受控主動運轉 (Controlled Active Motion, CAM)是幫助關節復原的術後復建療法，在臨床上有著良好的療效。研究顯示受控主動運轉不僅可以加快術後復元的速度；藉由結合前十字韌帶成形術與術後立即訓練神經肌，可以減少與本體感知受到的傷害。
傳統的受控主動運轉沒有電控設計，僅由機構設計而成。當病人想要嘗試較大的力量，可以調整機器的某些機構使得機器較難移動；反之可以調整使得機器較易移動。若使用馬達配合連桿的機構及電動控制，可以更方便、更有彈性地調整機台的移動情況。
本文主要的研究在於如何設計控制器使得機台準確地模擬一個質量-阻尼-彈簧系統。當足部放上機台，機台的參數會有急劇的變化，因此控制器不易設計。本文提出的滑模適應性類神經網路將被設計以解決此問題，如此的控制器可以結合滑模與模糊類神經網路控制器的優點，達成準確的控制。也會經由模擬與實驗驗證本文提出控制器之正確性。

The Controlled Active Motion (CAM) is the postoperative treatment that is designed to aid recovery after joint surgery. It is thought as a good treatment to accelerate the recovery time for the patient who has had a surgery in clinic. It is shown that the preoperatively existing, proprioceptive deficit can be reduced significantly by combining anterior crucial ligament plasty and neuromuscular training immediately postoperative using the CAM device.
The traditional CAM was designed without electronic control such as Camoped (Camoped, Germany). If the patient can not exert too large force just following surgery, the CAM would not be too heavy to be driven. On the contrary, the patient under good recovery condition may want to step on heavier machine. Therefore the resistance of CAM should be designed adjustable for different users. It is easier to adjust the resistance of electronic motorized CAM than in the practical mechanism.
In the thesis, the controller is designed to make the mechanism simulate a specified m-b-k system. The parameters of the mechanism vary very sharply due to the foot on the mechanism. Therefore the control scheme of sliding-mode adaptive Fuzzy Neural Network (FNN) was designed. The proposed control scheme combines the merits of sliding-mode and adaptive FNN controller. The simulation and experiment results will be shown to verify the proposed controller.

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