本論文針對兩輪行動載具的動態數學模式、運動控制，以及系統的穩定性進行研究。兩輪行動載具主要是依靠兩顆獨立的車輪馬達來驅動，承載人員或是物品。兩輪行動載具的動態方程式在本論文中分別使用動力學分析和Euler-Lagrangian的方法來建立，由動態方程式中可以發現，兩輪行動載具本身是一個相當不穩定的系統，而它的姿態主要是依靠它的兩輪來做控制。基於控制兩輪行動載具的運動與維持其傾斜角的穩定，本論文參照文中所推導的動態方程式，提出一個自調式比例-積分-微分控制策略來實現兩輪行動載具的運動控制。由於所提出的比例-積分-微分控制器參數會隨著響應情況來自行調整內部的參數，使得追蹤誤差能夠最小，因此在系統發生參數變動與外來的干擾時，皆能夠具有強健性來克服。另外，由於此方法的架構簡單，所以容易實現，且無須耗費處理器較多的計算資源。
兩輪行動載具可使用在平地或是在有坡度的地形上，而在有坡度的地形上時，兩輪行動載具往往需要較大的傾斜角來維持系統的穩定性。所以基於增強此非線性系統傾斜角追蹤的強健性與平順性，以實現兩輪行動載具的運動控制系統，本論文提出了一個新型的強健式參考模型控制策略，其中包含了參考模型模糊類神經網路控制器與強健式模糊類神經網路補償器。參考模型模糊類神經網路控制器是設計依照參考模型與系統之線性化模型輸出的誤差來即時調整其內部的權重值，使得線性化模型輸出會追蹤參考模型；而強健式模糊類神經網路補償器則是用來克服系統參數變動與外部干擾的影響，適時產生補償的能量來維持系統的響應與穩定，並藉由更新法則調整其內部參數，達到系統的輸出近似系統之線性化模型的結果。結合以上的控制器與補償器，所提出的強健式參考模型控制策略將可確保系統的響應與參考模型的輸出一致，並增強兩輪行動載具運動在不同地形上的強健性。除此之外，在兩輪行動載具系統發生參數變動、外來的干擾，或是變更參考模型時，參考模型模糊類神經網路控制器與強健式模糊類神經網路補償器皆將藉由即時的調變其權重和歸屬函數來維持系統的穩定性及所需的控制性能。
將自調式比例-積分-微分控制器結合差速控制器或是強健式參考模型控制器結合模糊差速控制器，即可達成兩輪行動載具轉彎角速度的運動控制。由模擬與實作的結果可以顯示兩輪行動載具動態方程式的正確性，以及本論文所提出之兩輪行動載具控制策略的效能及強健性。

This dissertation presents the dynamic model, the motion control and stability analysis of a two-wheel mobile vehicle (TWMV). The TWMV is driven using two independent wheel motors, upon which a vehicle body is mounted. A mathematical model of the TWMV is derived and established using dynamic analysis and Euler-Lagrangian method. The TWMV is inherently unstable and its position is controlled through the actions of the wheel motors. Vehicle motion depends on both the desired wheels response and the tilt angle. A self-tuning proportional-integral-derivative (STPID) control strategy, based on a deduced model, is proposed for implementing a motion control system that stabilizes the TWMV and follows the desired motion commands. The controller parameters are tuned automatically, on-line, to overcome the disturbances and parameter variations. Since the STPID control scheme is not very complex, the system is easy to implement and is not taken much computing time.
The TWMV is used as a transport, which works on the level ground or an incline. These motions of TWMV may be operated in more wide tilt angle range; especially it’s on an incline. Therefore, this dissertation proposes a new robust model reference motion control scheme of the TWMV for enhancing the system robustness. The robust model reference fuzzy neural networks control (RMRFNNC), consisted of a model reference fuzzy neural networks controller (MRFNNC) and a robust fuzzy neural networks compensator (RBFNNC), is proposed for implementing a motion control system that makes the TWMV be stable and traces desired tilt angle smoothly. According to the error between the reference model and the linearization model output, it will real-time adjust the weights in the MRFNNC using the update rule such that the overall system follows the trajectory of the reference model. The RBFNNC is designed to resist the system parameter variations and external disturbances of TWMV. It can provide a compensated force to TWMV that adjust its output response matching the linearization model. Combine MRFNNC and RBFNNC, the TWMV will depend on the reference model, procure the reference model control and enhance the system robustness on the level ground or an incline. Whether the TWMV internal parameters exhibits variations, suffers external disturbances or changes the reference model, RBFNNC and MRFNNC will immediately update its weights and membership functions, keeping system be stable and have a high performance. Applying the designed fuzzy differential controller, the TWMV can rotate right and left as desired. Computer simulations and experimental results demonstrate the reliability of the TWMV dynamic model and effectiveness of the proposed control schemes.

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