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- W2973082020 abstract "Aiming at enhancing the locomotion capability of human and improving the walking economy, this paper leverages the iterative learning and particle swarm optimization (PSO) algorithm and develops a human-in-the-loop (HIP) optimization control method for the ankle exoskeleton. In the developed optimization control method, PSO is implemented to identify the ankle exoskeleton control parameters, and the iterative learning control algorithm is applied to control the optimized control parameters founded by the PSO optimizer. Since the developed method belongs to the family of customer-individualized control strategy, it may be suited to handle the response variations among different participants and has the potential to improve the walking economy of each participant. In order to validate the efficiency of the developed method, we have conducted an experiment in the case where eight healthy subjects wearing an ankle exoskeleton on their left legs. The experimental results show that the soleus muscle activities of both legs of each subject are reduced, which, to some extent, validates the feasibility of the developed method." @default.
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- W2973082020 date "2019-07-01" @default.
- W2973082020 modified "2023-09-25" @default.
- W2973082020 title "Human-in-the-Loop Optimization Control for the Ankle Exoskeleton During Walking Based on Iterative Learning and Particle Swarm Optimization Algorithm" @default.
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- W2973082020 doi "https://doi.org/10.1109/icarm.2019.8834170" @default.
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