Kinematic and Dynamic Modeling and Analysis of a Lower Extremity Exoskeleton

Yan, Lei; Shen, Qiong; Yang, Dalin; Qiao, Jiachang; Datseris, P.

doi:10.1109/urai.2019.8768556

Cited by 6 publications

(2 citation statements)

References 7 publications

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“…In previous research, the movement of the exoskeleton is always divided into the swinging phase and the stance phase, and they are modeled separately [18,[45][46][47]; therefore, it is not necessary to directly describe the foot-ground interaction and the constraint force of the knee joint after the leg is straightened. In this research, the dynamics of the exoskeleton in a complete gait cycle are considered, and two strong nonlinear factors, i.e., the foot-ground interaction and the constraint force at the knee joints, are modeled and incorporated.…”

Section: Dynamic Modelingmentioning

confidence: 99%

Online Adaptive PID Control for a Multi-Joint Lower Extremity Exoskeleton System Using Improved Particle Swarm Optimization

2021

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Robotic exoskeletons have great potential in the medical rehabilitation and augmentation of human performance in a variety of tasks. Proposing effective and adaptive control strategies is one of the most challenging issues for exoskeleton systems to work interactively with the user in dynamic environments and variable tasks. This research, therefore, aims to advance the state of the art of the exoskeleton adaptive control by integrating the excellent search capability of metaheuristic algorithms with the PID feedback mechanism. Specifically, this paper proposes an online adaptive PID controller for a multi-joint lower extremity exoskeleton system by making use of the particle swarm optimization (PSO) algorithm. Significant improvements, including a ‘leaving and re-searching mechanism’, are introduced into the PSO algorithm for better and faster update of the solution and to prevent premature convergence. In this research, a 9-DOF lower extremity exoskeleton with seven controllable joints is adopted as a test-bench, whose first-principle dynamic model is developed, which includes as many uncertain factors as possible for generality, including human–exoskeleton interactions, environmental forces, and joint unilateral constraint forces. Based upon this, to illustrate the effectiveness of the proposed controller, the human–exoskeleton coupled system is simulated in four characteristic scenarios, in which the following factors are considered: exoskeleton parameter perturbations, human effects, walking terrain switches, and walking speed variations. The results indicate that the proposed controller is superior to the standard PSO algorithm and the conventional PID controller in achieving rapid convergence, suppressing the undesired chattering of PID gains, adaptively adjusting PID coefficients when internal or external disturbances are encountered, and improving tracking accuracy in both position and velocity. We also demonstrate that the proposed controller could be used to switch the working mode of the exoskeleton for either performance or an energy-saving consideration. Overall, aiming at a multi-joint lower extremity exoskeleton system, this research proposes a PSO-based online adaptive PID controller that can be easily implemented in applications. Through rich and practical case studies, the excellent anti-interference capability and environment/task adaptivity of the controller are exemplified.

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Section: Dynamic Modelingmentioning

confidence: 99%

Online Adaptive PID Control for a Multi-Joint Lower Extremity Exoskeleton System Using Improved Particle Swarm Optimization

2021

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“…Numerous researchers have developed the kinematic and dynamic model of lower limb exoskeleton by assuming different number of DOF.The dynamic modeling of Load carrying lower extremity exoskeleton in [1] is done by splitting the gait cycle into stance and swing phase whereas in [2] the modeling is done from clinical Gait analysis data. The dynamic model of the lower extremity exoskeleton is derived in [3][4][5] and validated the torques generated at each joint without involving human integration and trajectory generation for gait cycle. In this paper, kinematic and dynamic model of a 6 DOF exoskeleton that includes hip, knee and ankle joints along with their masses, inertial properties and joint frictional forces of both the legs is developed and simulated to compute joint torque and power requirements as the lower extremity moves along a specified trajectory.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Evaluation of Adaptive Super Twisting Sliding Mode Control in Lower Extremity Exoskeleton

Ezhilarasi

Nair

2021

Int. J. of Precis. Eng. and Manuf.-Green Tech.

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In this paper, an integrated human-in-the-loop simulation paradigm for the design and performance analysis of a 6 DOF lower extremity exoskeleton is presented. An adaptive Super Twisting Sliding Mode Controller (ASTSMC) is designed for the trajectory tracking control of the exoskeleton by considering the human motion as reference trajectory. The dynamic model, that include linear and rotational displacement of hip, knee and ankle joints of both the legs is developed using Lagrange energy formulation. The position and angular velocity error of the wearer and the exoskeleton are being considered to establish the control law. Super twisting SMC is a robust control scheme that works effectively in the presence of external disturbances and parameter variations. However, the STSMC introduces chattering in the closed loop because of its high gain, to overcome this drawback, an adaptive STSMC is proposed for the control of exoskeleton against unknown disturbances without chattering. An adaptation scheme using Lyapunov criterion is derived that ensures the stability of the system in closed loop. The performance of the proposed control strategy is verified by implementing on the integrated CAD model of the exoskeleton along with the wearer. The effectiveness of the controller is tested under wind disturbance of varying velocity and direction. The results demonstrate improved tracking performance of the proposed control scheme with least error and less control effort compared to constant gain STSMC in normal and uneven terrain.

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8 Degrees of freedom human lower extremity kinematic and dynamic model development and control for exoskeleton robot based physical therapy

Hasan

Dhingra

2020

Int. J. Dynam. Control

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Kinematic and Dynamic Modeling and Analysis of a Lower Extremity Exoskeleton

Cited by 6 publications

References 7 publications

Online Adaptive PID Control for a Multi-Joint Lower Extremity Exoskeleton System Using Improved Particle Swarm Optimization

Online Adaptive PID Control for a Multi-Joint Lower Extremity Exoskeleton System Using Improved Particle Swarm Optimization

Modeling and Evaluation of Adaptive Super Twisting Sliding Mode Control in Lower Extremity Exoskeleton

8 Degrees of freedom human lower extremity kinematic and dynamic model development and control for exoskeleton robot based physical therapy

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