Amin Jalali; Farzin Piltan; Atefeh Gavahian; Meysam Jalali; Mozhdeh Adibi

Model-Free Adaptive Fuzzy Sliding Mode Controller Optimized by Particle Swarm for Robot Manipulator

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Author(s)

Amin Jalali ^1,* Farzin Piltan ² Atefeh Gavahian ³ Meysam Jalali ⁴ Mozhdeh Adibi ⁵

1. Department of Maritime Electronic and Communication Engineering, College of Maritime Engineering, Chabahar University, Iran

2. Senior Researcher at Research and Development Unit, SanatkadeheSabze Pasargad company, (S.S.P. Co), Shiraz, Iran

3. Department of Electrical Engineering, Boushehr Branch, Islamic Azad University, Boushehr, Iran

4. Department of Electronic Engineering, University of Sistan and Balouchestan, Iran

5. Department of Computer Science, Islamic Azad University, Shiraz, Iran

* Corresponding author.

DOI: https://doi.org/10.5815/ijieeb.2013.01.08

Received: 18 Feb. 2013 / Revised: 11 Mar. 2013 / Accepted: 6 Apr. 2013 / Published: 8 May 2013

Index Terms

Uncertain nonlinear systems, non-classical control, fuzzy logic, classical control, sliding mode controller, robot manipulator, Model free adaptive fuzzy sliding mode controller (AFSMC), Model free sliding mode controller, Particle Swarm Optimization (PSO

Abstract

The main purpose of this paper is to design a suitable control scheme that confronts the uncertainties in a robot. Sliding mode controller (SMC) is one of the most important and powerful nonlinear robust controllers which has been applied to many non-linear systems. However, this controller has some intrinsic drawbacks, namely, the chattering phenomenon, equivalent dynamic formulation, and sensitivity to the noise. This paper focuses on applying artificial intelligence integrated with the sliding mode control theory. Proposed adaptive fuzzy sliding mode controller optimized by Particle swarm algorithm (AFSMC-PSO) is a Mamdani’s error based fuzzy logic controller (FLS) with 7 rules integrated with sliding mode framework to provide the adaptation in order to eliminate the high frequency oscillation (chattering) and adjust the linear sliding surface slope in presence of many different disturbances and the best coefficients for the sliding surface were found by offline tuning Particle Swarm Optimization (PSO). Utilizing another fuzzy logic controller as an impressive manner to replace it with the equivalent dynamic part is the main goal to make the model free controller which compensate the unknown system dynamics parameters and obtain the desired control performance without exact information about the mathematical formulation of model.

Cite This Paper

Amin Jalali, Farzin Piltan, Atefeh Gavahian, Meysam Jalali, MozhdehAdibi, "Model-Free Adaptive Fuzzy Sliding Mode Controller Optimized by Particle Swarm for Robot Manipulator ", International Journal of Information Engineering and Electronic Business(IJIEEB), vol.5, no.1, pp.68-78, 2013. DOI:10.5815/ijieeb.2013.01.08

Reference

[[1]Thomas R. Kurfess, 2005. Robotics and Automation Handbook, CRC press.

[2]Desai, P., 1992. “A Decentralized Adaptive Controller for Robot Manipulator Trajectory Control”, S.G Tzafestas, Robotic Systems, Netherlands, pp: 109-116.

[3]Slotine, J.J.E., 1984. Sliding controller design for nonlinear systems, Int. J. Control, 40: 421-434.

[4]Zhou, J., P. Coiffet, 1992. Fuzzy Control of Robots. Proceedings IEEE International Conference on Fuzzy Systems, pp: 1357-1364.

[5]Banerjee, S., Peng Yung Woo, 1993. Fuzzy logic control of robot manipulator. Proceedings Second IEEEConference on Control Applications, pp: 87-88.

[6]Lotfi, A. Zadeh, 1994. Fuzzy logic,Neural network, and Soft computing. Communications of the ACM, 3(37): 7784.

[7]Akbarzadeh, T.A.R., K. Kumbla, E. Tunstel, M. Jamshidi., 2000. Soft Computing for autonomous Robotic Systems. IEEE International Conference on Systems, Man and Cybernatics, pp: 5252-5258.

