Angle tracking adaptive backstepping control for a pneumatic artificial muscle antagonistic joint

Yu, Rui; Cai, Jianping; Wang, Binrui; Qian, Feng

doi:10.1109/cac48633.2019.8997497

Cited by 1 publication

(1 citation statement)

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“…However, there still exists significant drawbacks that are time-varying nonlinear dynamics and hysteresis characteristics [19,20] which may cause difficulties for applying traditional control algorithms such as Proportional Integral (PI), PID, linear control techniques or model-based controllers to manage the operation of PAM actuators. In recent years, the nonlinear control algorithms for PAM actuators have been proposed based on neural network [21,22], backstepping control [23]. As is well known, the sliding mode control technique is one of the power methods for controlling the nonlinear dynamic system, uncertainties and external disturbance system [24].…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Fast Terminal Sliding Mode Controller of Exercise-Assisted Robotic Arm for Elbow Joint Rehabilitation Featuring Pneumatic Artificial Muscle Actuator

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2020

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Due to the time-varying nonlinear dynamic, uncertain model and hysteresis characteristics of the pneumatic artificial muscle (PAM) actuator, it is not easy to apply model-based control algorithms for monitoring, as well as controlling, the operation of systems driven by PAM actuators. Hence, the main aim of this work is to propose an intelligent controller named adaptive sliding controller adding compensator (ASC + C) to operate a robotic arm, featuring a pneumatic artificial muscle actuator, which assists rehabilitation exercise of the elbow joint function. The structure of the proposed controller is a combination between the fuzzy logic technique and Proportional Integral Derivative (PID) algorithm. In which, the input of fuzzy logic controller is the sliding surface, meanwhile, its output is the estimated value of the unknown nonlinear function, meaning that the model-based requirement is released. A PID controller works as a compensator with online learning ability and is designed to compensate because of the approximate error and hysteresis characteristic. Additionally, to improve convergence and to obtain stability, a fast terminal sliding manifold is introduced and online learning laws for parameters of the controller are attainted through the stable criterion of Lyapunov. Finally, an experimental apparatus is also fabricated to evaluate control response of the system. The experimental result confirmed strongly the ability of the proposed controller, which indicates that the ASC + C can obtain a steady state tracking error less than 5 degrees and a position response without overshoot.

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