Robot Control Overview: An Industrial Perspective

One key competence for robot manufacturers is robot control, defined as all the technologies needed to control the electromechanical system of an industrial robot. By means of modeling, identification, optimization, and model-based control it is possible to reduce robot cost, increase robot performance, and solve requirements from new automation concepts and new application processes. Model-based control, including kinematics error compensation, optimal servo referenceand feed-forward generation, and servo design, tuning, and scheduling, has meant a breakthrough for the use of robots in industry. Relying on this breakthrough, new automation concepts such as high performance multi robot collaboration and human robot collaboration can be introduced. Robot manufacturers can build robots with more compliant components and mechanical structures without loosing performance and robots can be used also in applications with very high performance requirements, e.g., in assembly, machining, and laser cutting. In the future it is expected that the importance of sensor control will increase, both with respect to sensors in the robot structure to increase the control performance of the robot itself and sensors outside the robot related to the applications and the automation systems. In this connection sensor fusion and learning functionalities will be needed together with the robot control for easy and intuitive installation, programming, and maintenance of industrial robots.

[1]  Jon Rigelsford,et al.  Modelling and Control of Robot Manipulators , 2000 .

[2]  Mikael Norrlöf,et al.  A New Concept for Motion Control of Industrial Robots , 2008 .

[3]  Zengxi Pan,et al.  Machining with flexible manipulator: toward improving robotic machining performance , 2005, Proceedings, 2005 IEEE/ASME International Conference on Advanced Intelligent Mechatronics..

[4]  Bert Lauwers,et al.  Development of a robot based fettling cell for castings in low series , 2003 .

[5]  Mikael Norrlöf,et al.  Iterative Learning Control : Analysis, Design, and Experiments , 2000 .

[6]  Alin Albu-Schäffer,et al.  On a new generation of torque controlled light-weight robots , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[7]  Zhongxue Gan,et al.  Learning skills : robotics technology in automotive powertrain assembly , 2004 .

[8]  Zhijie Xia,et al.  Modeling and control of flexible manipulators , 1992 .

[9]  Mi Friswell,et al.  17th IFAC World Congress , 2008 .

[10]  Gunnar Bolmsjö,et al.  Extending an industrial robot controller: implementation and applications of a fast open sensor interface , 2005, IEEE Robotics & Automation Magazine.

[11]  Marcus Pettersson,et al.  Design Optimization in Industrial Robotics Methods and Algorithms for Drive Train Design , 2008 .

[12]  Måns Östring,et al.  Identification, Diagnosis, and Control of a Flexible Robot Arm , 2002 .

[14]  Svante Gunnarsson,et al.  A Benchmark Problem for Robust Control of a Multivariable Nonlinear Flexible Manipulator , 2008 .

[15]  Mike Wilson Robots in the Aerospace Industry , 1994 .

[16]  Erik Wernholt,et al.  Multivariable Frequency-Domain Identification of Industrial Robots , 2007 .

[17]  Geir Hovland,et al.  The Tau PKM Structures , 2008 .

[18]  Torgny Brogårdh,et al.  Present and future robot control development - An industrial perspective , 2007, Annu. Rev. Control..

[19]  Rolf Johansson,et al.  Integrated architecture for industrial robot programming and control , 1999, Robotics Auton. Syst..