Modeling and Control of a Lower-Limb Rehabilitation Robot

Ma, Yanjiao; He, Wei; Ge, Shuzhi Sam

doi:10.1007/978-3-642-34103-8_59

Cited by 4 publications

(2 citation statements)

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“…A model-based control strategy requires mathematical modeling of the system; however, a model-free controller does not need an exact mathematical model, but a system function is approximated. The lower limb exoskeletons are modeled as a two link [6,7] or three link [8,9] manipulator. In most of the previous research, the Lagrange method is used for development of the mathematical model of system dynamics.…”

Section: Introductionmentioning

confidence: 99%

“…The results show that all three joints reach the desired set angles in simulation but in experimentation the hip and knee joints do not reach the final value. Similarly, a pneumatic muscle-based two-DOF ES is controlled by proportional-derivative (PD) and adaptive control strategies separately in [7]. The results show that the output is slightly out of phase for PD control but for adaptive control the tracking results show adaptation within 1.5 s of operation.…”

Section: Introductionmentioning

confidence: 99%

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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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