Unified control systems for industrial robots

1978 ◽  
Vol 5 (4) ◽  
pp. 171-173
Author(s):  
E.I. Yurevich ◽  
V.A. Obukhov ◽  
Yu.D. Andrianov
Keyword(s):  
Control Systems ◽  
Industrial Robots

Optimization on quick-speed of control systems of industrial robots

2018 ◽  
pp. 54-57
Author(s):  
V.G. Farhadov ◽  
◽  
A.A. Babaeva ◽  
A.T. Mamedova ◽  
◽  
...  
Keyword(s):  
Control Systems ◽  
Industrial Robots

A Dynamic Simulation Model of Industrial Robots for Energy Examination Purpose

2015 ◽  
Vol 805 ◽  
pp. 223-230 ◽  
Author(s):  
Paryanto ◽  
Alexander Hetzner ◽  
Matthias Brossog ◽  
Jörg Franke
Keyword(s):  
Energy Consumption ◽  
Control Systems ◽  
Feedback Linearization ◽  
Coulomb Friction ◽  
State Of The Art ◽  
Industrial Robot ◽  
Motor Drives ◽  
Industrial Robots ◽  
Dynamic Simulation Model ◽  
Linear Damping

In this paper, a modular dynamic model of an industrial robot (IR) for predicting and analyzing its energy consumption is developed. The model consists of control systems, which include a state-of-the-art feedback linearization controller, permanent magnet synchronous drives and the mechanical structure with Coulomb friction and linear damping. By using the developed model, a detailed analysis of the influence of different parameter sets on the energy consumption and loss energy of IRs is investigated. The investigation results show that the operating parameters, robot motor drives, and mechanical damping and elasticity of robot transmissions have a significant effect on the energy consumption and accuracy of IRs. However, these parameters are not independent, but rather interrelated. For example, a higher acceleration and velocity shortens IRs’ operating periods, but needs a greater motor current, tends to excite vibrations to a greater extent, and thus produces a higher amount of loss energy.

scholarly journals A review on control systems hardware and software for robots of various scale and purpose. Part 1. Industrial robotics

2019 ◽  
Vol 7 (5) ◽  
pp. 30-46 ◽  
Author(s):  
A. M. Romanov
Keyword(s):  
Control System ◽  
Fault Tolerance ◽  
Control Systems ◽  
Intelligent Control ◽  
Industrial Robot ◽  
Cloud Service ◽  
Point Of View ◽  
Industrial Robots ◽  
Robotic Systems ◽  
Industrial Robotics

A review of robotic systems is presented. The paper analyzes applied hardware and software solutions and summarizes the most common block diagrams of control systems. The analysis of approaches to control systems scaling, the use of intelligent control, achieving fault tolerance, reducing the weight and size of control system elements belonging to various classes of robotic systems is carried out. The goal of the review is finding common approaches used in various areas of robotics to build on their basis a uniform methodology for designing scalable intelligent control systems for robots with a given level of fault tolerance on a unified component base. This part is dedicated to industrial robotics. The following conclusions are made: scaling in industrial robotics is achieved through the use of the modular control systems and unification of main components; multiple industrial robot interaction is organized using centralized global planning or the use of previously simulated control programs, eliminating possible collisions in working area; intellectual technologies in industrial robotics are used primarily at the strategic level of the control system which is usually non-real time, and in some cases even implemented as a remote cloud service; from the point of view of ensuring fault tolerance, the industrial robots developers are primarily focused on the early prediction of faults and the planned decommissioning of the robots, and are not on highly-avaliability in case of failures; industrial robotics does not impose serious requirements on the dimensions and weight of the control devices.

