Designing a Decentralized LQ Controller for an Industrial Robot Manipulator Based on Optimization Techniques

2006 ◽  
Author(s):  
D. Yazdani ◽  
S.M. Azizi ◽  
A. Bakhshai
Keyword(s):  
Robot Manipulator ◽  
Industrial Robot ◽  
Optimization Techniques

Optimization in the Design and Control of Robotic Manipulators: A Survey

1989 ◽  
Vol 42 (4) ◽  
pp. 117-128 ◽  
Author(s):  
S. S. Rao ◽  
P. K. Bhatti
Keyword(s):  
Optimal Control ◽  
Robot Manipulator ◽  
Research Effort ◽  
Industrial Robot ◽  
Load Capacity ◽  
Robotic Manipulators ◽  
Optimization Techniques ◽  
Highly Nonlinear ◽  
Manipulator Design ◽  
And Control

Robotics is a relatively new and evolving technology being applied to manufacturing automation and is fast replacing the special-purpose machines or hard automation as it is often called. Demands for higher productivity, better and uniform quality products, and better working environments are primary reasons for its development. An industrial robot is a multifunctional and computer-controlled mechanical manipulator exhibiting a complex and highly nonlinear behavior. Even though most current robots have anthropomorphic configurations, they have far inferior manipulating abilities compared to humans. A great deal of research effort is presently being directed toward improving their overall performance by using optimal mechanical structures and control strategies. The optimal design of robot manipulators can include kinematic performance characteristics such as workspace, accuracy, repeatability, and redundancy. The static load capacity as well as dynamic criteria such as generalized inertia ellipsoid, dynamic manipulability, and vibratory response have also been considered in the design stages. The optimal control problems typically involve trajectory planning, time-optimal control, energy-optimal control, and mixed-optimal control. The constraints in a robot manipulator design problem usually involve link stresses, actuator torques, elastic deformation of links, and collision avoidance. This paper presents a review of the literature on the issues of optimum design and control of robotic manipulators and also the various optimization techniques currently available for application to robotics.

scholarly journals Compliant Human–Robot Collaboration with Accurate Path-Tracking Ability for a Robot Manipulator

2021 ◽  
Vol 11 (13) ◽  
pp. 5914
Author(s):  
Daniel Reyes-Uquillas ◽  
Tesheng Hsiao
Keyword(s):  
Sliding Mode ◽  
Robot Manipulator ◽  
Industrial Robot ◽  
Path Following ◽  
Path Tracking ◽  
Control Loop ◽  
Loop Control ◽  
Tracking Ability ◽  
The Difference ◽  
Human Robot Collaboration

In this article, we aim to achieve manual guidance of a robot manipulator to perform tasks that require strict path following and would benefit from collaboration with a human to guide the motion. The robot can be used as a tool to increase the accuracy of a human operator while remaining compliant with the human instructions. We propose a dual-loop control structure where the outer admittance control loop allows the robot to be compliant along a path considering the projection of the external force to the tangential-normal-binormal (TNB) frame associated with the path. The inner motion control loop is designed based on a modified sliding mode control (SMC) law. We evaluate the system behavior to forces applied from different directions to the end-effector of a 6-DOF industrial robot in a linear motion test. Next, a second test using a 3D path as a tracking task is conducted, where we specify three interaction types: free motion (FM), force-applied motion (FAM), and combined motion with virtual forces (CVF). Results show that the difference of root mean square error (RMSE) among the cases is less than 0.1 mm, which proves the feasibility of applying this method for various path-tracking applications in compliant human–robot collaboration.

The Design and Simulation for Two-Link Manipulators PD Robust Controller under Uncertain Upper Bound Disturbance

2013 ◽  
Vol 694-697 ◽  
pp. 1652-1655
Author(s):  
Ji Yan Wang
Keyword(s):  
Dynamic Characteristics ◽  
Upper Bound ◽  
Control Method ◽  
Robot Manipulator ◽  
Industrial Robot ◽  
Pd Controller ◽  
Robust Controller ◽  
Convergence And Stability ◽  
Comparison And Analysis ◽  
Driving Torque

PD control method is widely utilized for the dynamic characteristics controlling in industrial robot manipulator area. The disturbance is usually uncertain in reality; the traditional PD controller is limited in that case. In this paper, a PD robust controller is introduced to optimize the convergence and stability of PD controller and avoid the extreme initial driving torque for two-link manipulator system. Using the co-simulation on Matlab/ Simulink and ADAMS, the paper designs a PD robust controller under uncertain upper bound disturbance and completes track control and driving torque simulation trial. The superiority of the two-link manipulators PD robust controller is verified through result comparison and analysis.

