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

2021 ◽  
Vol 11 (19) ◽  
pp. 9040
Author(s):  
Lina Hao ◽  
Jichun Xiao ◽  
Wenlong Li
Keyword(s):  
Shape Memory Alloy ◽  
Shape Memory ◽  
Control Method ◽  
Measurement Data ◽  
Mean Value Theorem ◽  
Equivalent Linearization ◽  
Robotic Hand ◽  
Driving Voltage ◽  
Prescribed Performance ◽  
Model Free

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.

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

2021 ◽  
pp. 095440622098201
Author(s):  
Mingfang Liu ◽  
Zhirui Zhao ◽  
Wei Zhang ◽  
Lina Hao
Keyword(s):  
Reinforcement Learning ◽  
Shape Memory Alloy ◽  
Shape Memory ◽  
Pid Controller ◽  
Weight Ratio ◽  
Robotic Hand ◽  
Free System ◽  
Q Learning ◽  
Model Free ◽  
Wide Range

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.

scholarly journals Disturbance compensation-based output feedback adaptive control for shape memory alloy actuator system

2021 ◽  
Vol 18 (1) ◽  
pp. 172988142199399
Author(s):  
Xiaoguang Li ◽  
Bi Zhang ◽  
Daohui Zhang ◽  
Xingang Zhao ◽  
Jianda Han
Keyword(s):  
Adaptive Control ◽  
Shape Memory Alloy ◽  
Shape Memory ◽  
Control Method ◽  
Control Law ◽  
Reference Trajectory ◽  
Disturbance Compensation ◽  
Comparison Results ◽  
Control Scheme ◽  
Actuator System

Shape memory alloy (SMA) has been utilized as the material of smart actuators due to the miniaturization and lightweight. However, the nonlinearity and hysteresis of SMA material seriously affect the precise control. In this article, a novel disturbance compensation-based adaptive control scheme is developed to improve the control performance of SMA actuator system. Firstly, the nominal model is constructed based on the physical process. Next, an estimator is developed to online update not only the unmeasured system states but also the total disturbance. Then, the novel adaptive controller, which is composed of the nominal control law and the compensation control law, is designed. Finally, the proposed scheme is evaluated in the SMA experimental setup. The comparison results have demonstrated that the proposed control method can track reference trajectory accurately, reject load variations and stochastic disturbances timely, and exhibit satisfactory robust stability. The proposed control scheme is system independent and has some potential in other types of SMA-actuated systems.

Development of a Shape Memory Alloy (SMA) Based Assistive Hand

2015 ◽  
Vol 1115 ◽  
pp. 454-457 ◽  
Author(s):  
Alala M. Ba Hamid ◽  
Mohatashem R. Makhdoomi ◽  
Tanveer Saleh ◽  
Moinul Bhuiyan
Keyword(s):  
Shape Memory Alloy ◽  
Shape Memory ◽  
Sensory System ◽  
Open Loop ◽  
Rehabilitation Robotics ◽  
Robotic Hand ◽  
After Effect ◽  
Conventional Structure ◽  
Force Mechanism ◽  
Gripping Force

In Malaysia, every year approximately 40000 people suffer from stroke and many of them become immobilized as an after effect. Rehabilitation robotics to assist disabled people has drawn significant attention by the researchers recently. This project also aims to contribute to this field. This paper presents a Shape Memory Alloy (SMA) actuated wearable assistive robotic hand for grasping. The proposed design is compact and sufficiently light to be used as an assistive hand. It is a joint less structure, has the potential because the human skeleton and joint replace the robot’s conventional structure. This design has been implemented on index and thumb fingers to enable grasping. Shape memory alloy springs and bias force mechanism are used for purpose of hand’s flexion and extension. This paper describes the mechatronic design of the wearable hand, experimental study of actuation unit and sensory system. Open loop experiments are conducted to understand the hand characterization and grip force provided by index finger. Current, temperature, extension and contraction of shape memory alloy springs are reported. This mechanism requires approximately 2A current for the SMA to actuate which provides maximum of 1.6N of gripping force. Conducted experiments show promising results that encourage further developments.

Design and fabrication of a Robotic Hand using shape memory alloy actuators

2015 ◽  
Author(s):  
Ali Ahmadi ◽  
Mohammad Mahdavian ◽  
Nafiseh Faridi Rad ◽  
Aghil Yousefi-Koma ◽  
Fatemeh Alidoost ◽  
...  
Keyword(s):  
Shape Memory Alloy ◽  
Shape Memory ◽  
Robotic Hand

Model Reference Adaptive Control Based on KP Model for Magnetically Controlled Shape Memory Alloy Actuators

2017 ◽  
Vol 15 (1_suppl) ◽  
pp. 31-37 ◽  
Author(s):  
Miaolei Zhou ◽  
Yannan Zhang ◽  
Kun Ji ◽  
Dong Zhu
Keyword(s):  
Adaptive Control ◽  
Shape Memory Alloy ◽  
Shape Memory ◽  
Control Method ◽  
Model Reference Adaptive Control ◽  
Positioning Accuracy ◽  
Model Reference ◽  
Hysteresis Nonlinearity ◽  
Adopted Model ◽  
Model Reference Adaptive

Introduction Magnetically controlled shape memory alloy (MSMA) actuators take advantages of their large deformation and high controllability. However, the intricate hysteresis nonlinearity often results in low positioning accuracy and slow actuator response. Methods In this paper, a modified Krasnosel'skii-Pokrovskii model was adopted to describe the complicated hysteresis phenomenon in the MSMA actuators. Adaptive recursive algorithm was employed to identify the density parameters of the adopted model. Subsequently, to further eliminate the hysteresis nonlinearity and improve the positioning accuracy, the model reference adaptive control method was proposed to optimize the model and inverse model compensation. Results The simulation experiments show that the model reference adaptive control adopted in the paper significantly improves the control precision of the actuators, with a maximum tracking error of 0.0072 mm. Conclusions The results prove that the model reference adaptive control method is efficient to eliminate hysteresis nonlinearity and achieves a higher positioning accuracy of the MSMA actuators.

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