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首页> 外文期刊>Mechatronics, IEEE/ASME Transactions on >Nonlinear Control of Robotic Manipulators Driven by Pneumatic Artificial Muscles
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Nonlinear Control of Robotic Manipulators Driven by Pneumatic Artificial Muscles

机译：气动人工肌肉驱动机器人的非线性控制

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摘要

Lightweight, compliant actuators are particularly desirable in safety-conscious robotic systems intended for interaction with humans. Pneumatic artificial muscles (PAMs) exhibit these characteristics and are capable of higher specific work than comparably sized hydraulic actuators and electric motors. However, control of PAM-actuated systems has proven difficult due to the highly nonlinear nature of the actuators and the pneumatic systems driving their actuation. This study develops and investigates the performance of three advanced control strategies—sliding mode control, adaptive sliding mode control, and adaptive neural network (ANN) control—each containing a distinct level of model knowledge, to enable smooth and accurate motion tracking of a single degree-of-freedom PAM-actuated manipulator. Originally developed by J.-J. Slotine and R.M. Sanner, the specific controllers employed in this study are significantly modified for application to pneumatically actuated open-chain manipulators with complex nonlinear dynamics. The two adaptive controllers are updated online and require no pretraining step. Several experiments are performed with each controller to evaluate and compare closed-loop tracking performance. Results highlight the dependence of a preferred control strategy on the level of model completeness and quality, and suggest that in most PAM-actuated manipulator scenarios, the ANN controller is preferable because it does not require a model of the pneumatic system or joint mechanism design, which can be difficult and time consuming to characterize, and is robust to changes in PAM actuator characteristics (due to fatigue or replacement).
机译：在旨在与人互动的具有安全意识的机器人系统中，特别需要轻巧的执行器。气动人造肌肉（PAM）具有这些特性，并且比同等大小的液压执行器和电动机具有更高的比功。然而，由于致动器和驱动其致动的气动系统的高度非线性特性，已证明控制PAM致动系统很困难。这项研究开发并研究了三种高级控制策略的性能-滑模控制，自适应滑模控制和自适应神经网络（ANN）控制-每个策略都包含不同级别的模型知识，从而能够对单个运动进行平滑且准确的运动跟踪PAM驱动的自由度操纵器。最初由J.-J. Slotine和R.M. Sanner，本研究中使用的特定控制器经过了重大改进，可应用于具有复杂非线性动力学的气动开链操纵器。这两个自适应控制器可以在线更新，并且不需要任何预培训步骤。每个控制器都进行了几次实验，以评估和比较闭环跟踪性能。结果突显了首选控制策略对模型完整性和质量水平的依赖性，并表明在大多数PAM驱动的机械手场景中，ANN控制器是可取的，因为它不需要气动系统模型或关节机构设计，表征起来可能很困难且耗时，并且对于PAM执行器特性的变化（由于疲劳或更换）具有鲁棒性。

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