The cerebellum, which is responsible for motor control and learning, has been suggested to act as a Smith predictor for compensation of time-delays by means of internal forward models. However, insights about how forward model predictions are integrated in the Smith predictor have not yet been unveiled. 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.

中文翻译：

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一种受小脑启发的自适应和预期控制学习方法

负责运动控制和学习的小脑已被建议充当史密斯预测器，以通过内部前向模型补偿时间延迟。然而，关于如何将前向模型预测集成到 Smith 预测器中的见解尚未公布。为了填补这一空白，提出了一种新颖的仿生模块化控制架构，该架构融合了用于自适应控制的循环小脑循环和 Smith 预测器控制器。目标是尽管存在感官延迟，但仍为电机命令的生成提供准确的预期校正，并验证所提出的控制方法对输入和物理动态变化的鲁棒性。将建议的架构与不包括 Smith 控制策略或类似小脑校正的其他两种控制方案的结果进行比较。四组实验获得的结果证实，当仅采用 Smith 策略并启用参数微调、快速适应和可重复配置时，类小脑电路提供更有效的校正。