Swing Phase Control of Semi-Active Prosthetic Knee Using Neural Network Predictive Control With Particle Swarm Optimization
Swing Phase Control of Semi-Active Prosthetic Knee Using Neural Network Predictive Control With Particle Swarm Optimization
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DOI：
10.1109/tnsre.2016.2521686
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发表时间：
2016-11-01
影响因子：
4.9
通讯作者：
Nilkhamhang, Itthisek
中科院分区：
文献类型：
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作者：
Ekkachai, Kittipong;Nilkhamhang, Itthisek
近年来，智能假肢膝盖已经被开发出来，使截肢者能够与健康人相比尽可能正常地行走。尽管利用磁流变（MR）阻尼器的半主动假膝有几个优点，但它们缺乏在正常步态周期的某些状态下产生主动力的能力。这使得半活动膝关节无法达到与活动膝关节相同的性能水平。在这项工作中，提出了一种新的半主动假膝在摆动阶段的控制算法来减少这种差距。该控制器采用神经网络预测控制和粒子群算法计算合适的指令信号。采用双摆模型的仿真结果表明，在不同的行走速度下，该控制器生成的膝关节轨迹比以往的开环控制器更接近正常步态。此外，研究表明，该算法可以通过嵌入式系统实时计算，从而易于在真实的假膝上实现。
In recent years, intelligent prosthetic knees have been developed that enable amputees to walk as normally as possible when compared to healthy subjects. Although semi-active prosthetic knees utilizing magnetorheological (MR) dampers offer several advantages, they lack the ability to generate active force that is required during some states of a normal gait cycle. This prevents semi-active knees from achieving the same level of performance as active devices. In this work, a new control algorithm for a semi-active prosthetic knee during the swing phase is proposed to reduce this gap. The controller uses neural network predictive control and particle swarm optimization to calculate suitable command signals. Simulation results using a double pendulum model show that the generated knee trajectory of the proposed controller is more similar to the normal gait than previous open-loop controllers at various ambulation speeds. Moreover, the investigation shows that the algorithm can be calculated in real time by an embedded system, allowing for easy implementation on real prosthetic knees.