Robust sliding mode adaptive control for lower extremity exoskeleton

Cao, Fucheng; Li, Chunfeng; Li, Yuanchun

doi:10.1109/cac.2015.7382533

Cited by 2 publications

(1 citation statement)

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“…q ∈ R n represents Coriolis and centrifugal force, G(q) ∈ R n is the gravitational force, and q ∈ R n denotes the angle. Equation (1) can be modified with addition of external disturbances 'd' (refers to the interference that becomes part of the system exogenously) at the system input [10] and model uncertainties '∆' (refers to difference between the actual system and the modeled system) [11,12]. The resulting modification is shown in Equation (2):…”

Section: Introductionmentioning

confidence: 99%

Design of Model-Based and Model-Free Robust Control Strategies for Lower Limb Rehabilitation Exoskeletons

et al. 2022

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Rehabilitation in the form of locomotion assistance and gait training through robotic exoskeletons requires both precision and accuracy to achieve effective results. The essential challenge is to ensure robust tracking of the reference signal, i.e., of the gait or locomotion. This paper presents the design of model-based (MB) and model-free (MF) robust control strategies to achieve desired performance and robustness in terms of transient behavior and steady-state/tracking error, implementable to the locomotion assistance and gait training by exoskeletons. The dynamic responses of the exoskeleton system were investigated with both the control strategies. The study was carried out with a variety of reference signals and performance was evaluated to identify the best suited approach for rehabilitation exoskeletons. In case of the model-based control, a mathematical model of the system was developed using a bond graph modeling technique and a lead compensated H-infinity reference gain controller was designed to ensure robust tracking performance. In the model-free control strategy, however, the system function is approximated using radial basis function neural networks (RBFNNs) and an adaptive proportional-derivative RBFNN controller was designed to achieve the desired results with minimum tracking error. Both strategies make the system robust and stable. However, the MF control strategy is faster for all reference inputs as compared to the MB control strategy i.e., faster to approach the peak value and settle, and rapidly approaches the zero steady-state/tracking error. The rise time in the case of a sinusoidal input for model-free control is 0.4 s faster than the rise time in model-based control. Similarly, the settling time is 3.9 s faster in the case of model-free control, which is a prominent difference and can provide better rehabilitation results.

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