù),是一種數(shù)據(jù)驅(qū)動的控制方法。通過嚴格的數(shù)學分析,在理論上證明了所提方法是穩(wěn)定的,并且能夠使得跟蹤誤差漸進收斂到零。仿真研究中加入隨機擾動,更進一步驗證了所提方案的有效性和實用性。
[Abstract]:In this paper, the problems of the change of initial conditions and the change of reference trajectory in adaptive terminal iterative learning control are studied further. A series of new adaptive terminal iterative learning control methods are proposed. The main innovations and contributions of this paper can be summarized as follows: first, for a class of nonlinear discrete time systems with single input and single output (SISO). The forgetting factor is added to the controller to improve the control performance, and the convergence of the controller is analyzed strictly. The boundedness of unknown parameters and input and output is proved. And the asymptotic convergence of tracking error. At the same time, the simulation results show that the controller with forgetting factor has better control performance. Second, for the general multi-input multi-output MIMO-linear time-varying system. A stochastic high-order internal model method is proposed to deal with the initial conditions of iterative variation. In the study, the expected reference points also change with the iteration. The proposed method overcomes the limitation of the same initial condition and target trajectory in the traditional iterative learning control. The rigorous theoretical analysis proves the feasibility of the scheme. Compared with the previous control methods based on the same initial conditions in simulation, the advantages of the proposed adaptive terminal iterative learning control method based on high-order internal model are validated effectively. Thirdly, further. The method based on higher order internal model is extended to the terminal iterative learning control of nonlinear systems. The higher order internal model is used to describe the controlled nonlinear system indirectly. An adaptive iterative learning controller is designed. The controller does not contain the information of the controlled system model, but only uses measurable input and output data. The proposed method is proved to be stable in theory by strict mathematical analysis, and the tracking error can converge gradually to zero. Random perturbation is added to the simulation research. The effectiveness and practicability of the proposed scheme are further verified.
【學位授予單位】：青島科技大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP273

【參考文獻】

相關(guān)期刊論文 前10條

1 Yu Liu;Rong-Hu Chi;Zhong-Sheng Hou;;Neural Network State Learning Based Adaptive Terminal ILC for Tracking Iteration-varying Target Points[J];International Journal of Automation and Computing;2015年03期

2 ;Adaptive Iterative Learning Control for Nonlinearly Parameterized Systems with Unknown Time-varying Delay and Unknown Control Direction[J];International Journal of Automation and Computing;2012年06期

3 池榮虎;侯忠生;王鄲維;金尚泰;;非線性離散時間系統(tǒng)的最優(yōu)終端迭代學習控制(英文)[J];控制理論與應用;2012年08期

4 裴九芳;王海;許德章;;基于迭代學習控制的移動機器人軌跡跟蹤控制[J];計算機工程與應用;2012年09期

5 楊俊友;馬航;關(guān)麗榮;楊松;;永磁直線電機二維分段復合迭代學習控制[J];中國電機工程學報;2010年30期

6 閻世梁;張華;王銀玲;肖曉萍;;極坐標下基于迭代學習的移動機器人軌跡跟蹤控制[J];計算機應用;2010年08期

7 池榮虎;侯忠生;;非線性非仿射離散時間系統(tǒng)的兩階段最優(yōu)迭代學習控制(英文)[J];自動化學報;2007年10期

8 石陽春;周云飛;李鴻;李介明;黃永紅;;長行程直線電機的迭代學習控制[J];中國電機工程學報;2007年24期

9 姚仲舒,楊成梧;迭代學習控制在煙葉發(fā)酵系統(tǒng)中的應用[J];自動化儀表;2002年12期

10 劉山,吳鐵軍,劉玉文,王治國;無縫鋼管張減過程平均壁厚控制迭代自學習方法[J];鋼鐵;2002年04期

，

本文編號：1378164