Mathematical Models of the Control Systems for Calibration of the Tools' Orientation for the Industrial Robots

2017 ◽  
Vol 18 (10) ◽  
pp. 664-669
Author(s):  
O. N. Krakhmalev ◽  
◽  
D. I. Petreshin ◽  
G. N. Krakhmalev ◽  
◽  
...  
Keyword(s):  
Mathematical Models ◽  
Control Systems ◽  
Industrial Robots

Design analysis and optimization of 6-DOF telemanipulator based on performance indices

Robotica ◽  
2018 ◽  
Vol 36 (12) ◽  
pp. 1822-1835
Author(s):  
Seungnam Yu ◽  
Soonwoong Hwang ◽  
Jongkwang Lee ◽  
Byungsuk Park ◽  
Hyojik Lee
Keyword(s):  
Optimal Design ◽  
Control Systems ◽  
Current System ◽  
Industrial Robots ◽  
Target System ◽  
Performance Indices ◽  
Automated Control ◽  
Current Design ◽  
Teleoperation Systems ◽  
High Degree

SUMMARYIn contrast to general industrial robots, which are operated in normal environments and are easily accessible by human workers, telemanipulators are typically designed to perform specific and extreme tasks in hazardous areas. Teleoperation systems are difficult-to-equip fully intuitive or automated control systems because these are the kinds of manipulator systems used as substitutes to perform tasks that humans have to guide directly because they may require tough, sensitive, or sophisticated handling motions. Basically, these kinds of tasks are difficult to remotely perform through a slave manipulator operated by a human unless modification and optimization of the system are conducted. In this regard, the target system dealt within this study has similar disadvantages even though it has a high degree of freedom (DOF) arm structure. The performance of the current system was quantitatively evaluated to optimize the structure according to the considered main tasks. This work presents the various performance indices utilized and analyzes the current design of the considered telemanipulator system. An optimal design approach using the parameters associated with the frequent motions of the considered 6-DOF telemanipulator is then proposed based on the conducted analyses.

The Cerebellum Analogue Approach to Robot Sequence Control

1976 ◽  
Vol 3 (3) ◽  
pp. 113-117
Author(s):  
J.F. Young ◽  
J.J. Walker
Keyword(s):  
Control Systems ◽  
Robot Control ◽  
Industrial Robots ◽  
Human Operator ◽  
Shop Floor ◽  
Operational Environment ◽  
Current Systems

The programmable sequence‐control systems used for most of the industrial robots available at the present time are basically simple and straight‐forward. While these are very desirable attributes for any system which has to achieve reliability in the performance of a repetitive task on the shop floor, there can be attendant disadvantages. Although most of the current systems are flexible enough to allow for fairly easy re‐programming by a human operator, this flexibility does not in general extend to the inclusion of any facilities for control adaptation to slight and possibly quite random changes in either the operational requirements or in the operational environment. The features of flexibility and adaptability, so obvious and so useful in the human operator, are notably absent from robot control systems.

A direct approach to modeling an industrial robot from samples of input-output data

Robotica ◽  
1984 ◽  
Vol 2 (3) ◽  
pp. 161-167 ◽  
Author(s):  
Ajit M. Karnik ◽  
Naresh K. Sinha
Keyword(s):  
Control Systems ◽  
Direct Method ◽  
Industrial Robot ◽  
Industrial Robots ◽  
Continuous Time Model ◽  
Output Data ◽  
Time Model ◽  
System A ◽  
Input And Output ◽  
A Direct Method

SUMMARYThe increased demand on the performance and efficiency of industrial robots, has led to the design of sophisticated control systems. Such control systems require an accurate dynamic model of the system. A commonly used method of modeling an industrial robot, involves the description of a set of dynamic equations, relating actuator torques to loads and accelerations. These equations are generally quite complex and inconvenient for implementation on digital computers.Another method often used for identification, is the ‘indirect method’, in which the transfer function is obtained in two steps. The discrete time model is first derived from samples of the input and output measurements, which is then transformed to the continuous-time model. A limitation of this method is that it requires the excitation to be of the ‘persistently exciting’ type, thus precluding the application of simple inputs like the step signal.This paper describes a ‘direct’ method for identification of an ‘industrial robot’ from samples of input and output observations. Results of modeling an industrial robot and two simulations are presented. One of the simulations, and the industrial robot uses the step input as excitation. The other example was excited with an exponential input.

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