受控體的數學模式，其建構存在著種種困難。本論文不是在探求精確的數學模式，而在尋求數學模型建構時所產生之誤差的補償方法。更何況傳統的控制方法對於一個複雜、較高階及非線性受控體而言，其控制器之設計及性能指標不易達成，縱然有精確的數學模式也徒然。所以本論文為了達成控制的目的，並考慮系統參數變動、負載干擾、受控體未考慮到的非線性因素以及數學模型建構時之誤差等等的影響，可能會導致系統的工作偏離其性能指標，因此對一複雜、較高階、非線性之受控體，提出一個＜植基於小腦模型補償之狀態回授控制器設計＞來達成控制的目的，並改善控制系統的性能指標。
本研究採用狀態回授控制理論的系統設計，應用極點配置方法去穩定系統，並透過狀態回授來實現，使系統具有要求的閉迴路極點，再以外加小腦模型輔助補償控制的方式組成控制架構，因此而建立的控制方法所具備之特點為：對於二階之受控系統而言，由於狀態回授需要受控體的數學模式，吾人使用精度不太高的頻率響應法求取簡化模型，就能應用到控制系統中，而不必具有它們的數學描述；對於複雜、較高階及非線性之受控系統而言，雖然需要對系統的動態特性作數學描述，但是設計過程中無需對受控體或外部雜訊干擾等因素做精密測量及分析。而系統響應之誤差部份，則可藉由小腦模型控制器來改善。
本研究，先使用Matlab語言撰寫模擬控制之程式，並比較PID控制器之傳統控制方法、PID控制器的改良型控制方法及本論文所提控制方法等之控制器性能，以性能指標和輸出響應圖來評估本論文所提之控制方法的控制品質；再將所設計之非線性受控體的控制方法，以Matlab程式模擬方式作倒單擺的倒立平衡控制及球軸系統的平衡控制，驗證本論文之控制架構能有效改善複雜、較高階及不穩定之非線性控制系統的控制性能(performance)。
本研究為了驗證其控制方法具備學習與補償能力，於倒單擺的倒立平衡控制及球軸系統的平衡控制等之模擬當中，對負載干擾之抑制能力及控制系統參數變動之適應能力作測試，從小腦模型控制器加入與否的比較結果，驗證小腦模型控制器可改善控制系統並補償系統響應之誤差。於小腦模型控制器和可微小腦模型控制器的理論分析中，探討小腦模型控制器的特性。並以Turbo C語言撰寫等距離自動追蹤模型車控制系統程式，採取實驗方式以驗證小腦模型控制器之自動調整、自我學習的能力。

There are various difficulties existing in the plant establishment with mathematics mode. This paper is not looking for the accurate mathematics mode but the error compensation method that is occurred during the process of finding the establishment of mathematics model. For a complicated, higher order and non-linear plant, the traditional control method will be limited in designing and hard to get its performance index. For achieving the controlling purpose, this paper takes the effect on parameter variation of the system, load interference, the non-linear factors that plant has not taken into consideration and the error that is resulted during the establishment of mathematics model into account and considers that the factors mentioned above may have caused the work of system to deviate from its performance index. Therefore, we design a controlling method for the complicated, higher order, non-linear plant and submit 「The Design of Full-state Feedback Controller based on Cerebellar Model Compensation」 to achieve the controlling purpose and to improve the performance index of the controller system.
This study adopts the system design of the theory of full-state feedback controller and applies the pole-placement method to stabilize the system and to accomplish the stability through full-state feedback. The system will be possessed of the required pole of closed loop and then using the method of Cerebellar Model Assistance Compensation Feedback to form the control frame. Therefore, the established control frame has the following characteristics, which are: since full-state feedback needs the mathematics model of second order plant, we use the frequency response method with low accuracy to obtain the simplified model. And then it can be applied to the control system without their mathematics description. During the design procedure, we often use mathematics model to describe the dynamic properties of those complicated, higher order and non-linear plant.In the other word, we seldom take effort in accurately measuring and analyzing the plant and its external noise interference. And for the error in system response, it can be improved by using the Cerebellar Model Articulation Controller (CMAC).
We use Matlab language to write the program of controlling method for simulation respectively and compare it with the traditional control method of PID controller, the improved model control of PID controller and the controller performance of control architecture mentioned in this study. And the performance index and output response diagram is used to estimate the control quality of control architecture that is mentioned in this study after being applied to the general plant system. To design the control method of non-linear plant, we use Matlab program to simulate the headstand balance control of the Inverted-Pendulum system and balance control of Ball-Beam system and prove that the control architecture of this study can be effectively applied to the complicated, higher order and instable non-linear control system.
In the simulation of headstand balance control of the Inverted-Pendulum system and balance control of Ball-Beam system, the control ability of load interference and the adaptation ability of parameter variation of control system is tested and compared to the result with and without the addition of the CMAC. The compared result proves that the CMAC can effectively improve the control system and compensate the error of the system response. This paper discusses the characteristics of the CMAC through the discussion and theory analysis of one, two dimensions CMAC and differentiable CMAC. We use Turbo C language to write the control system program of equidistance auto tracing model car to prove the auto regulation and self-learning ability of the CMAC through experimental method.

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