scholarly journals Adaptive Control of Hysteretic Robotic arm in Operational Space

2016 ◽  
Author(s):  
Somasundar Kannan ◽  
Serket Quintanar-Guzman ◽  
Souad Bezzaoucha ◽  
Miguel A. Olivares-Mendez ◽  
Holger Voos
Keyword(s):  
Adaptive Control ◽  
Robotic Arm ◽  
Operational Space

scholarly journals A Tutorial on Robust Control, Adaptive Control and Robust Adaptive Control—Application to Robotic Manipulators

Inventions ◽  
2019 ◽  
Vol 4 (3) ◽  
pp. 49
Author(s):  
Bin Wei
Keyword(s):  
Adaptive Control ◽  
Robust Control ◽  
Robotic Manipulators ◽  
Joint Space ◽  
Robust Adaptive Control ◽  
Control Techniques ◽  
Operational Space ◽  
Advanced Control ◽  
Robust Adaptive ◽  
Control Application

A tutorial on robust control, adaptive control, robust adaptive control and adaptive control of robotic manipulators is presented in a systematic manner. Some limitations of the above methods are also illustrated. The relationships between the robust control, adaptive control and robust adaptive control are demonstrated. Basic information on the joint space control, operational space control and force control is also given. This tutorial summarizes the most advanced control techniques currently in use in a very simple manner, and applies to robotic manipulators, which can provide an informative guideline for students who have little knowledge of controls or who want to understand the adaptive control of robotics in a systematic way.

scholarly journals Operational space control of a lightweight robotic arm actuated by shape memory alloy wires: A comparative study

2017 ◽  
Vol 30 (9) ◽  
pp. 1368-1384 ◽  
Author(s):  
Serket Quintanar-Guzmán ◽  
Somasundar Kannan ◽  
Adriana Aguilera-González ◽  
Miguel A. Olivares-Mendez ◽  
Holger Voos
Keyword(s):  
Shape Memory Alloy ◽  
Shape Memory ◽  
Sliding Mode ◽  
Joint Space ◽  
Robotic Arm ◽  
Shape Memory Alloy Wire ◽  
Simulation Studies ◽  
End Effector ◽  
Alloy Wire ◽  
Operational Space

This article presents the design and control of a two-link lightweight robotic arm using shape memory alloy wires as actuators. Both a single-wire actuated system and an antagonistic configuration system are tested in open and closed loops. The mathematical model of the shape memory alloy wire, as well as the kinematics and dynamics of the robotic arm, are presented. The operational space control of the robotic arm is performed using a joint space control in the inner loop and closed-loop inverse kinematics in the outer loop. In order to choose the best joint space control approach, a comparative study of four different control approaches (proportional derivative, sliding mode, adaptive, and adaptive sliding mode control) is carried out for the proposed model. From this comparative analysis, the adaptive controller was chosen to perform operational space control. This control helps us to perform accurate positioning of the end-effector of shape memory alloy wire–based robotic arm. The complete operational space control was successfully tested through simulation studies performing position reference tracking in the end-effector space. Through simulation studies, the proposed control solution is successfully verified to control the hysteretic robotic arm.

Design of Adaptive Control System for Flexible Robotic ARM

1986 ◽  
Vol 19 (14) ◽  
pp. 273-276
Author(s):  
F. Ohkawa ◽  
T. Suehiro ◽  
K. Kurosu ◽  
T. Yamashita
Keyword(s):  
Adaptive Control ◽  
Control System ◽  
Robotic Arm ◽  
Adaptive Control System

Application of real-time machine learning to myoelectric prosthesis control: A case series in adaptive switching

2016 ◽  
Vol 40 (5) ◽  
pp. 573-581 ◽  
Author(s):  
Ann L Edwards ◽  
Michael R Dawson ◽  
Jacqueline S Hebert ◽  
Craig Sherstan ◽  
Richard S Sutton ◽  
...  
Keyword(s):  
Machine Learning ◽  
Adaptive Control ◽  
Cognitive Load ◽  
Real Time ◽  
Case Series ◽  
Robotic Arm ◽  
User Intent ◽  
Myoelectric Prostheses ◽  
Time Prediction ◽  
Case Series Study

Background: Myoelectric prostheses currently used by amputees can be difficult to control. Machine learning, and in particular learned predictions about user intent, could help to reduce the time and cognitive load required by amputees while operating their prosthetic device. Objectives: The goal of this study was to compare two switching-based methods of controlling a myoelectric arm: non-adaptive (or conventional) control and adaptive control (involving real-time prediction learning). Study design: Case series study. Methods: We compared non-adaptive and adaptive control in two different experiments. In the first, one amputee and one non-amputee subject controlled a robotic arm to perform a simple task; in the second, three able-bodied subjects controlled a robotic arm to perform a more complex task. For both tasks, we calculated the mean time and total number of switches between robotic arm functions over three trials. Results: Adaptive control significantly decreased the number of switches and total switching time for both tasks compared with the conventional control method. Conclusion: Real-time prediction learning was successfully used to improve the control interface of a myoelectric robotic arm during uninterrupted use by an amputee subject and able-bodied subjects. Clinical relevance Adaptive control using real-time prediction learning has the potential to help decrease both the time and the cognitive load required by amputees in real-world functional situations when using myoelectric prostheses.

scholarly journals Design, Analysis, and Comparison of Control Strategies for an Industrial Robotic Arm Driven by a Multi-Level Inverter

Symmetry ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 86
Author(s):  
Claudio Urrea ◽  
Daniel Jara
Keyword(s):  
Fuzzy Logic ◽  
Adaptive Control ◽  
Computer Simulations ◽  
Control Strategies ◽  
Gain Scheduling ◽  
Robotic Arm ◽  
Permanent Magnet Motor ◽  
Design And Implementation ◽  
Three Phase ◽  
Multi Level

In this article, we present the design and implementation of different control strategies for the position of a 2-Degree-of-Freedom (DoF) robotic arm, namely gain scheduling per trenches, gain scheduling by interpolation, adaptive control, and fuzzy logic. The first link of this robot is driven by an Alternating Current Brushless Permanent Magnet Motor (ACBPMM) through a three-phase multi-level inverter with 27 levels of voltage per phase. Thanks to the topologies offered by ACBPMMs and to the multi-level inverter, high commutation frequencies are reduced, as observed in the computer simulations. Additionally, to determine which proposed control strategies are the most suitable for an ACBPMM connected to a multi-level inverter, a comparative study on the performance of the controllers implemented for this robot is conducted.

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