[8]Yoo, B. and Ham, W. (1998). Adaptive fuzzy sliding mode, control of nonlinear system, IEEE Transactions on Fuzzy Systems 6(2): 315–321.

[9]Wang, L.X., 1993. Stable adaptive fuzzy control of nonlinear systems. IEEE Trans. Fuzzy systems, 1(2): 146-154

[10]Wang, J., Rad, A. B. and Chan, P. T. (2001). Indirect adaptive fuzzy sliding mode control: Part i and ii, Fuzzy Sets and Systems 122: 21–43.

[11]Chang, W., Park, J. B., Joo, Y. H. and Chen, G. (2002). Design of robust fuzzy-model-based controller with sliding mode control for SISO nonlinear systems, Fuzzy Sets and Systems 125: 1–22.

[12]Wang, X.-S., Su, C.-Y. and Hong, H. (2004). Robust adaptive control of a class of nonlinear systems with unknown dead-zone, Automatica 40: 407–413.

[13]Kung, C.-C. and Chen, T.-H, 2005. Observer-based indirect adaptive fuzzy sliding mode control with state variable filters for unknown nonlinear dynamical systems, Fuzzy Sets and Systems 155: 292–308.

[14]Wang, Q. and Su, C.-Y. (2006). Robust adaptive control of a class of nonlinear systems including actuator hysteresis with prandt–ishlinskii presentations, Automatica 42: 859–867.

[15]Lin, C.K. and S.D. Wang, 1997. Robust self-tuning rotated fuzzy basis function controller for robot arms, IEE Proc-control theory Appl., 144(4): 293-298.

[16]Elshafei, A.L., A. Elnaggar, 1997. Adaptive versus adaptive fuzzy logic control, IEEE, International Symposium on intelligent control, pp: 49-54.

[17]Kwan, E., M. Liu, 1999. An adaptive fuzzy approach for robot manipulators tracking, IEEE conference on computation intelligent in robotics and automation, pp: 53-58.

[18]Liu, M., 2000. Stability analysis of decentralized adaptive fuzzy logic control for robot arm tracking, IEEE conference on decision and control, pp: 883-888.

[19]Berstecher, R.G., R. Palm and H.D. Unbehaven, 2001. An adaptive fuzzy sliding mode controller, IEEE Trans. On industrial electronics, 48(1): 18-31.

[20]Kim, V.T., 2002. Independent joint adaptive fuzzy control of robot manipulator, IEEE conference on Automation congress, pp: 645-652.

[21]Wang, Y.F. and T.Y. Chai, 2004. Robust adaptive fuzzy observer design in robot arms, IEEE conference on control, pp: 857-862.

[22]Sudeept Mohan, and Surekha Bhanot, 2006. Comparative Study of Some Adaptive Fuzzy algorithms for Manipulator Control. International Journal of computation and intelligence, 3(4): 303-311.

[23]Sharma, R. and M. Gopal, 2008. A Markov game-adaptive fuzzy controller for robot manipulators, IEEETrans. On fuzzy systems, 16(1): 171-186.

[24]Farzin Piltan, Shahnaz Tayebi Haghighi, Ali Reza Zare, Amin Jalali, Ali Roshanzamir, Marzie Zare, Farhad Golshan,2011. Artificial Control of Nonlinear Second Order Systems Based on AFGSMC. On fuzzy systems, 5(6):509-522

[25]Slotine, J.J.E. and J.A. Coetsee, 1986. Adaptive sliding controller synthesis for nonlinear systems, Int. J. Control, 43(6): 1631-1651.

[26]Slotine, J.J.E. and W. Li, 1987. On the adaptive control of robot manipulators, Int. J. Robotics research, 6(3): 49-59.

[27]Utkin, I.V., 1977. Variable structure systems with sliding modes, IEEE. Trans. Auto. Control, 22(2): 2121-222.

[28]Armstrong, B., Khatib, O. and Burdick, J. 2002. The Explicit Dynamic Model and Inertial Parameters of the PUMA 560 Arm. IEEE. 510-518.

