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To fill this gap, a novel bio-inspired modular control architecture that merges a recurrent cerebellar-like loop for adaptive control and a Smith predictor controller is proposed. The goal is to provide accurate anticipatory corrections to the generation of the motor commands in spite of sensory delays and to validate the robustness of the proposed control method to input and physical dynamic changes. The outcome of the proposed architecture with other two control schemes that do not include the Smith control strategy or the cerebellar-like corrections are compared. The results obtained on four sets of experiments confirm that the cerebellum-like circuit provides more effective corrections when only the Smith strategy is adopted and that minor tuning in the parameters, fast adaptation and reproducible configuration are enabled. <\/jats:p>","DOI":"10.1142\/s012906571950028x","type":"journal-article","created":{"date-parts":[[2019,9,25]],"date-time":"2019-09-25T10:44:36Z","timestamp":1569408276000},"page":"1950028","source":"Crossref","is-referenced-by-count":19,"title":["A Cerebellum-Inspired Learning Approach for Adaptive and Anticipatory Control"],"prefix":"10.1142","volume":"30","author":[{"given":"Silvia","family":"Tolu","sequence":"first","affiliation":[{"name":"Automation and Control Group, Department of Electrical Engineering, Technical University of Denmark, Richard Petersens Plads, Building 326, Kgs. Lyngby, 2800, Denmark"}]},{"given":"Marie Claire","family":"Capolei","sequence":"additional","affiliation":[{"name":"Automation and Control Group, Department of Electrical Engineering, Technical University of Denmark, Richard Petersens Plads, Building 326, Kgs. 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