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Machine Intelligence Research

Erschienen in:

01.06.2013

Type-2 Fuzzy Control for a Flexible- joint Robot Using Voltage Control Strategy

verfasst von: Majid Moradi Zirkohi, Mohammad Mehdi Fateh, Mahdi Aliyari Shoorehdeli

Erschienen in: Machine Intelligence Research | Ausgabe 3/2013

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Abstract

Type-1 fuzzy sets cannot fully handle the uncertainties. To overcome the problem, type-2 fuzzy sets have been proposed. The novelty of this paper is using interval type-2 fuzzy logic controller (IT2FLC) to control a flexible-joint robot with voltage control strategy. In order to take into account the whole robotic system including the dynamics of actuators and the robot manipulator, the voltages of motors are used as inputs of the system. To highlight the capabilities of the control system, a flexible joint robot which is highly nonlinear, heavily coupled and uncertain is used. In addition, to improve the control performance, the parameters of the primary membership functions of IT2FLC are optimized using particle swarm optimization (PSO). A comparative study between the proposed IT2FLC and type-1 fuzzy logic controller (T1FLC) is presented to better assess their respective performance in presence of external disturbance and unmodelled dynamics. Stability analysis is presented and the effectiveness of the proposed control approach is demonstrated by simulations using a two-link flexible-joint robot driven by permanent magnet direct current motors. Simulation results show the superiority of the IT2FLC over the T1FLC in terms of accuracy, robustness and interpretability.

Vorheriger Artikel An Optimization Algorithm Employing Multiple Metamodels and Optimizers

Nächster Artikel Projective Lag Synchronization and Parameter Identification of a New Hyperchaotic System

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[1]

B. Brogliato, R. Ortega, R. Lozano. Global tracking controllers for flexible-joint manipulators: A comparative study. Automatica, vol. 31, no. 7, pp. 941–956, 1995.CrossRefMATHMathSciNet

[2]

M. O. Tokhi, A. K. M. Azad. Flexible Robot Manipulators: Modeling, Simulation and Control, London: The Institution of Engineering and Technology, 2008.CrossRef

[3]

J. OReilly, P. Kokotovic, H. K. Khalil. Singular Perturbation Methods in Control: Analysis and Design, New York: Academic Press, 1986.

[4]

A. De Luca, A. Isidori, F. Nicolo. Control of robot arm with elastic joints via nonlinear dynamic feedback. In Proceedings of the 24th IEEE Conference on Decision Control, IEEE, Ft. Lauderdale, FL, USA, pp. 1671–1679, 1985.

[5]

M. W. Spong. Adaptive control of flexible joint manipulators: Comments on two papers. Automatica, vol. 31, no. 4, pp. 585–590, 1995.CrossRefMATHMathSciNet

[6]

G. A. Wilson. Robust tracking of elastic joint manipulators using sliding mode control. Transactions of the Institute of Measurement and Control, vol. 16, no. 2, pp. 99–107, 1994.CrossRef

[7]

M. W. Spong. Modeling and control of elastic joint robots. Journal of Dynamic Systems, Measurement, and Control, vol. 109, pp. 310–319, 1987.CrossRefMATH

[8]

L. C. Lin, C. C. Chen. Rigid model-based fuzzy control of flexible-joint manipulators. Journal of Intelligent and Robotic Systems, vol. 13, no.2, pp. 107–126, 1995.CrossRef

[9]

V. Zeman, R. V. Patel, K. Khorasani. Control of a flexible-joint robot using neural networks. IEEE Transactions on Control Systems Technology, vol. 5, no. 4, pp. 453–462, 1997.CrossRef

[10]

M. M. Fateh. Robust control of flexible-joint robots using voltage control strategy. Nonlinear Dynamics, vol. 67, no. 2, pp. 1525–1537, 2012.CrossRefMATHMathSciNet

[11]

M. M. Fateh. Robust voltage control of electrical manipulators in task-space. International Journal of Innovative Computing Information and Control, vol. 6, no. 6, pp. 2691–2700, 2010.

[12]

M. M. Fateh. On the voltage-based control of robot manipulators. International Journal of Control, Automation, and Systems, vol. 6, no. 5, pp. 702–712, 2008.

