Access the full text.
Sign up today, get DeepDyve free for 14 days.
I. Mareels, H. Penfold, R. Evans (1992)
Controlling nonlinear time-varying systems via euler approximationsAutom., 28
Ming-Chang Tsai, G. Anwar, M. Tomizuka (1988)
Discrete time repetitive control for robot manipulatorsProceedings. 1988 IEEE International Conference on Robotics and Automation
H. Ho, Y. Wong, A. Rad (2007)
Robust fuzzy tracking control for robotic manipulatorsSimul. Model. Pract. Theory, 15
M. Corless, G. Leitmann (1981)
Continuous state feedback guaranteeing uniform ultimate boundedness for uncertain dynamic systemsIEEE Transactions on Automatic Control, 26
Abdelhamid Tayebi (2003)
Adaptive iterative learning control for robot manipulatorsProceedings of the 2003 American Control Conference, 2003., 5
Ming-Chang Tsai, M. Tomizuka (1989)
Model reference adaptive control and repetitive control for robot manipulatorsProceedings, 1989 International Conference on Robotics and Automation
K. Ogata (1987)
Discrete-time control systems
Abdelhamid Tayebi (2004)
Adaptive iterative learning control for robot manipulators*1Automatica, 40
P. Jiang, Leon Bamforth, Zuren Feng, J. Baruch, Y. Chen (2007)
Indirect Iterative Learning Control for a Discrete Visual Servo Without a Camera-Robot ModelIEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 37
Zhi-hui Zhan, Jun Zhang (2008)
Adaptive Particle Swarm OptimizationIEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 39
H. Modares, A. Alfi, M. Fateh (2010)
Parameter identification of chaotic dynamic systems through an improved particle swarm optimizationExpert Syst. Appl., 37
M. Fateh (2008)
On the Voltage-Based Control of Robot ManipulatorsInternational Journal of Control Automation and Systems, 6
Z. Qu, D. Dawson (1996)
Robust tracking control of robot manipulators
R. Poli, J. Kennedy, T. Blackwell (1995)
Particle swarm optimizationSwarm Intelligence, 1
C. Kempf, W. Messner, M. Tomizuka, R. Horowitz (1993)
A Comparison of Four Discrete-Time Repetitive Control Algorithms1992 American Control Conference
M. Fateh, H. Tehrani, S. Karbassi (2013)
Repetitive control of electrically driven robot manipulatorsInternational Journal of Systems Science, 44
M. Fateh (2009)
Robust impedance control of a hydraulic suspension systemInternational Journal of Robust and Nonlinear Control, 20
W. Dixon, M. Queiroz, Fumin Zhang, D. Dawson (1999)
Tracking Control of Robot Manipulators with Bounded Torque InputsRobotica, 17
P. Pathak, R. Kumar, A. Mukherjee, A. Dasgupta (2008)
A scheme for robust trajectory control of space robotsSimul. Model. Pract. Theory, 16
M. Fateh (2012)
Robust control of flexible-joint robots using voltage control strategyNonlinear Dynamics, 67
M. Fateh, S. Khorashadizadeh (2012)
Robust control of electrically driven robots by adaptive fuzzy estimation of uncertaintyNonlinear Dynamics, 69
M. Spong, S. Hutchinson, M. Vidyasagar (2005)
Robot Modeling and Control
C. Neuman, V. Tourassis (1985)
Discrete dynamic robot modelsIEEE Transactions on Systems, Man, and Cybernetics, SMC-15
M. Fateh (2010)
Proper uncertainty bound parameter to robust control of electrical manipulators using nominal modelNonlinear Dynamics, 61
M. Fateh, M. Zirkohi (2011)
Adaptive impedance control of a hydraulic suspension system using particle swarm optimisationVehicle System Dynamics, 49
Purpose – Applying discrete linear optimal control to robot manipulators faces two challenging problems, namely nonlinearity and uncertainty. This paper aims to overcome nonlinearity and uncertainty to design the discrete optimal control for electrically driven robot manipulators. Design/methodology/approach – Two novel discrete optimal control approaches are presented. In the first approach, a control‐oriented model is applied for the discrete linear quadratic control while modeling error is estimated and compensated by a robust time‐delay controller. Instead of the torque control strategy, the voltage control strategy is used for obtaining an optimal control that is free from the manipulator dynamics. In the second approach, a discrete optimal controller is designed by using a particle swarm optimization algorithm. Findings – The first controller can overcome uncertainties, guarantee stability and provide a good tracking performance by using an online optimal algorithm whereas the second controller is an off‐line optimal algorithm. The first control approach is verified by stability analysis. A comparison through simulations on a three‐link electrically driven robot manipulator shows superiority of the first approach over the second approach. Another comparison shows that the first approach is superior to a bounded torque control approach in the presence of uncertainties. Originality/value – The originality of this paper is to present two novel optimal control approaches for tracking control of electrically driven robot manipulators with considering the actuator dynamics. The novelty is that the proposed control approaches are free from the robot's model by using the voltage control strategy. The first approach is a novel discrete linear quadratic control design supported by a time‐delay uncertainty compensator. The second approach is an off‐line optimal design by using the particle swarm optimization.
COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering – Emerald Publishing
Published: Jan 1, 2014
Keywords: Electric motors; Robotics; Nonlinear control systems; Optimal control; Optimal design
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.