Fuzzy Control of a Robotic Finger Actuated by Shape Memory Alloy Wires

Silva, André F. C.; Silva, Simplicio A. da; Santos, Alexsandro José Virgínio dos; Ries, Andreas; Souto, Cícero da Rocha; Araújo, Carlos José de

doi:10.1115/1.4038652

Cited by 9 publications

(5 citation statements)

References 15 publications

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“…Consider the nonlinear system (8) with the discrete sliding mode function (18) meeting the requirements (26). If the sliding mode variable s(k) will finally reach a zone by the control law (27), µ(k) is constrained and the controlled system satisfies the prescribed performance.…”

Section: Stability Analysismentioning

confidence: 99%

“…However, the SMA-based robotic hand is a nonlinear system, and there is a large error between the system identification model and the actual system. In [27], a fuzzy logic control method is designed to realize the motion control of the SMA-based robotic hand. The visual camera is used to track the position of the finger joint instead of the joint angle sensor, which reduces the complexity of the system.…”

Section: Introductionmentioning

confidence: 99%

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Model-Free Tracking Control with Prescribed Performance for a Shape Memory Alloy-Based Robotic Hand

Hao¹,

Xiao²,

Li³

2021

Applied Sciences

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The shape memory alloy (SMA)-based robotic hand has been a new emerging technology with potential applications ranging from life service to surgical treatment, because of the characteristics of SMA, such as high power-to-weight ratio, small volume and low driving voltage. However, due to the complex dynamic model and nonlinear aspects of SMA, it is complicated to control an SMA-based robotic hand. This paper presents a novel model free adaptive control for the SMA-based robotic hand system. By applying the Taylor series expansion method and the differential mean value theorem, the SMA based robotic hand system can be transformed into an equivalent linearization model, which merely depends on measurement data without any information on the system. Combined with prescribed performance control, the novel control method can constrain the tracking error in a preassigned domain. Experiments are conducted on the SMA-based robotic hand system to verify the performance of the presented control method.

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Section: Stability Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Model-Free Tracking Control with Prescribed Performance for a Shape Memory Alloy-Based Robotic Hand

Hao¹,

Xiao²,

Li³

2021

Applied Sciences

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“…20,22 Fuzzy logic as a model-free control technique is applied to perform the angular positioning for SMA-based hands. 23,24 However, the deficiency of fuzzy logic systematization is a main limitation of fuzzy logic control. Therefore, designing a controller for such robotic hands, especially for highly integrated SMA-based hands, is a challenging problem.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement learning control of a humanoid robotic hand actuated by shape memory alloy

Liu

Zhao

Zhang

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part C:

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Humanoid robotic hand actuated by shape memory alloy (SMA) represents a new emerging technology. SMA has a wide range of potential applications in many different fields, ranging from industrial assembly to biomedicine applications, due to the characteristic of high power-to-weight ratio, low driving voltages and noiselessness. However, nonlinearities of SMA and complex dynamic models of SMA-based robotic hands result in difficulties in controlling. In this paper, a humanoid SMA-based robotic hand composed of five fingers is presented with the ability of adaptive grasping. Reinforcement learning as a model-free control strategy can search for optimal control of systems with nonlinear and uncertainty. Therefore, an adaptive SA-Q-Learning (ASA-Q-learning) controller is proposed to control the humanoid robotic finger. The performance of ASA-Q-learning controller is compared with SA-Q-learning and PID controller through experimentation. Results have shown that ASA-Q-learning controller can control the humanoid SMA-based robotic hand effectively with faster convergence rate and higher control precision than SA-Q-learning and PID controller, and is feasible for implementation in a model-free system.

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“…Silva et al [9], [10] designed a prototype robotic finger driven by 0.31 mm diameter SMA wires. The finger was manufactured by rapid prototyping, using ABS (Acrylonitrile Butadiene Styrene), with 3 degrees of freedom.…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy Logic Control System for a Robotic Hand Driven by Shape Memory Alloy Wires

Silva¹,

Oliveira²,

Ries³

et al. 2019

EJERS

Self Cite

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This paper presents a robotic hand using wires with shape memory (NiTi) as non-conventional actuators. The mechanical structure of the robot hand was first designed by means of a CAD computer program and afterwards 3D printed using ABS polymer. The robotic hand was designed according to the physiological characteristics of the human hand, with particular attention to the angles formed by the phalanges of the fingers. A mechanical system accommodates the thin NiTi wires compactly, thus forming an artificial muscle. A fuzzy logic based control system allows an accurate positioning of each phalanx. The contribution of the present work to science lies in the practical implementation of known techniques and materials.

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Fuzzy Control of a Robotic Finger Actuated by Shape Memory Alloy Wires

Cited by 9 publications

References 15 publications

Model-Free Tracking Control with Prescribed Performance for a Shape Memory Alloy-Based Robotic Hand

Model-Free Tracking Control with Prescribed Performance for a Shape Memory Alloy-Based Robotic Hand

Reinforcement learning control of a humanoid robotic hand actuated by shape memory alloy

A Fuzzy Logic Control System for a Robotic Hand Driven by Shape Memory Alloy Wires

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