[13]

M. M. Fateh. Nonlinear control of electrical flexible-joint robots. Nonlinear Dynamics, vol. 67, no.4, pp. 2549–2559, 2012.CrossRefMATHMathSciNet

[14]

L. Cheng, Z. G. Hou, M. Tan. Adaptive neural network tracking control for manipulators with uncertain kinematics, dynamics and actuator model. Automatica, vol. 45, no. 10, pp. 2312–2318, 2009.CrossRefMATHMathSciNet

[15]

F. Karray, W. Gueaieb, S. Al-Sharhan. The hierarchical expert tuning of PID controllers using tools of soft computing. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 32, no. 1, pp. 77–90, 2002.CrossRef

[16]

M. M. Fateh, S. Fateh. A precise robust fuzzy control of robots using voltage control strategy. International Journal of Automation and Computing, vol. 10, no. 1, pp. 64–72, 2013.CrossRefMathSciNet

[17]

Q. Zhu, A. G. Song, T. P. Zhang, Y. Q. Yang. Fuzzy adaptive control of delayed high order nonlinear systems. International Journal of Automation and Computing, vol. 9, no.2, pp. 191–199, 2012.CrossRef

[18]

J. M. Mendel. Uncertain Rule-based Fuzzy Logic Systems: Introduction and New Directions, Upper Saddle River, NJ: Prentice-Hall, 2001.

[19]

L. A. Zadeh. The concept of a linguistic variable and its application to approximate reasoning-1. Information Sciences, vol. 8, no. 3, pp. 199–249, 1975.CrossRefMATHMathSciNet

[20]

D. R. Wu, W. W. Tan. Genetic learning and performance evaluation of interval type-2 fuzzy logic controllers. Engineering Applications of Artificial Intelligence, vol. 19, no. 8, pp. 829–841, 2006.CrossRef

[21]

L. X. Wang. A Course in Fuzzy Systems and Control, New York: Prentice Hall, 1996.

[22]

H. A. Hagras. A hierarchical type-2 fuzzy logic control architecture for autonomous mobile Robots. IEEE Transactions on Fuzzy Systems, vol. 12, no. 4, pp. 524–539, 2004.CrossRef

[23]

T. Ozen, J. M. Garibaldi, S. Musikasuwan. Modelling the variation in human decision making. In Proceedings of IEEE Annual Meeting of the Fuzzy Information, IEEE, Banff, Alberta, Canada, vol.2, pp. 617–622, 2000.

[24]

P. Melin, O. Castillo. A new approach for quality control of sound speakers combining type-2 fuzzy logic and fractal theory. In Proceedings of the IEEE International Conference on Fuzzy Systems, IEEE, Honolulu, HI, USA, vol. 2, pp. 825–830. 2002.

[25]

V. Mukherjee, S. P. Ghoshal. Intelligent particle swarm optimized fuzzy PID controller for AVR system. Electric Power Systems Research, vol. 77, no. 12, pp. 1689–1698, 2007.CrossRef

[26]

R. Poli, J. Kennedy, T. Blackwell. Particle swarm optimization. Swarm Intelligent, vol. 1, no. 1, pp. 33–57, 2007.CrossRef

[27]

M. M. Fateh, M. M. Zirkohi. Adaptive impedance control of a hydraulic suspension system using particle swarm optimisation. Vehicle System Dynamics, vol. 49, no. 12, pp. 1951–1965, 2011.CrossRef

[28]

M. W. Spong, S. Hutchinson, M. Vidyasagar. Robot Modeling and Control, New York: Wiley, 2006.

[29]

Q. L. Liang, J. M. Mendel. Interval type-2 fuzzy logic systems: Theory and design. IEEE Transactions on Fuzzy Systems, vol. 8, no. 5, pp. 535–550, 2000.CrossRef

[30]

M. Biglarbegian, W. W. Melek, J. M. Mendel. On the stability of interval type-2 TSK fuzzy logic control systems. IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, vol.40, no. 3, pp. 798–818, 2010.CrossRef

[31]

M. M. Fateh. Robust control of flexible-joint robots using voltage control strategy. Nonlinear Dynamics, vol. 67, no. 2, pp. 1525–1537, 2012.CrossRefMATHMathSciNet

[32]

M. M. Fateh. Robust fuzzy control of electrical manipulators. Journal Intelligent & Robotic Systems, vol. 60, no. 3-4, pp. 415–434, 2010.CrossRefMATH

Titel: Type-2 Fuzzy Control for a Flexible- joint Robot Using Voltage Control Strategy
verfasst von: Majid Moradi Zirkohi
Mohammad Mehdi Fateh
Mahdi Aliyari Shoorehdeli
Publikationsdatum: 01.06.2013
Verlag: Springer-Verlag
Erschienen in: Machine Intelligence Research / Ausgabe 3/2013
Print ISSN: 2731-538X
Elektronische ISSN: 2731-5398
DOI: https://doi.org/10.1007/s11633-013-0717-x

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