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Invasive weed optimization algorithm optimized fuzzy logic scaling parameters in controlling a lower limb exoskeleton

机译：入侵杂草优化算法优化模糊逻辑缩放参数控制下肢外骨骼

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© 2016 IEEE. This paper describes a new modified versions of invasive weed optimization algorithm with exponential seeds-spread factor. The modified invasive weed optimization algorithm (MIWO) is employed to optimize the fuzzy input-output scaling factors of lower limb exoskeleton. A fuzzy logic control (FLC) system with the (MIWO) are evolved for reference tracking control. The exoskeleton is developed to enhance and upgrade the lower limb capability and augment the torque of knee and hip of elderly people during the walking cycle. Invasive weed optimization is a bio-inspired search algorithm that mimics how weeds colonize a certain area in nature. The algorithm is modified by applying local knowledge during distribution of seeds that depends on their cost function value in each generation to narrow the accuracy and improve the local search ability. The obtained results from the modified invasive weed optimization algorithm are compared with heuristic gain values to improve the performance of the exoskeleton system. The Visual Nastran 4D software is used to develop a simulation model of the humanoid and an exoskeleton for testing and verification of the developed control mechanism. Simulation results demonstrating the performance of the adopted approach are presented and discussed.
机译：©2016 IEEE。本文介绍了一种具有指数种子扩散因子的入侵杂草优化算法的新修改版本。采用改进的入侵杂草优化算法（MIWO）对下肢外骨骼的模糊输入输出比例因子进行优化。具有（MIWO）的模糊逻辑控制（FLC）系统已经发展成为参考跟踪控制。开发外骨骼是为了增强和提升下肢的能力，并在步行周期中增加老年人的膝盖和臀部的扭矩。侵入性杂草优化是一种受生物启发的搜索算法，可模拟杂草如何在自然界中的特定区域定居。通过在种子分配过程中应用本地知识来修改该算法，该知识取决于每一代种子的成本函数值，以缩小准确性并提高本地搜索能力。将改进的侵入性杂草优化算法获得的结果与启发式增益值进行比较，以改善外骨骼系统的性能。 Visual Nastran 4D软件用于开发类人动物和外骨骼的仿真模型，以测试和验证所开发的控制机制。仿真结果演示和讨论了所采用方法的性能。

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