The viewpoint about human how to control themselves to achieve the assigned movements provides important reference materials for developing a control scheme to the human-like biped robot. In this paper, the human sensorimotor control consists of two control forces, i.e., an inverse control force in the feedforward loop and a feedback control force in the internal feedback loop to construct a robust tracking control scheme for 5-DOFs human-like biped robot. The inverse model with adaptive fuzzy dynamic in human brain provides an inverse control force to compensate most of system dynamics in a feedforward way. On the other hand, the internal feedback control loop that describes human spring-like muscles possesses a robust feedback control force to achieve the robust tracking with external disturbance and system uncertainty. Furthermore, if the state variables are unavailable, the forward model in the brain that behaves like a state estimator through the sensory information is utilized to predict system states for the feedforward inverse control and internal feedback control to achieve estimator- based tracking design even if the measurement noise is also considered. In addition, the proposed estimator-based control scheme based on human sensorimotor control concept can be formulated as an eigenvalue problem (EVP) with some linear matrix inequality (LMI) constraints, which can be solved very efficiently by the convex optimization techniques. Finally, a simulation example about human-like 5-DOFs biped robot is given to illustrate the design procedure and to confirm the performance of the proposed method.

近幾年來，機器人系統的控制問題已經廣泛的被研究。其中包括了系統動態分析、軌跡追蹤控制、機器人行走軌跡規劃以及如何在環境的影響下讓機器人完成被指派的任務等，都是重要的相關課題。除此之外，從生物的角度來探討控制問題也逐漸受到關注，像是人類如何完成自身動作的觀點在類人型雙足機器人的控制上提供了重要的參考。本篇文章提到了人類的感覺運動控制(Sensorimotor Control)主要由兩個控制力組成，即在順向迴圈(Feedforward Loop)方向的逆向控制力以及在內回授迴圈(Internal Feedback Loop)方向的回授控制力。我們將利用此觀點來建立五自由度類人型雙足機器人的強健追蹤控制。其中在人類大腦中具有適應性動態的逆向模型(Inverse Model)提供逆向控制力以順向的方式來補償受控系統大部分動態。另一方面，描述人類肌肉系統的內回授迴圈具有強健回授控制力以達到強健追蹤控制即使受到外部干擾以及受控系統不確定性的影響。再者，在受控系統狀態不可得知的情形下，在大腦中的順向模型(Forward Model)可透過感知的量測資訊來預測受控系統狀態以達到強健估測考量下的追蹤控制即使受到量測雜訊的影響。此外，本研究提出的控制架構可以轉換成求解含有線性矩陣不等式(Linear Matrix Inequality, LMI)限制條件的特徵值問題(Eigenvalue Problem, EVP)，而線性矩陣不等式則可以利用最佳化方法有效的求解。

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