學習型模型預測控制(Learning-type Model Predictive Control, L-MPC)為一種將反覆學習控制用於更新模型預測控制器的參考輸入，再藉由模型預測控制器運算最佳控制量的控制演算法。此控制法具備最佳控制演算法概念以及限制處理之優點，同時離線批次更新學習後的參考輸入進一步獲得良好控制效能。現有文獻中所提的學習型模型預測控制主要是以P型學習律更新參考輸入，然而P型學習律在多次學習後易受高頻雜訊影響其學習穩定度，造成誤差發散。因此本文提出以PD型學習律設計學習模型預測控制器改善此問題，並首度應用此控制方法於線性伺服平台之精密運動控制。本研究探討此控制法於多種運動軌跡之追跡效能並比較上述兩種反覆學習控制律在追跡控制上所造成的差異；實驗結果證實本文所提出之學習型模型預測控制能有效改善伺服平台的定位效能，尤其加入輸入限制處理後更能降低在運動軌跡高頻轉角處所產生的追跡誤差。

Learning-type Model Predictive Control(L-MPC) is a type of model predictive control (MPC) approach that iteratively updates the setting input in the MPC control system by using so called learning control algorithms. This control algorithm reserves the advantages of MPC in calculating the optimal control moves at all sample steps and handling system constraints, and particularly obtains the optimal reference batch command for further improved control performance. The existing literature shows that P-type learning control law is widely adopted to the L-MPC control systems. However, the stability is easily influenced by high frequency noises and the learning performance may be deteriorated with divergent error. This study proposes a PD-type L-MPC control law to tackle this problem because of the better stability offered by the PD learning control. Unlike the slow dynamic system applications presented in the previous work, the proposed L-MPC control law is applied to a linear servo stage for precision motion control. Comparative experiments for tracking dynamic motion profiles are conducted to investigate the effects of using two different learning control laws. The results show that the proposed PD-type L-MPC achieves better tracking performance than P-type L-MPC, and can effectively reduce the error magnitude occurred at the abrupt corner of a challenging motion profile.

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