對於吞嚥困難的中風、頸脊髓傷害等神經損傷病患而言，吞嚥過程中最危險的就是因呼吸道閉合不完全導致食物進入呼吸道而發生吸入與嗆入的現象。為避免上述吞嚥障礙之發生，本研究發展功能性電刺激系統，刺激甲狀軟骨肌來輔助喉部上舉，以達到呼吸道閉合的第一道防線。因此肌肉系統為時變系統且其生物力學特性隨個體而有所差異，所以本研究應用適應控制於此神經義肢系統，並藉由神經肌肉電刺激實現喉部上舉的定位控制。

為判斷控制時機，本研究使用貼附在腹部的加速度規來偵測呼吸振動，發現可有效判斷訊號雜訊比較高的控制時機。系統模型鑑別則是採用虛擬二進位隨機訊號(pseudo random binary signal, PRBS)來測試喉部上舉系統的動態特性，PRBS訊號谷值與峰值分別為系統靜態測試之閥電流與飽和電流，藉此避開系統的非線性靜態特性。模型選用ARMAX模型，結果顯示二階系統即足以描述系統的主要動態特性。定位控制器設計則是採用適應控制理論中的自調式調節器，其估測模型是以二階系統為基礎並使用遞迴最小平方法估測參數，實驗結果顯示自調式調節器可即時估測系統特性，且控制器參數會隨著系統特徵改變而自調，並成功在家兔模型實現喉部上舉定位控制。

For stroke and spinal cord injury (SCI) patients who have dysphagia, the aspiration and pneumonia are the most dangerous in swallowing. Aspiration and pneumonia are caused by incomplete airway closure and food or saliva bolus would move into the airway during pharyngeal swallowing phase. In order to prevent aspiration and pneumonia, this study develop a neural prosthetic system to assist laryngeal elevation which is the major movement of airway closure by functional electrically stimulating the thyrohyoid muscle. Because the muscle has a time-varying biomechanical property and the dynamics of laryngeal elevation system is subject-dependent. The controller of neural prosthetic system is designed by using adaptive control theory to achieve control of laryngeal elevation.

To reduce disturbance from the output noise caused by the vibration of breath, the acceleration of abdominal was used to detect optimal timing for laryngeal elevation control especially when small signal-to-noise ratio was present. The method could be realized on-line. Data for identification of plant model was derived from the pseudo random binary signal (PRBS) response of the laryngeal elevation system. To reduce the static nonlinear dynamics of the plant, the valley and peak values of PRBS are chosen as the threshold and saturation values of the static recruitment voltages of the system. The model adopted is an autoregressive moving average with an exogenous signals (ARMAX) model and a second order model can fit the dynamic property of the system well. The adaptive controller of the neural prosthetic system was self-tuning regulator (STR) to achieve position control. The order of the plant model in STR was chosen to as two and the parameters were estimated by using the recursive least square (RLS) method. The experimental results showed that the model of laryngeal elevation system can be successfully estimated on-line, and parameters of the STR can be tuned on-line. In summary, by using the STR in the functional electrical stimulating system, the laryngeal elevation control of a rabbit can be achieved.

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