Characterization of pneumatic muscles and their use for the position control of a mechatronic finger

Oliver-Salazar, Marco Antonio; Szwedowicz-Wasik, Dariusz; Ortega, Andrés Blanco; Acevedo, Francisco Aguilar; Ruiz-Gonzalez, Ruben

doi:10.1016/j.mechatronics.2016.12.006

Cited by 21 publications

(6 citation statements)

References 10 publications

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“…For instance, a pneumatic muscle for the position control of the finger is presented in ref. [12]. This system is embedded with Hall-effect sensors to measure the angular displacement of joints.…”

Section: Introductionmentioning

confidence: 99%

Design of sensing system for experimental modeling of soft actuator applied for finger rehabilitation

2021

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Safe interaction and inherent compliance with soft robots have motivated the evolution of soft rehabilitation robots. Among these, soft robotic gloves are known as an effective tool for stroke rehabilitation. This research proposed a pneumatically actuated soft robotic for index finger rehabilitation. The proposed system consists of a soft bending actuator and a sensing system equipped with four inertial measurement unit sensors to generate kinematic data of the index finger. The designed sensing system can estimate the range of motion (ROM) of the finger’s joints by combining angular velocity and acceleration values with the standard Kalman filter. The sensing system is evaluated regarding repeatability and reliability through static and dynamic experiments in the first step. The root mean square error attained in static and dynamic states are 2 $^\circ$ and 3 $^\circ$ , sequentially, representing an efficient function of the fusion algorithm. In the next step, experimental models have been developed to analyze and predict a soft actuator’s behavior in free and constrained states using the sensing system’s data. Thus, parametric system identification methods, artificial neural network—multilayer perceptron (ANN-MLP), and artificial neural network—radial basis function algorithms (ANN-RBF) have been compared to achieve an optimal model. The results reveal that ANN models, particularly RBF ones, can predict the actuator behavior with reasonable accuracy in the free and constrained state (<1 $^\circ$ ). Hence, the need for intricate analytical modeling and material characterization will be eliminated, and controlling the soft actuator will be more practical. Besides, it assesses the ROM and finger functionality.

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Section: Introductionmentioning

confidence: 99%

Design of sensing system for experimental modeling of soft actuator applied for finger rehabilitation

2021

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“…Since the middle of the last century, McKibben artificial muscles have attracted more attention. This very well-known muscle has been designed with the intent of applying it in medical environment [5], [6]. Since the PAMs have many advantages (low weight, acceptable stiffness, high flexibility, etc.…”

Section: Pneumatic Artificial Musclesmentioning

confidence: 99%

“…), the development of this type of drive continues to advance. [6] PAMs work on the principle of changing muscle length when changing the pressure in the muscle. If the muscle is compressed by compressed air, it will shorten from L0 to L, increasing its volume and muscle diameter from D0 to D [7].…”

Section: Pneumatic Artificial Musclesmentioning

confidence: 99%

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Comparison of Different Neural Networks Models for Identification of Manipulator Arm Driven by Fluidic Muscles

Trojanová¹,

Hosŏvský²

2018

ACTA POLYTECH HUNG

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The main subject of the study, which is summarized in this article, was to compare three different models of neural networks (Linear Neural Unit (LNU), Quadratic Neural Unit (QNU) and Multi-Layer Perceptron Network (MLP)) to identify of the real system of the manipulator arm. The arm is powered by FESTO fluidic muscles, which allows for two degrees of freedom. The data obtained by the measurements were processed in Python. This program served as a tool for compiling individual dynamic models, predicting measured data, and then graphically interpreting the resulting models. Levenberg-Marquardt (LM) was used as the learning algorithm because it is more suitable for learning neural networks than for example, the Gauss-Newton method.

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“…Nonlinear and hysteretic effects must not be a deterrent to the adoption of PAMs as compliant actuators for assistive and rehabilitation robots, since control engineers have developed nonlinear compensation control strategies that can achieve robust trajectory tracking control of rehabilitation exoskeletons [7] and prostheses [8]. The most efficient compensation strategies rely on gray-box models, such as the phenomenological model in [9] and the lumped-parameter model in [10].…”

Section: Introductionmentioning

confidence: 99%

A Multistate Friction Model for the Compensation of the Asymmetric Hysteresis in the Mechanical Response of Pneumatic Artificial Muscles

et al. 2019

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These days, biomimetic and compliant actuators have been made available to the main applications of rehabilitation and assistive robotics. In this context, the interaction control of soft robots, mechatronic surgical instruments and robotic prostheses can be improved through the adoption of pneumatic artificial muscles (PAMs), a class of compliant actuators that exhibit some similarities with the structure and function of biological muscles. Together with the advantage of implementing adaptive compliance control laws, the nonlinear and hysteretic force/length characteristics of PAMs pose some challenges in the design and implementation of tracking control strategies. This paper presents a parsimonious and accurate model of the asymmetric hysteresis observed in the force response of PAMs. The model has been validated through the experimental identification of the mechanical response of a small-sized PAM where the asymmetric effects of hysteresis are more evident. Both the experimental results and a comparison with other dynamic friction models show that the proposed model could be useful to implement efficient compensation strategies for the tracking control of soft robots.

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Characterization of pneumatic muscles and their use for the position control of a mechatronic finger

Cited by 21 publications

References 10 publications

Design of sensing system for experimental modeling of soft actuator applied for finger rehabilitation

Design of sensing system for experimental modeling of soft actuator applied for finger rehabilitation

Comparison of Different Neural Networks Models for Identification of Manipulator Arm Driven by Fluidic Muscles

A Multistate Friction Model for the Compensation of the Asymmetric Hysteresis in the Mechanical Response of Pneumatic Artificial Muscles

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