Learning-Based Error-Constrained Motion Control for Pneumatic Artificial Muscle-Actuated Exoskeleton Robots With Hardware Experiments
Learning-Based Error-Constrained Motion Control for Pneumatic Artificial Muscle-Actuated Exoskeleton Robots With Hardware Experiments
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DOI：
10.1109/tase.2021.3131034
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发表时间：
2022-10
影响因子：
5.6
通讯作者：
Tong Yang;Yiheng Chen;Ning Sun;Lianqing Liu;Yanding Qin;Yongchun Fang
中科院分区：
文献类型：
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作者：
Tong Yang;Yiheng Chen;Ning Sun;Lianqing Liu;Yanding Qin;Yongchun Fang
气动人工肌肉系统具有良好的生物适应性和灵活性，被广泛应用于外骨骼机器人中，以实现重复运动的康复训练。然而，PAM的一些固有特性和不可避免的实际因素，例如，高非线性、滞后、不确定动态以及有限的工作空间可能严重降低跟踪性能和安全性。因此，本文设计了一种新的基于学习的运动控制器的PAM，同时补偿模型的不确定性，消除跟踪误差，并满足预设的运动约束。特别地，当PAM具有周期性非参数不确定性时，精心设计的连续更新算法可以在线重复学习，提高跟踪精度，而不需要利用未知部分的上/下界进行控制器设计和增益选择。同时，一些非周期的不确定性处理的鲁棒项，其值只与PAM的初始状态，而不是确切的上界未知动态。从安全考虑，我们引入误差相关的饱和项，以限制饱和约束内的控制输入的初始振幅，并避免过大的误差引起过大的加速度。同时，利用约束相关的辅助项，将跟踪误差控制在允许的范围内。针对不确定PAM驱动的外骨骼机器人，提出了第一种基于学习的误差约束控制器，在不增加增益条件的情况下，实现了高精度的跟踪控制，提高了机器人的安全性.此外，跟踪误差的渐近收敛性严格证明了基于李雅普诺夫稳定性分析。最后，基于一台自行研制的外骨骼机器人，通过硬件实验验证了所提控制器的有效性。从业人员注意-这项工作是出于外骨骼机器人在康复训练和探索领域的实际需求。目前，PAM系统作为一种新型的柔性驱动装置，在外骨骼机器人控制的发展中发挥着越来越重要的作用。然而，不确定（或时变）的参数/结构和高度非线性的动力学，如蠕变和滞后，可能会严重增加控制的难度。此外，越来越高的跟踪精度和安全性要求也导致PAM驱动的外骨骼机器人的实际应用中迫切需要解决的问题，例如，平滑启动、运动约束和快速消除错误。为此，本文提出了一种新的基于学习的自适应控制器，它实现了精确的跟踪控制的PAM驱动的外骨骼机器人通过利用精心设计的重复学习算法和鲁棒项处理周期性和非周期性的不确定性，分别。更重要的是，所提出的控制器，同时提高PAM的瞬态性能，包括逐步提高跟踪精度，有效的约束启动加速度和跟踪误差。此外，它不需要考虑未知动态的上界和额外的增益选择条件，这对PAM系统具有重要的理论和实际意义。硬件实验进一步验证了该控制器的有效性和鲁棒性。在我们未来的工作中，我们打算设计更有效的方法来处理不可测状态和时滞的PAM。
Due to high biological adaptability and flexibility, pneumatic artificial muscle (PAM) systems are widely employed in exoskeleton robots to accomplish rehabilitation training with repetitive motions. However, some intrinsic characteristics of PAMs and inevitable practical factors, e.g., high nonlinearity, hysteresis, uncertain dynamics, and limited working space, may badly degrade tracking performance and safety. Hence, this paper designs a new learning-based motion controller for PAMs, to simultaneously compensate for model uncertainties, eliminate tracking errors, and satisfy preset motion constraints. Particularly, when PAMs suffer from periodically non-parametric uncertainties, the elaborately designed continuous update algorithm can repetitively learn them online to enhance tracking accuracy, without employing upper/lower bounds of unknown parts for controller design and gain selections. Meanwhile, some non-periodic uncertainties are handled by a robust term, whose value is only related to the initial states of PAMs, instead of exact upper bounds of unknown dynamics. From safety concerns, we introduce error-related saturation terms to limit initial amplitudes of control inputs within saturation constraints and avoid overlarge errors inducing overlarge acceleration. Meanwhile, the constraint-related auxiliary term is utilized to keep tracking errors within allowable ranges. To the best of our knowledge, this paper presents the first learning-based error-constrained controller for uncertain PAM-actuated exoskeleton robots, to realize high-precision tracking control and improve safety without additional gain conditions. Moreover, the asymptotic convergence of tracking errors is strictly proven by Lyapunov-based stability analysis. Finally, based on a self-built exoskeleton robot, the effectiveness of the proposed controller is verified by hardware experiments. Note to Practitioners—This work is motivated by the practical requirements of exoskeleton robots in rehabilitation training and exploration fields. Currently, PAM systems, as a kind of new flexible actuator equipment, are playing increasingly important roles in the development of exoskeleton robot control. However, uncertain (or time-varying) parameters/structures and highly nonlinear dynamics, such as creep and hysteresis, may badly increase the control difficulty of PAMs. Moreover, higher and higher tracking accuracy and safety requirements also induce urgently solved problems to practical PAM-actuated exoskeleton robots, e.g., smooth start, motion constraints, and rapid error elimination. To this end, this paper proposes a new learning-based adaptive controller, which realizes accurate tracking control for PAM-actuated exoskeleton robots by utilizing an elaborately designed repetitive learning algorithm and a robust term to handle periodic and non-periodic uncertainties, respectively. More importantly, the proposed controller simultaneously enhances transient performance of PAMs, including gradually improved tracking accuracy, effective constraints for startup acceleration and tracking errors. Additionally, it is not required to consider the upper bounds of unknown dynamics and additional gain selection conditions, which is theoretically and practically important for PAM systems. Some hardware experiments further verify the effectiveness and robustness of the suggested controller. In our future work, we intend to design more effective methods for PAMs with unmeasurable states and time-delay.