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Recursive Robust Regulator for Discrete-time Markovian Jump Linear Systems: Control of Series Elastic Actuators

机译:离散马尔可夫跳跃线性系统的递归鲁棒调节器:串联弹性致动器的控制

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Impedance control for human-robot interaction allows a more secure and controllable robotic rehabilitation. High-performance force control is required to achieve a more precise impedance-controlled robotic therapy using series elastic actuators. However, the performance of these controllers is sensitive to the highly uncertain and time-varying human dynamics. In this paper, we develop a force controller of a series elastic actuator using a Recursive Robust Regulator for discrete-time Markov jump linear systems subject to parametric uncertainties. We define three Markovian modes of operation depending on the human-robot interaction dynamics. Also, we include an integral action to eliminate steady-state errors, and propose a methodology for robust tracking based on the analysis of the frequency response of the system under parametric uncertainties. Experimental results of the impedance and force controllers for a robotic platform for ankle rehabilitation are presented. These results indicate that the proposed controller maintains similar performance levels despite the transitions between different modes of operation and even subject to the uncertainties introduced by human dynamics.
机译:人机交互的阻抗控制可实现更安全和可控的机器人康复。需要高性能的力控制,以使用串联弹性致动器实现更精确的阻抗控制的机器人治疗。但是,这些控制器的性能对高度不确定和时变的人类动态非常敏感。在本文中,我们针对受参数不确定性影响的离散时间马尔可夫跳跃线性系统,使用递归鲁棒调节器开发了系列弹性执行器的力控制器。我们根据人机交互作用的动力学定义了三种马尔可夫操作模式。此外,我们还包括一个消除稳态误差的整体动作,并基于对系统在参数不确定性下的频率响应的分析,提出了一种鲁棒跟踪的方法。提出了用于踝关节康复机器人平台的阻抗和力控制器的实验结果。这些结果表明,尽管在不同的操作模式之间进行了转换,但即使受到人类动力学引入的不确定性的影响,所提出的控制器仍可保持相似的性能水平。

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