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首页> 外文期刊>International Journal of Innovative Computing Information and Control >SUPPRESSION OF HAND TREMOR MODEL USING ACTIVE FORCE CONTROL WITH PARTICLE SWARM OPTIMIZATION AND DIFFERENTIAL EVOLUTION
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SUPPRESSION OF HAND TREMOR MODEL USING ACTIVE FORCE CONTROL WITH PARTICLE SWARM OPTIMIZATION AND DIFFERENTIAL EVOLUTION

机译：基于粒子群优化和微分进化的主动力控制压制人体动作模型

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摘要

Debilitating conditions for patients with hand tremor may find their daily activities such as writing and holding objects affected. In order to provide a non-invasive solution, an active tremor control technique is proposed to suppress a human hand tremor model. In this study, a hybrid controller which is a combination of the classic Proportional-Integration-Derivative (PID) and Active Force Control (AFC) strategy is employed to a four degree-of-freedom (4-DOF) biodynamic model of a human hand. In this study, two intelligent optimization techniques, namely, the Particle Swarm Optimization (PSO) and Differential Evolution (DE) methods are employed to estimate the PID and AFC parameters. The findings of the study demonstrate that the hybrid controller gives excellent performance in reducing the tremor error in comparison with the classic pure PID controller. Based on the fitness evaluation, the AFC-based scheme enhances the PID controller performance by about 25% for both PSO and DE techniques. The numerical simulation work could be used as an initial stage of study for the development of an anti tremor device for use on actual human subject that utilizes linear voice coil actuator as the main active suppressive element.
机译：手部震颤患者的虚弱条件可能会影响他们的日常活动，例如书写和握持物体。为了提供非侵入性解决方案，提出了一种主动震颤控制技术来抑制人手震颤模型。在这项研究中，将经典比例积分微分（PID）和主动力控制（AFC）策略结合在一起的混合控制器用于人类的四自由度（4-DOF）生物动力学模型手。在这项研究中，两种智能优化技术，即粒子群优化（PSO）和差分进化（DE）方法被用来估计PID和AFC参数。研究结果表明，与传统的纯PID控制器相比，混合控制器在减少震颤误差方面具有出色的性能。基于适应性评估，基于AFC的方案将PSO和DE技术的PID控制器性能提高了约25％。数值模拟工作可以用作开发抗震颤装置的初始研究阶段，该装置用于实际人体，该抗震颤装置利用线性音圈致动器作为主要的有源抑制元件。

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