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• W2036401064 abstract "Internal models and adaptive controls are empirical and mathematical paradigms that have evolved separately to describe learning control processes in brain systems and engineering systems, respectively. This paper presents a comprehensive appraisal of the correlation between these paradigms with a view to forging a unified theoretical framework that may benefit both disciplines. It is suggested that the classic equilibrium-point theory of impedance control of arm movement is analogous to continuous gain-scheduling or high-gain adaptive control within or across movement trials, respectively, and that the recently proposed inverse internal model is akin to adaptive sliding control originally for robotic manipulator applications. Modular internal models' architecture for multiple motor tasks is a form of multi-model adaptive control. Stochastic methods, such as generalized predictive control, reinforcement learning, Bayesian learning and Hebbian feedback covariance learning, are reviewed and their possible relevance to motor control is discussed. Possible applicability of a Luenberger observer and an extended Kalman filter to state estimation problems-such as sensorimotor prediction or the resolution of vestibular sensory ambiguity-is also discussed. The important role played by vestibular system identification in postural control suggests an indirect adaptive control scheme whereby system states or parameters are explicitly estimated prior to the implementation of control. This interdisciplinary framework should facilitate the experimental elucidation of the mechanisms of internal models in sensorimotor systems and the reverse engineering of such neural mechanisms into novel brain-inspired adaptive control paradigms in future." @default.
• W2036401064 created "2016-06-24" @default.
• W2036401064 creator A5015802672 @default.
• W2036401064 creator A5049574955 @default.
• W2036401064 date "2005-08-31" @default.
• W2036401064 modified "2023-10-01" @default.
• W2036401064 title "Internal models in sensorimotor integration: perspectives from adaptive control theory" @default.
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• W2036401064 doi "https://doi.org/10.1088/1741-2560/2/3/s01" @default.
• W2036401064 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/2263077" @default.
• W2036401064 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/16135881" @default.
• W2036401064 hasPublicationYear "2005" @default.
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