Gain-scheduling H/sup ∞/ control of a pneumatic muscle using wireless MEMS sensors

Djouadi, Seddik M.; Repperger, D.W.; Berlin, J.

doi:10.1109/mwscas.2001.986292

Cited by 4 publications

(2 citation statements)

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“…To deal with the complex non-linear dynamics of the PMA, researchers have attempted non-linear control methods such as adaptive pole-placement control, H1 control, fuzzy backstepping, variable structure control (VSC), neural network control etc. [16][17][18][19][20][21][22][23][24][25]. Caldwell studied the controller design and application of the PM control systems.…”

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confidence: 99%

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Tracking control of pneumatic artificial muscle actuators based on sliding mode and non-linear disturbance observer

Xing

Huang

Wang

et al. 2010

IET Control Theory Appl.

110

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The dynamic properties and non-linear control of the pneumatic muscle actuator (PMA) were investigated in this study for use in a specially designed hand rehabilitation device. The phenomenological model of PMA was established in the lower pressure range applicable for hand rehabilitation. The experimental results show that PMA's characteristics can be approximated by piecewise functions. In order to improve the performance and robustness of control for accurate trajectory tracking, a sliding mode control based on non-linear disturbance observer (SMCBNDO) was designed. The simulation and experimental results demonstrated that the model and the sliding mode control achieved the desired performance in tracking a desired trajectory within guaranteed accuracy. The work indicates that the model and the non-linear control proposed in this study can be applied in PMA-driven hand function rehabilitation devices requiring lower pressures.

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confidence: 99%

“…Repperger et al designed a gain scheduling method based on the PM model approximated from experiment data [13]. They also investigated gain-scheduling H1 control [18], adaptive backstepping [19], fuzzy backstepping [20] and VSC [21]. Among these control methods, satisfactory performance can be achieved by using adaptive backstepping or VSC methods.…”

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confidence: 99%