Disturbance-Estimated Adaptive Backstepping Sliding Mode Control of a Pneumatic Muscles-Driven Ankle Rehabilitation Robot

Abstract

A rehabilitation robot plays an important role in relieving the therapists’ burden and helping patients with ankle injuries to perform more accurate and effective rehabilitation training. However, a majority of current ankle rehabilitation robots are rigid and have drawbacks in terms of complex structure, poor flexibility and lack of safety. Taking advantages of pneumatic muscles’ good flexibility and light weight, we developed a novel two degrees of freedom (2-DOF) parallel compliant ankle rehabilitation robot actuated by pneumatic muscles (PMs). To solve the PM’s nonlinear characteristics during operation and to tackle the human-robot uncertainties in rehabilitation, an adaptive backstepping sliding mode control (ABS-SMC) method is proposed in this paper. The human-robot external disturbance can be estimated by an observer, who is then used to adjust the robot output to accommodate external changes. The system stability is guaranteed by the Lyapunov stability theorem. Experimental results on the compliant ankle rehabilitation robot show that the proposed ABS-SMC is able to estimate the external disturbance online and adjust the control output in real time during operation, resulting in a higher trajectory tracking accuracy and better response performance especially in dynamic conditions.

Backstepping Sliding Mode Robust Control for a Wire-Driven Parallel Robot Based on a Nonlinear Disturbance Observer

“…It is a regression design method combining the selection of Lyapunov functions with the design of the controller. e backstepping method has been applied extensively because of the unique superiority in dealing with the nonlinear control problem [18][19][20][21][22][23]. It begins with the lowest order differential equations of the system, introduces the concept of virtual control, and designs the virtual control to meet the requirements step by step.…”

High Precision Adaptive Robust Neural Network Control of a Servo Pneumatic System

“…Therefore, the model-based control strategy is considered for pneumatic systems [9]. The sliding mode control [10][11][12] method is introduced to pneumatic systems for strong robustness; however, it has limited capacity to compensate for nonlinear dynamics. In addition, the backstepping [13] technique is used for controller design and stability analysis of the pneumatic systems.…”

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“…Structurally, PAM resembles a human muscle in power, size, and weight. This is an advantage for humanoid robots, soft robotics, prosthetic and orthotic devices, as well as rehabilitation or training exoskeleton systems (Liu et al, 2015;Ai et al, 2018) in which heavy actuators can significantly increase the payload.…”