Sensorless Force Control with Observer for Multi-functional Upper Limb Rehabilitation Robot

Choi, Jung Hyun; Oh, Sehoon; An, Jinung

doi:10.7746/jkros.2017.12.3.356

Cited by 3 publications

(8 citation statements)

References 19 publications

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“…Therefore, we should obtain a vector χ that contains the base inertial parameters. Thus, the dynamic model ( 1) can be written such that the parameters enter in a linear fashion as τ = W(q, q, q)χ (10) where W(q, q, q) ∈ R N×p is the regressor matrix. It is important to note that it will be denoted as W in the rest of this report to simplify the notation.…”

Section: Transforming the Modelmentioning

confidence: 99%

“…Next, we will present the adjustment for the mathematical model of the LLRR (1) to the form (10) indicated by the formulation reported by Riani et al [36]. Therefore, we assumed a set of parameters χ i as…”

Section: Transforming the Modelmentioning

confidence: 99%

“…This linear parametrization of robot dynamics is not unique in the chosen set of dynamic coefficients χ and the associated W. The W is a 3 × 9 matrix, where n = 3 is the number of DOF of the LLRR and, in this case, p = 9 is the number of the base inertial parameters. Therefore, the dynamic model ( 1) can be written as (10) where W(q, q, q) is the regressor matrix, and its notation may be simplified as See Appendix D for a detailed description of the elements of (16). In this equation, we can note that the regressor matrix has a block upper triangular structure as reported by Siciliano et al [30].…”

Section: Transforming the Modelmentioning

confidence: 99%

“…This calibration phase reduces BSIPs uncertainties based on an online parameter estimation method. Taking into account the model presented in (10), based in the regressor matrix W(q, q, q) and the vector χ, we used the observer reported by Riani et al [36] to perform an online parameter estimation. This method is expressed as…”

Section: Calibration Phase Designmentioning

confidence: 99%

“…A lower limb rehabilitation robot (LLRR) may be used to conduct isotonic exercises for victims of APM, IED, and UXO. According to Hyunchoi et al [10], an isotonic exercise is a type of resisted mode. A resistive method is an exercise where the patient is asked to overcome a given force during the movements imposed by the LLRR.…”

Section: Introductionmentioning

confidence: 99%

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Human-Robot Interaction Torque Estimation Methods for a Lower Limb Rehabilitation Robotic System with Uncertainties

et al. 2022

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Lower limb rehabilitation robot (LLRR) users, to successfully conduct isotonic exercises, require real-time feedback on the torque they exert on the robot to meet the goal of the treatment. Still, direct torque measuring is expensive, and indirect encoder-based estimation strategies, such as inverse dynamics (ID) and Nonlinear Disturbance Observers (NDO), are sensitive to Body Segment Inertial Parameters (BSIPs) uncertainties. We envision a way to minimize such parametric uncertainties. This paper proposes two human–robot interaction torque estimation methods: the Identified ID-based method (IID) and the Identified NDO-based method (INDO). Evaluating in simulation the proposal to apply, in each rehabilitation session, a sequential two-phase method: (1) An initial calibration phase will use an online parameter estimation to reduce sensitivity to BSIPs uncertainties. (2) The torque estimation phase uses the estimated parameters to obtain a better result. We conducted simulations under signal-to-noise ratio (SNR) = 40 dB and 20% BSIPs uncertainties. In addition, we compared the effectiveness with two of the best methods reported in the literature via simulation. Both proposed methods obtained the best Coefficient of Correlation, Mean Absolute Error, and Root Mean Squared Error compared to the benchmarks. Moreover, the IID and INDO fulfilled more than 72.2% and 88.9% of the requirements, respectively. In contrast, both methods reported in the literature only accomplish 27.8% and 33.3% of the requirements when using simulations under noise and BSIPs uncertainties. Therefore, this paper extends two methods reported in the literature and copes with BSIPs uncertainties without using additional sensors.

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Section: Transforming the Modelmentioning

confidence: 99%

Section: Transforming the Modelmentioning

confidence: 99%

Section: Transforming the Modelmentioning

confidence: 99%

Section: Calibration Phase Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Human-Robot Interaction Torque Estimation Methods for a Lower Limb Rehabilitation Robotic System with Uncertainties

et al. 2022

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High Precision Hybrid Torque Control for 4-DOF Redundant Parallel Robots under Variable Load

Liu

Kang

et al. 2023

Actuators

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As regards the impact and chattering of 4-DOF redundant parallel robots that occur under high-speed variable load operating conditions, this study proposed a novel control algorithm based on torque feedforward and fuzzy computational torque feedback hybrid control, which considered both the joint friction torque and the disturbance torque caused by the variable load. First of all, a modified dynamic model under variable load was established as follows: converting terminal load change to terminal centroid coordinate change, then mapping to the calculation of terminal energy, and lastly, establishing a dynamic model for each branch chain under variable load based on the Lagrange equation. Subsequently, torque feedforward was used to compensate for the friction torque and the disturbance torque caused by the variable load. Feedforward torques include friction torque and nonlinear disturbance torque under variable load. The friction torque is obtained by parameter identification based on the Stribeck friction model, while the nonlinear disturbance torque is obtained by real-time calculation based on the modified dynamic model under variable load. Finally, dynamic control of the robot under variable load was realized in combination with the fuzzy computational torque feedback control. The experimental and simulation results show that the motion accuracy of the fuzzy calculation torque feedback and torque feedforward control of the three drive joints of the robot under variable loads is 49.87%, 70.48%, and 50.37% lower than that of the fuzzy calculation torque feedback. Compared with pure torque feedback control, the speed stability of the three driving joints under fuzzy calculation torque feedback and torque feedforward control is 23.35%, 17.66%, and 25.04% higher, respectively.

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Improved Wearability of the Upper Limb Rehabilitation Robot NREX with respect to Shoulder Motion

Song¹,

Lee²,

Song³

2019

J. Korea Robot. Soc.

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Sensorless Force Control with Observer for Multi-functional Upper Limb Rehabilitation Robot

Cited by 3 publications

References 19 publications

Human-Robot Interaction Torque Estimation Methods for a Lower Limb Rehabilitation Robotic System with Uncertainties

Human-Robot Interaction Torque Estimation Methods for a Lower Limb Rehabilitation Robotic System with Uncertainties

High Precision Hybrid Torque Control for 4-DOF Redundant Parallel Robots under Variable Load

Improved Wearability of the Upper Limb Rehabilitation Robot NREX with respect to Shoulder Motion

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