Design of Hybrid Phase Sliding Mode Control Scheme for Lower Extremity Exoskeleton

Abstract

Aiming at a pneumatic artificial muscle (PAM) lower extremity exoskeleton, a control mechanism based on hybrid phase sliding mode control (SMC) is proposed. First of all, the human gait cycle is mainly divided into the swing phase and stance phase, and the lower extremity exoskeleton phase models are established by the Euler–Lagrange method, respectively. Secondly, the lower limb exoskeleton is inevitably affected in the diverse working environment, and the exoskeleton model has nonlinear and strong coupling characteristics, which both increase the control difficulty. In this situations, a robust sliding mode control method is designed based on an Extended State Observer (ESO). Thirdly, the pneumatic muscle takes time to contract and relax, and then the joint input torque cannot jump when the gait phase changes, hence, the smoothing switching of the assistive control scheme is introduced to solve it. The smoothing switching time is detected by a phase detector, and the phase detector is designed by the plantar pressure information. Finally the comparative simulation shows that this control strategy has the advantages of fast time, high control precision and no jump during control torque switching. Pneumatic artificial muscle contraction rate curve shows that the pneumatic muscles’ motion range meets the control requirement of the exoskeleton.

Observer-based finite-time control for trajectory tracking of lower extremity exoskeleton

“…In this section,the effectiveness of the proposed control strategy are evaluated by MATLAB simulation results, where the tradition ESO-SMC 27 and ADRC 33 are utilized in comparison group. Besides, the lower extremity exoskeleton is utilized to describe the experimental validations of the control system.…”

“…Moreover, the detailed design methods of the controller and observer parameters are given. Compared with the existing works, 27,29 both the proposed ESO and sliding mode controller own finite-time convergence performance in this article. The estimated errors of ESO and the trajectory tracking error can converge to a small vicinity of zero in finite time.…”

“…Similar to the literature, 25 define the column vector g i : = ½sig(s i ) 1=2 , L i T , and the Lyapunov function (27) can be expressed as V 2i = g i T F i g i , where the positive definite matrix F i is given by…”

“…A few literatures investigate the trajectory tracking control of exoskeleton combined ESO and SMC. 27,28 However, there are a few works considering the finite-time performance of both ESO and SMC simultaneously for the exoskeleton.…”

The Design of a Smart Lower-Limb Prosthesis Supporting People with Transtibial Amputation—A Data Acquisition System

“…The first electrically powered ankle-foot prosthesis was built in 1998 and was pneumatically operated [10]. Subsequently, from 2005 until now, studies have focused on designing and developing autonomous energy supply systems [11][12][13][14][15][16][17][18]. The main factors considered for the design of prostheses are both the mechanical properties [19,20] and the length of the prosthesis [21].…”