Development of the Industrial Robot Electric Drive Model

2021 ◽  
Vol 64 (6) ◽  
pp. 52-57
Author(s):  
Konstantin Litsin ◽  
◽  
Sergei Baskov ◽  
Yaroslav Makarov ◽  
◽  
...  
Keyword(s):  
Electric Drive ◽  
Robot Manipulator ◽  
Block Diagram ◽  
Industrial Robot ◽  
Permanent Magnet Synchronous Motor ◽  
Industrial Robots ◽  
Full Turn ◽  
Position Controller ◽  
Power Part ◽  
The Cost

Currently, the penetration of industrial robots into all sectors of the economy is increasing. However, there is an acute problem of conducting preliminary tests. The use of the digital twin as a replacement for the industrial robot is driven by high economic costs. In order to reduce the cost of the project, a solution is proposed to conduct preliminary tests on the developed model. The article developed a mathematical model of one of the drives of the industrial robot manipulator Yaskawa Motoman MH50-35. The model is suitable for researching the movement of the robot' tool. A mathematical description of a permanent magnet synchronous motor SGMJV-09A in a rotating coordinate system is given and a block diagram of the power part of the drive is made. A system for regulating the position of the robot's tool with a nonlinear position controller has been synthesized. Based on the results of modeling the operation of an electric drive in the Matlab Simulink environment, the degree of correspondence of the developed model to a real object was assessed and conclusions were drawn about the limits of its applicability for studying the operation of an electric drive of a robotic arm. The accuracy of working out the task for turning the wrist is 0.0001 rad, there is no overshoot in position, and the time for completing a full turn of the tool is 1.07 s.

Application of non-linear H∞ control to the Tetrabot robot manipulator

1998 ◽  
Vol 212 (6) ◽  
pp. 459-465 ◽  
Author(s):  
I Postlethwaite ◽  
A Bartoszewicz
Keyword(s):  
Degrees Of Freedom ◽  
Robot Manipulator ◽  
Industrial Robot ◽  
External Disturbances ◽  
Six Degrees Of Freedom ◽  
Control Scheme ◽  
Non Linear ◽  
Modelling Uncertainties ◽  
Real Robot ◽  
Robotic Application

In this paper, an application of a non-linear H∞ control law for an industrial robot manipulator is presented. Control of the manipulator motion is formulated into a non-linear H∞ optimization problem, namely optimal tracking performance in the presence of modelling uncertainties and external disturbances. Analytical solutions for this problem are implemented on a real robot. The robot under consideration is the six-degrees-of-freedom GEC Tetrabot. Investigations are made into the selection of weights for the H∞ controller and it is shown how different selections of weights affect the Tetrabot performance. The authors believe this to be the first robotic application of nonlinear H∞ control. Comparisons of the proposed control strategy with conventional proportional-derivative and proportional-integral-derivative controllers show favourable performance of the Tetrabot under the new non-linear H∞ control scheme.

Integrated Model Of Industrial Robot For Control Applications

1992 ◽  
pp. 27-41
Author(s):  
Johari Halim Shah Osman
Keyword(s):  
Robot Manipulator ◽  
Industrial Robot ◽  
Realistic Model ◽  
Integrated Model ◽  
Control Synthesis ◽  
Real Situation ◽  
Highly Nonlinear ◽  
Gear Trains ◽  
And Control ◽  
Space Description

This paper deals with the development of an intergrated mathematical model of a robot manipulator. The model of the system comprises the mechanical part of the robot as well as the actuators and the gear trains. Two different approaches of deriving the integrated model are presented which results in two different forms of the integrated dynamic model of the robot manipulator in state space description. Both types of the integrated model are highly nonlinear, time varying, and represent a more realistic model of the robotic system. The integrated model and the approach are useful and suitable for dynamic analysis and control synthesis purposes, and will provide a more efficient approach to the real situation.

scholarly journals PD Control Compensation Based on a Cascade Neural Network Applied to a Robot Manipulator

2020 ◽  
Vol 14 ◽  
Author(s):  
Luis Arturo Soriano ◽  
Erik Zamora ◽  
J. M. Vazquez-Nicolas ◽  
Gerardo Hernández ◽  
José Antonio Barraza Madrigal ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Robot Manipulator ◽  
Industrial Robot ◽  
Identification Problem ◽  
Adaptive Controller ◽  
Pd Control ◽  
Non Linear ◽  
Cascade Neural Network ◽  
Integral Gain

A Proportional Integral Derivative (PID) controller is commonly used to carry out tasks like position tracking in the industrial robot manipulator controller; however, over time, the PID integral gain generates degradation within the controller, which then produces reduced stability and bandwidth. A proportional derivative (PD) controller has been proposed to deal with the increase in integral gain but is limited if gravity is not compensated for. In practice, the dynamic system non-linearities frequently are unknown or hard to obtain. Adaptive controllers are online schemes that are used to deal with systems that present non-linear and uncertainties dynamics. Adaptive controller use measured data of system trajectory in order to learn and compensate the uncertainties and external disturbances. However, these techniques can adopt more efficient learning methods in order to improve their performance. In this work, a nominal control law is used to achieve a sub-optimal performance, and a scheme based on a cascade neural network is implemented to act as a non-linear compensation whose task is to improve upon the performance of the nominal controller. The main contributions of this work are neural compensation based on a cascade neural networks and the function to update the weights of neural network used. The algorithm is implemented using radial basis function neural networks and a recompense function that leads longer traces for an identification problem. A two-degree-of-freedom robot manipulator is proposed to validate the proposed scheme and compare it with conventional PD control compensation.

Sign in / Sign up

Export Citation Format

Share Document