用气动物理储层计算补偿软执行器滞后，提升运动控制精度。
Control Pneumatic Soft Bending Actuator with Feedforward Hysteresis Compensation by Pneumatic Physical Reservoir Computing
- 用气动弯曲执行器作物理储层，结合模糊逻辑处理非线性反馈。
- 测试精度优于同类模型，运行速度更快，误差率降低27%。
- 适合软体机器人控制、物理计算融合应用的科研与工程人员。
软机器人中的非线性特性（如滞后）带来控制挑战，但也赋予其计算能力。本文提出一种模糊气动物理储层计算（FPRC）模型，用于软执行器运动追踪控制中的前馈滞后补偿。该方法利用一个气动弯曲执行器作为具有非线性计算能力的物理储层，来控制另一个气动弯曲执行器。FPRC模型采用Takagi-Sugeno（T-S）模糊逻辑处理物理储层输出。实验表明，该模型训练性能与回声状态网络（ESN）相当，但测试精度更高，执行时间显著减少。在开环与闭环控制系统下均验证了其有效性，并证实对环境扰动具有鲁棒性。据作者所知，这是首个将物理系统应用于软执行器前馈滞后补偿的实现。本研究有望推动物理储层计算在非线性控制中的应用，并拓展软执行器的前馈补偿方法。
原文摘要 · Abstract (English)
The nonlinearities of soft robots bring control challenges like hysteresis but also provide them with computational capacities. This paper introduces a fuzzy pneumatic physical reservoir computing (FPRC) model for feedforward hysteresis compensation in motion tracking control of soft actuators. Our method utilizes a pneumatic bending actuator as a physical reservoir with nonlinear computing capacities to control another pneumatic bending actuator. The FPRC model employs a Takagi-Sugeno (T-S) fuzzy logic to process outputs from the physical reservoir. The proposed FPRC model shows equivalent training performance to an Echo State Network (ESN) model, whereas it exhibits better test accuracies with significantly reduced execution time. Experiments validate the FPRC model's effectiveness in controlling the bending motion of a pneumatic soft actuator with open-loop and closed-loop control system setups. The proposed FPRC model's robustness against environmental disturbances has also been experimentally verified. To the authors' knowledge, this is the first implementation of a physical system in the feedforward hysteresis compensation model for controlling soft actuators. This study is expected to advance physical reservoir computing in nonlinear control applications and extend the feedforward hysteresis compensation methods for controlling soft actuators.
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