傳統的回授線性法只適用於已知非線性系統，一旦系統存在未知項，則無法進行推導。本論文針對具未知項的非線性系統，提出新的輸入-輸出回授線性化的方法。其係對輸出訊號逐步施以微分，其間只要有未知項出現，即將之集總在一起。如此逐步推導，直至輸入訊號出現。接著經由一巧妙的座標轉換，可得到一線性非時變系統。此時，所有的不確定項，都集中於最後一階之中。該不確定項的變化邊界並不易求得，因此本文以函數近似法來處理之，並配合Lyapunov穩定法則來證明系統的穩定性。電腦模擬結果顯示，本文所提方法確實能對具未知項的非線性系統，進行回授線性化，並且得到良好性能。

Traditional feedback linearization is only applicable to known nonlinear systems. Once the system has uncertainties, the derivation cannot be feasible. This paper focuses on the input-output feedback linearization of nonlinear systems with uncertainties. The time derivatives of the output functions are taken until the presence of the control signal. During this derivation, all uncertainties will be lumped together. Finally, we can obtain a linear time invariant system through a particular set of coordinate transformations. At this step, all uncertainties are gathered into a term in the last subsystem. Then we may take advantage of the function approximation technique to estimate the uncertainty and prove system stability by the Lyapunov theorem. Simulation results show that the approach is feasible and can give desired system performance.

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