A lower limb assistive exoskeleton is designed to help operators walk or carry payloads. The exoskeleton is required to shadow\nhuman motion intent accurately and compliantly to prevent incoordination. If the user�s intention is estimated accurately, a\nprecise position control strategy will improve collaboration between the user and the exoskeleton. In this paper, a hybrid position\ncontrol scheme, combining sliding mode control (SMC) with a cerebellar model articulation controller (CMAC) neural network,\nis proposed to control the exoskeleton to react appropriately to human motion intent. A genetic algorithm (GA) is utilized to\ndetermine the optimal sliding surface and the sliding control law to improve performance of SMC. The proposed control strategy\n(SMC GA CMAC) is compared with three other types of approaches, that is, conventional SMC without optimization, optimal\nSMC with GA (SMC GA), and SMC with CMAC compensation (SMC CMAC), all of which are employed to track the desired\njoint angular position which is deduced from Clinical Gait Analysis (CGA) data. Position tracking performance is investigated\nwith cosimulation using ADAMS and MATLAB/SIMULINK in two cases, of which the first case is without disturbances while the\nsecond case is with a bounded disturbance. The cosimulation results show the effectiveness of the proposed control strategy which\ncan be employed in similar exoskeleton systems.
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