Neural Network Control of a Rehabilitation Robot by State and Output Feedback

He, Wei; Ge, Shuzhi Sam; Li, Yanan; Chew, Effie; Ng, Yee Sien

doi:10.1007/s10846-014-0150-6

Cited by 226 publications

(110 citation statements)

References 40 publications

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“…[41][42][43][44] In this paper, we will use a three-layer MIMO neural network, which can be seen in Fig. 1.…”

Section: B Description Of the Neural Networkmentioning

confidence: 99%

Prescribed performance synchronization controller design of fractional-order chaotic systems: An adaptive neural network control approach

Jiao

2017

AIP Advances

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In this study, an adaptive neural network synchronization (NNS) approach, capable of guaranteeing prescribed performance (PP), is designed for non-identical fractional-order chaotic systems (FOCSs). For PP synchronization, we mean that the synchronization error converges to an arbitrary small region of the origin with convergence rate greater than some function given in advance. Neural networks are utilized to estimate unknown nonlinear functions in the closed-loop system. Based on the integer-order Lyapunov stability theorem, a fractional-order adaptive NNS controller is designed, and the PP can be guaranteed. Finally, simulation results are presented to confirm our results.

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“…[41][42][43][44] In this paper, we will use a three-layer MIMO neural network, which can be seen in Fig. 1.…”

Section: B Description Of the Neural Networkmentioning

confidence: 99%

Prescribed performance synchronization controller design of fractional-order chaotic systems: An adaptive neural network control approach

Jiao

2017

AIP Advances

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“…9,[46][47][48][49][50][51][52][53] In this paper, we will use a three-layer multiple-input multiple-output (MIMO) neural network, which can be seen in Figure 1.…”

Section: B Description Of the Neural Networkmentioning

confidence: 99%

Adaptive neural network backstepping control for a class of uncertain fractional-order chaotic systems with unknown backlash-like hysteresis

Wu¹,

2016

AIP Advances

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In this paper, we consider the control problem of a class of uncertain fractionalorder chaotic systems preceded by unknown backlash-like hysteresis nonlinearities based on backstepping control algorithm. We model the hysteresis by using a differential equation. Based on the fractional Lyapunov stability criterion and the backstepping algorithm procedures, an adaptive neural network controller is driven. No knowledge of the upper bound of the disturbance and system uncertainty is required in our controller, and the asymptotical convergence of the tracking error can be guaranteed. Finally, we give two simulation examples to confirm our theoretical results. C 2016 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license

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“…In [18], the uncertain human limb's dynamics is taken into account in addition to the robot uncertainties for designing an adaptive learning controller for a rehabilitation exoskeleton. A neural network-based controller [19] using both full-state and output feedbacks has been suggested to approximate the unknown exoskeleton model and improve the system robustness by compensation of the dynamic uncertainties. A model-free adaptive sliding mode scheme has been proposed [20] based on the input/output measurement data of the controlled plant without using the system model in the controller's structure.…”

Section: Introductionmentioning

confidence: 99%

“…According to the above-mentioned works, different model-free control methods [15][16][17][18][19][20] have been suggested without taking the dynamics of the exoskeleton robot into account. On the other hand, some model-based controllers [13,14] have been presented for exoskeleton rehabilitation robots considering the nonlinear system's dynamics and the physical interaction between the robot and human.…”

Section: Introductionmentioning

confidence: 99%

Robust Adaptive Sliding Mode Admittance Control of Exoskeleton Rehabilitation Robots

2017

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A nonlinear robust adaptive sliding mode admittance controller is proposed for exoskeleton rehabilitation robots. The proposed controller has robustness against uncertainties of dynamic parameters using an adaptation law. Furthermore, an adaptive Sliding Mode Control (SMC) scheme is employed in the control law to provide robustness against disturbances (non-parametric uncertainties) with unknown bounds.For this purpose, another adaptation law is defined for the variation of the SMC gain.The proposed scheme is augmented with an admittance control method to provide compliance for the patient during interaction with the rehabilitation robot. The stability of the proposed controller and the tracking performance of the system are proven using the Lyapunov stability theorem. To verify the effectiveness of the proposed control method, some simulations are conducted for a nonlinear lower-limb exoskeleton robot interacting with a patient leg via some braces. Based on the obtained results, the controller is able to provide a flexibility for the patient and appropriately respond to his/her non-compliant interaction torques. Moreover, the proposed controller significantly reduces the chattering of the input torques in comparison with a previous adaptive control method with a constant SMC gain, while it maintains a similar tracking performance.

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Neural Network Control of a Rehabilitation Robot by State and Output Feedback

Cited by 226 publications

References 40 publications

Prescribed performance synchronization controller design of fractional-order chaotic systems: An adaptive neural network control approach

Prescribed performance synchronization controller design of fractional-order chaotic systems: An adaptive neural network control approach

Adaptive neural network backstepping control for a class of uncertain fractional-order chaotic systems with unknown backlash-like hysteresis

Robust Adaptive Sliding Mode Admittance Control of Exoskeleton Rehabilitation Robots

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