Population of the world above the age of 65 years is increasing rapidly. Aging causes weakening of human joints which increases constraints on mobility of the body. Sit-to-Stand (STS), an important part of Activities of Daily Living (ADL) is one of the motions that is affected because of weakened joints. With the lack of personal care there is going to be a need for devices which can assist the aging population in STS. We propose the use of a lower-limb exoskeleton as an assistive device. One of the main challenges in this area is to generate a human like reference trajectory for exoskeleton to follow. This paper proposes the use of Genetic Algorithm (GA), to generate reference trajectories for the joint angles for lower limb exoskeleton for STS transition. The fitness function for the GA presented here is constructed based on the fact that for a successful STS center of mass (COM) needs to stay in the area of support. After the trajectory generation a simple controller is proposed to control a 3 degrees of freedom exoskeleton. The dynamics of the system being controlled are modelled as an inverse 3 degrees of freedom pendulum and the equations are derived using the Euler-Lagrange equation. The highly non-linear dynamics are linearized using an input-output feedback linearization technique. A PD controller is presented for this linearized dynamic system and the validation of the controller is done using simulations. Simulation results show that GA successfully generates a human like trajectory which eliminates the need to use motion tracking system for measuring human trajectories.
- Dynamic Systems and Control Division
Trajectory Generation for a Lower Limb Exoskeleton for Sit-to-Stand Transition Using a Genetic Algorithm
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Chamnikar, AS, Patil, G, Radmanesh, M, & Kumar, M. "Trajectory Generation for a Lower Limb Exoskeleton for Sit-to-Stand Transition Using a Genetic Algorithm." Proceedings of the ASME 2017 Dynamic Systems and Control Conference. Volume 1: Aerospace Applications; Advances in Control Design Methods; Bio Engineering Applications; Advances in Non-Linear Control; Adaptive and Intelligent Systems Control; Advances in Wind Energy Systems; Advances in Robotics; Assistive and Rehabilitation Robotics; Biomedical and Neural Systems Modeling, Diagnostics, and Control; Bio-Mechatronics and Physical Human Robot; Advanced Driver Assistance Systems and Autonomous Vehicles; Automotive Systems. Tysons, Virginia, USA. October 11–13, 2017. V001T36A004. ASME. https://doi.org/10.1115/DSCC2017-5261
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