[29]ZHANG D. Q. and Panda S. K. Chattering-free and fast response sliding mode controller. IEE Proceedings D:Theory and Applications, 1999, 146: 171-177.

[30]Palm, R., 1992. Sliding mode fuzzy control, IEEE conference, pp: 519-526.

[31]Emami, M.R., I.B. Turksen and A.A. Goldenberg, 1998. Development of a systematic methodology of fuzzy logic modeling, IEEE Trans. On fuzzy systems, 6(3): 346-361.

[32]B. Siciliano and O. Khatib, Springer handbook of robotics: Springer-Verlag New York Inc, 2008.

[33]Abdel-Razzak MERHEB, “Nonlinear Control Algorithms applied to 3 DOF PUMA Robot”, METU 2008.

[34]Reznik. L., 1997. Fuzzy controllers. B.H Newnes.

[35]Jinhui Zhang, Peng Shi, Senior Member, IEEE, and Yuanqing Xia, Robust Adaptive Sliding Mode Control for Fuzzy Systems with Mismatched Uncertainties.

[36]n investigation of adaptive fuzzy sliding mode control for robot manipulator, by Xiasong Lu Y. Hsu, and L. C. Fu, 1995;Yoo, and Hams, 1998; C. C. Chain, and C. C. Hu, 1999; Yoo, and Hams, 2000; C. L. Hwang, 2000; Y. Guo,and P. Y. Yung, 2003; C. L. Hwang, and C. F. Chao, 2004; N. Sadati, and A. Talasaz, 2004; Lin,and su,2004; C. M. Lin, and C. F. Hsu, 2004; R. Shahnazi, and M. R. Akbarzadeh, 2005; R. Shahnazi et al., 2006-2008; J. K. Liu, and F. C. Sun, 2006; H. Medhaffar et al., 2006; C. C. Chiang, and C. H. Wu, 2007; C. C.Weng, and W. S. Yu, 2008; Z. X. Yu, 2009.

[37]Piltan, F., et al., Design of Model Free Adaptive Fuzzy Computed Torque Controller: Applied to Nonlinear Second Order System, International Journal of Robotics and Automation (IJRA), Volume (2): Issue (4): 2011.

[38]Piltan, F., et al., Evolutionary Design of Mathematical Tunable FPGA Based MIMO Fuzzy Estimator Sliding Mode Based Lyapunov Algorithm: Applied to Robot Manipulator,” International Journal of Robotics and Automation (IJRA), Volume (2): Issue (5): 2011.

[39]Piltan, F., et al., Artificial Robust Control of Robot Arm: Design a Novel SISO Back-stepping Adaptive Lyapunov Based Variable Structure Control, International Journal of Control and Automation (IJCA): Vol. 4 No. 4, December, 2011.

[40]Piltan, F., et al., 2011. Design Artificial Nonlinear Robust Controller Based on CTLC and FSMC with Tunable Gain, International Journal of Robotic and Automation, 2 (3): 205-220.

[41]Piltan, F., et al., 2011. Design Mathematical Tunable Gain PID-Like Sliding Mode Fuzzy Controller with Minimum Rule Base, International Journal of Robotic and Automation, 2 (3): 146-156.

[42]Piltan, F., et al., Design of PC-Based Sliding Mode Controller and Normalized Sliding Surface Slope Using PSO Method for Robot Manipulator, International Journal of Robotics and Automation (IJRA), Volume (2): Issue (4): 2011.

[43]Piltan, F., et al., Novel Artificial Control of Nonlinear Uncertain System: Design a Novel Modified PSO SISO Lyapunov Based Fuzzy Sliding Mode Algorithm, International Journal of Robotics and Automation (IJRA): Volume (2): Issue (5): 2011.

[44]Arquilla, J. and Ronfeldt, D., “Swarming and the Future of Conflict”. DB-311, RAND, SantaMonica, CA, 2000.

[45]Kennedy, J., “The Particle Swarm: Social Adaptation of Knowledge”, IEEE International conference on Evolutionary Computation, Indianapolis, Indiana, USA, pp. 303-308, 1997.

[46]Sun Tzu, “The Art of War” (tr. S.B Griffith), Oxford University Press 1963.

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