This work experimentally investigates a model-predictive motion planning strategy to impose oscillatory and undulation movements in a macro fiber composite (MFC)-actuated robotic fish. Most of the results in this field exploit sinusoidal input signals at the resonance frequency, which reduces the device's maneuverability. Differently, this work uses body/caudal fin locomotion patterns as references in a motion planning strategy formulated as a model-based predictive control (MPC) scheme. This open-loop scheme requires the modeling of the device, which is accomplished by deriving a gray box state-space model using experimental modal data. This state-space model considers the electromechanical coupling of the actuators. Based on the references and the model, the MPC scheme derives the input signals for the MFC actuators. An experimental campaign is carried out to verify two references for mimicking the locomotion patterns of a fish under limited actuation. The experimental results confirm the motion planning scheme's capability to impose oscillatory and undulation movements.

中文翻译：

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宏观纤维复合材料驱动的软体机器鱼：有限驱动下的灰盒模型预测运动规划策略。

这项工作通过实验研究了模型预测运动规划策略，以在宏纤维复合材料（MFC）驱动的机器鱼中施加振荡和波动运动。该领域的大多数结果都利用谐振频率下的正弦输入信号，这降低了设备的可操作性。不同的是，这项工作使用身体/尾鳍运动模式作为运动规划策略的参考，该策略制定为基于模型的预测控制（MPC）方案。这种开环方案需要对器件进行建模，这是通过使用实验模态数据推导灰盒状态空间模型来完成的。该状态空间模型考虑了执行器的机电耦合。基于参考文献和模型，MPC 方案导出 MFC 执行器的输入信号。进行了一项实验活动，以验证在有限驱动下模仿鱼的运动模式的两个参考。实验结果证实了运动规划方案施加振荡和波动运动的能力。