Data Driven Calibration and Control of Compact Lightweight Series Elastic Actuators for Robotic Exoskeleton Gloves

This paper describes data-driven modelling methods and their use for the control of a novel set of series-elastic actuators (SEAs). A set of elastic actuators was developed in order to fulfill the end-user needs for tailored industrial collaborative robot manipulators of different morphologies and payloads. Three different types of elastic actuation were investigated, namely, disc springs, coil springs and torsion bars. The developed algorithms were validated both on single actuators and on a 6-DOF robotic arm composed of such actuators.

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Design and Control Considerations for High-Performance Series Elastic Actuators

IEEE/ASME Transactions on Mechatronics ◽

10.1109/tmech.2013.2270435 ◽

2014 ◽

Vol 19 (3) ◽

pp. 1080-1091 ◽

Cited By ~ 197

Author(s):

Nicholas Paine ◽

Sehoon Oh ◽

Luis Sentis

Keyword(s):

High Performance ◽

Series Elastic Actuators ◽

And Control ◽

Series Elastic

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Modeling and Control of Nonlinear Series Elastic Actuator

Volume 4: ASME/IEEE International Conference on Mechatronic and Embedded Systems and Applications and the 19th Reliability, Stress Analysis, and Failure Prevention Conference ◽

10.1115/detc2007-34997 ◽

2007 ◽

Cited By ~ 3

Author(s):

Ehsan Basafa ◽

Hassan Salarieh ◽

Aria Alasty

Keyword(s):

Nonlinear Models ◽

Human Gait ◽

Output Impedance ◽

Modeling And Control ◽

Series Elastic Actuator ◽

Scheduling Method ◽

Normal Human ◽

Series Elastic Actuators ◽

And Control ◽

Series Elastic

Series Elastic Actuators are force actuators with applications in robotics and biomechanics. In linear Series Elastic Actuators, a large force bandwidth requires a stiff sensor (spring), but the output impedance puts an upper limit on this parameter, therefore selecting the proper spring is difficult in these actuators. In this paper, Series Elastic Actuator is modeled with a nonlinear, stiffening spring and controlled using the Gain Scheduling method. Simulations show that both linear and nonlinear models have similar force bandwidths, but the nonlinear one shows much lower output impedance. Hence, the choice of spring for actuator design is an easier task than that of the linear model. Also, as a force-augmenting device for the knee joint in normal human gait, the nonlinear model acts better in simulations.

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Modeling and Control of Four-bar Mechanism with Series Elastic Actuators

10.48011/asba.v2i1.1396 ◽

2020 ◽

Author(s):

Felipe R. Lopes ◽

Marco A. Meggiolaro

Keyword(s):

Human Interaction ◽

Robotic Manipulation ◽

Modeling And Control ◽

Lqr Control ◽

In Series ◽

Flexible Behavior ◽

Series Elastic Actuators ◽

New Generation ◽

And Control ◽

Series Elastic

A new generation of robots that work in cooperation with humans (called collaborative robots) needs some flexibility to adapt to the environment and activities with people. That is why the Series Elastic Actuator (SEA) has been a breakthrough in actuator technologies. The idea of inserting an elastic element in series with a motor allows a lower output impedance, consequently a flexible behavior in the manipulator, in addition to providing torque feedback to better compensate disturbances caused e.g. by friction losses. This article presents a four-bar mechanism with SEA for the purpose of robotic manipulation. Its kinematics and dynamicsare studied, as well as its regulation and trajectory control. The behavior of the decoupled four-bar mechanism and the characteristics of the SEA are also analyzed. Then the regulation control of the complete system is carried out using LQR control. Finally, a circular trajectory is controlled in a simulation to validate the proposed control strategy. The simulation results show the effectiveness of the proposed controller for the mechanism in the presence of SEAs estimating torque and providing the desired compliance for human interaction.

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Multistable series elastic actuators: Design and control

Robotics and Autonomous Systems ◽

10.1016/j.robot.2019.04.014 ◽

2019 ◽

Vol 118 ◽

pp. 167-178

Author(s):

Leonardo Cappello ◽

Michele Xiloyannis ◽

Binh Khanh Dinh ◽

Alberto Pirrera ◽

Filippo Mattioni ◽

...

Keyword(s):

Series Elastic Actuators ◽

And Control ◽

Series Elastic

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Data-Driven Stability Assessment of Multilayer Long Short-Term Memory Networks

Applied Sciences ◽

10.3390/app11041829 ◽

2021 ◽

Vol 11 (4) ◽

pp. 1829

Author(s):

Davide Grande ◽

Catherine A. Harris ◽

Giles Thomas ◽

Enrico Anderlini

Keyword(s):

Neural Network ◽

Network Architecture ◽

Short Term Memory ◽

Moving Average ◽

Machine Learning Algorithms ◽

Physical Models ◽

Data Driven ◽

The Stability ◽

And Control ◽

Nonlinear Autoregressive

Recurrent Neural Networks (RNNs) are increasingly being used for model identification, forecasting and control. When identifying physical models with unknown mathematical knowledge of the system, Nonlinear AutoRegressive models with eXogenous inputs (NARX) or Nonlinear AutoRegressive Moving-Average models with eXogenous inputs (NARMAX) methods are typically used. In the context of data-driven control, machine learning algorithms are proven to have comparable performances to advanced control techniques, but lack the properties of the traditional stability theory. This paper illustrates a method to prove a posteriori the stability of a generic neural network, showing its application to the state-of-the-art RNN architecture. The presented method relies on identifying the poles associated with the network designed starting from the input/output data. Providing a framework to guarantee the stability of any neural network architecture combined with the generalisability properties and applicability to different fields can significantly broaden their use in dynamic systems modelling and control.

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