Intelligent active force control of a three-link manipulator using fuzzy logic

The paper presents a novel approach to estimate the inertia matrix of a robot arm using a fuzzy logic (FL) mechanism in order to trigger the active force control (AFC) strategy. A comprehensive study is performed on a rigid three-link manipulator subjected to a number of external disturbances. The robustness and effectiveness of the proposed control scheme are investigated considering the trajectory track performance of the robotic arm taking into account the application of external disturbances and that the arm is commanded to describe a reference trajectory given a number of initial and operating conditions. The results show that the FL mechanism used in the study successfully computes appropriate estimated inertia matrix value to execute the control action. The proposed scheme exhibits a high degree of robustness and accuracy as the track error is bounded within an acceptable range of value even under the influence of the introduced disturbances

Disturbance Observer-based Robust Control and Its Applications: 35th Anniversary Overview

Disturbance Observer has been one of the most widely used robust control tools since it was proposed in 1983. This paper introduces the origins of Disturbance Observer and presents a survey of the major results on Disturbance Observer-based robust control in the last thirty-five years. Furthermore, it explains the analysis and synthesis techniques of Disturbance Observer-based robust control for linear and nonlinear systems by using a unified framework. In the last section, this paper presents concluding remarks on Disturbance Observer-based robust control and its engineering applications.Comment: 12 pages, 4 figure

A Framework of Hybrid Force/Motion Skills Learning for Robots

Human factors and human-centred design philosophy are highly desired in today’s robotics applications such as human-robot interaction (HRI). Several studies showed that endowing robots of human-like interaction skills can not only make them more likeable but also improve their performance. In particular, skill transfer by imitation learning can increase usability and acceptability of robots by the users without computer programming skills. In fact, besides positional information, muscle stiffness of the human arm, contact force with the environment also play important roles in understanding and generating human-like manipulation behaviours for robots, e.g., in physical HRI and tele-operation. To this end, we present a novel robot learning framework based on Dynamic Movement Primitives (DMPs), taking into consideration both the positional and the contact force profiles for human-robot skills transferring. Distinguished from the conventional method involving only the motion information, the proposed framework combines two sets of DMPs, which are built to model the motion trajectory and the force variation of the robot manipulator, respectively. Thus, a hybrid force/motion control approach is taken to ensure the accurate tracking and reproduction of the desired positional and force motor skills. Meanwhile, in order to simplify the control system, a momentum-based force observer is applied to estimate the contact force instead of employing force sensors. To deploy the learned motion-force robot manipulation skills to a broader variety of tasks, the generalization of these DMP models in actual situations is also considered. Comparative experiments have been conducted using a Baxter Robot to verify the effectiveness of the proposed learning framework on real-world scenarios like cleaning a table

Industrial Robotics

This book covers a wide range of topics relating to advanced industrial robotics, sensors and automation technologies. Although being highly technical and complex in nature, the papers presented in this book represent some of the latest cutting edge technologies and advancements in industrial robotics technology. This book covers topics such as networking, properties of manipulators, forward and inverse robot arm kinematics, motion path-planning, machine vision and many other practical topics too numerous to list here. The authors and editor of this book wish to inspire people, especially young ones, to get involved with robotic and mechatronic engineering technology and to develop new and exciting practical applications, perhaps using the ideas and concepts presented herein

A brief review of neural networks based learning and control and their applications for robots

As an imitation of the biological nervous systems, neural networks (NN), which are characterized with powerful learning ability, have been employed in a wide range of applications, such as control of complex nonlinear systems, optimization, system identification and patterns recognition etc. This article aims to bring a brief review of the state-of-art NN for the complex nonlinear systems. Recent progresses of NNs in both theoretical developments and practical applications are investigated and surveyed. Specifically, NN based robot learning and control applications were further reviewed, including NN based robot manipulator control, NN based human robot interaction and NN based behavior recognition and generation

STUDY OF FRICTION COMPENSATION MODEL FOR MOBILE ROBOT’S JOINTS

Frictional forces inside the joints of mobile robots hurt robot operation's stability and positioning accuracy. Therefore, establishing a suitable friction force compensation model has been a hot research topic in robotics. To explore the robot joint friction compensation model, three friction compensation models: linear, nonlinear, and neural network models, are developed in this paper. Based on the deep learning algorithm for three models at low speed, high speed, acceleration, and uniform speed training test, respectively results have been obtained. The test results show that the best friction compensation effect comes from combining neural network models in acceleration and a consistent speed state way. The friction compensation model trained this way yielded superior results to the other combinations tested. Finally, using the method, a friction compensation model trained by adding a neural network to the feedforward control torque was tested on a four-wheeled mobile robot platform. The test results show that the relative error of the torque caused by the friction of each joint is reduced by 15%-75% in 8 groups of tests, which indicates that our friction compensation method has a positive effect on improving the accuracy of the joint torque

Open-loop position control in collaborative, modular Variable-Stiffness-Link (VSL) robots

— Collaborative robots (cobots) open up new avenues in the fields of industrial robotics and physical Human-Robot Interaction (pHRI) as they are suitable to work in close approximation and in collaboration with humans. The integration and control of variable stiffness elements allow inherently safe interaction. Apart from notable work on Variable Stiffness Actuators, the concept of Variable-Stiffness-Link (VSL) manipulators promises safety improvements in cases of unintentional physical collisions. However, position control of these type of robotic manipulators is challenging for critical task-oriented motions (e.g., pick and place). Hence, the study of open-loop position control for VSL robots is crucial to achieve high levels of safety, accuracy and hardware cost-efficiency in pHRI applications. In this paper, we propose a hybrid, learning based kinematic modelling approach to improve the performance of traditional open-loop position controllers for a modular, collaborative VSL robot. We show that our approach improves the performance of traditional open-loop position controllers for robots with VSL and compensates for position errors, in particular, for lower stiffness values inside the links: Using our upgraded and modular robot, two experiments have been carried out to evaluate the behaviour of the robot during taskoriented motions. Results show that traditional model-based kinematics are not able to accurately control the position of the end-effector: the position error increases with higher loads and lower pressures inside the VSLs. On the other hand, we demonstrate that, using our approach, the VSL robot can outperform the position control compared to a robotic manipulator with 3D printed rigid links