為追蹤一個機器人的參考模型，一個混模的控制器在此文中提出。它是由四個部分所構成：一個用來重組此機器人未知的非線性項的類神經網路(NN)；一個用來補償這個重組非線性項的適應性控制；一個當趨向奇異點時，暫時接管的高增益控制。最後，為一個抵制近似誤差造成的效能減退的強健控制。此法能夠維持適應性控制的優點且藉由結合暫時性的高增益控制，同時避免奇異點發生。此外，這個切換機制是完全的平滑，因此不會引起振動的現象。模擬結果證明此設計的可行性。
A hybrid controller for the tracking of a model reference of robots is presented. It consists of four parts: a neural network (NN) controller for resembling the unknown nonlinearities of the robot; an adaptive controller for compensating the resembled nonlinearities; a high-gain controller which takes over temporarily once the former is approaching singularity; last, a robust controller to counteract the degradation due to the approximation errors. Such an approach preserves the advantages of adaptive control scheme while avoids running into singularity at the same time by incorporating the temporary high-gain control. Moreover, the switching mechanism is absolutely smooth and hence does not incur any chattering behavior. Simulation results demonstrating the validity of the proposed design are given in the final.
