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Adaptive MIMO Fuzzy Compensate Fuzzy Sliding Mode Algorithm: Applied to Second Order Nonlinear System
Farzin Piltan, N. Sulaiman, Payman Ferdosali, Mehdi Rashidi, Zahra Tajpeikar
Pages - 380 - 398     |    Revised - 01-11-2011     |    Published - 15-12-2011
Volume - 5   Issue - 5    |    Publication Date - November / December 2011  Table of Contents
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KEYWORDS
Adaptive Fuzzy Sliding Mode Algorithm, Lyapunov Based, Adaptive MIMO Fuzzy Compensate Fuzzy Sliding Mode, Chattering Phenomenon, Fuzzy Logic Controller, Adaptive Law
ABSTRACT
This research is focused on proposed adaptive fuzzy sliding mode algorithms with the adaptation laws derived in the Lyapunov sense. The stability of the closed-loop system is proved mathematically based on the Lyapunov method. Adaptive MIMO fuzzy compensate fuzzy sliding mode method design a MIMO fuzzy system to compensate for the model uncertainties of the system, and chattering also solved by linear saturation method. Since there is no tuning method to adjust the premise part of fuzzy rules so we presented a scheme to online tune consequence part of fuzzy rules. Classical sliding mode control is robust to control model uncertainties and external disturbances. A sliding mode method with a switching control low guarantees the stability of the certain and/or uncertain system, but the addition of the switching control low introduces chattering into the system. One way to reduce or eliminate chattering is to insert a boundary layer method inside of a boundary layer around the sliding surface. Classical sliding mode control method has difficulty in handling unstructured model uncertainties. One can overcome this problem by combining a sliding mode controller and artificial intelligence (e.g. fuzzy logic). To approximate a time-varying nonlinear dynamic system, a fuzzy system requires a large amount of fuzzy rule base. This large number of fuzzy rules will cause a high computation load. The addition of an adaptive law to a fuzzy sliding mode controller to online tune the parameters of the fuzzy rules in use will ensure a moderate computational load. The adaptive laws in this algorithm are designed based on the Lyapunov stability theorem. Asymptotic stability of the closed loop system is also proved in the sense of Lyapunov.
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1 Bazregar, M., Piltan, F., Nabaee, A., & Ebrahimi, M. (2014). Design Modified Fuzzy PD Gravity Controller with Application to Continuum Robot. International Journal of Information Technology and Computer Science (IJITCS), 6(3), 82.
2 Mozafari, N. G., Piltan, F., Shamsodini, M., Yazdanpanah, A., & Roshanzamir, A. (2014). On Line Tuning Premise and Consequence FIS Based on Lyaponuv Theory with Application to Continuum Robot. International Journal of Intelligent Systems and Applications (IJISA), 6(3), 96.
3 Nazari, I., Hosainpour, A., Piltan, F., Emamzadeh, S., & Mirzaie, M. (2014). Design Sliding Mode Controller with Parallel Fuzzy Inference System Compensator to Control of Robot Manipulator. International Journal of Intelligent Systems and Applications (IJISA), 6(4), 63.
4 Piran, M., Piltan, F., Akbari, M., Garg, R., & Bazregar, M. (2014). Quality Model and Artificial Intelligence Base Fuel Ratio Management with Applications to Automotive Engine. International Journal of Intelligent Systems and Applications (IJISA), 6(2), 76.
5 Piltan, F., Bazregar, M., Piran, M., & Akbari, M. (2014). Quality Model and Artificial Intelligence Base Fuel Ratio Management with Applications to Automotive Engine. IAES International Journal of Artificial Intelligence (IJ-AI), 3(1), 36-48.
6 Piltan, F., Piran, M., Bazregar, M., & Akbari, M. (2013). Design High Impact Fuzzy Baseline Variable Structure Methodology to Artificial Adjust Fuel Ratio. International Journal of Intelligent Systems and Applications (IJISA), 5(2), 59.
7 Piltan, F., Yarmahmoudi, M., Mirzaie, M., Emamzadeh, S., & Hivand, Z. (2013). Design Novel Fuzzy Robust Feedback Linearization Control with Application to Robot Manipulator. International Journal of Intelligent Systems and Applications (IJISA), 5(5), 1.
8 Piltan, F., Nabaee, A., Ebrahimi, M., & Bazregar, M. (2013). Design robust fuzzy sliding mode control technique for robot manipulator systems with modeling uncertainties. International Journal of Information Technology and Computer Science (IJITCS), 5(8), 123.
9 Salehi, A., Piltan, F., Mousavi, M., Khajeh, A., & Rashidian, M. R. (2013). Intelligent Robust Feed-forward Fuzzy Feedback Linearization Estimation of PID Control with Application to Continuum Robot. International Journal of Information Engineering and Electronic Business (IJIEEB), 5(1), 1.
10 Ebrahimi, M. M., Piltan, F., Bazregar, M., & Nabaee, A. (2013). Intelligent Robust Fuzzy-Parallel Optimization Control of a Continuum Robot Manipulator. International Journal of Control and Automation, 6(3), 15-34.
11 Piltan, F., Rafaati, M. J., Khazaeni, F., Hosainpour, A., & Soltani, S. (2013). A Design High Impact Lyapunov Fuzzy PD-Plus-Gravity Controller with Application to Rigid Manipulator. International Journal of Information Engineering and Electronic Business (IJIEEB), 5(1), 17.
12 Piltan, F., Eram, M., Taghavi, M., Sadrnia, O. R., & Jafari, M. (2013). Nonlinear Fuzzy Model-base Technique to Compensate Highly Nonlinear Continuum Robot Manipulator. International Journal of Intelligent Systems and Applications (IJISA), 5(12), 135.
13 Piltan, F., Bazregar, M., Akbari, M., & Piran, M. (2013). Adjust the fuel ratio by high impact chattering free sliding methodology with application to automotive engine. International Journal of Hybrid Information Technology, 6(1), 13-24.
14 Piltan, F., Mansoorzadeh, M., Zare, S., Shahryarzadeh, F., & Akbari, M. (2013). Artificial tune of fuel ratio: Design a novel siso fuzzy backstepping adaptive variable structure control. International Journal of Electrical and Computer Engineering (IJECE), 3(2), 171-185.
15 Piltan, F., ShahryarZadeh, F., Mansoorzadeh, M., & Zare, S. (2013). Robust Fuzzy PD Method with Parallel Computed Fuel Ratio Estimation Applied to Automotive Engine. International Journal of Intelligent Systems and Applications (IJISA), 5(8), 83.
16 Jahed, A., Piltan, F., Rezaie, H., & Boroomand, B. (2013). Design Computed Torque Controller with Parallel Fuzzy Inference System Compensator to Control of Robot Manipulator. International Journal of Information Engineering & Electronic Business, 5(3).
17 Mirshekaran, M., Piltan, F., Esmaeili, Z., Khajeaian, T., & Kazeminasab, M. (2013). Design Sliding Mode Modified Fuzzy Linear Controller with Application to Flexible Robot Manipulator. International Journal of Modern Education and Computer Science (IJMECS), 5(10), 53.
18 Ebrahimi, M. M., Piltan, F., Bazregar, M., & Nabaee, A. (2013). Artificial Chattering Free on-line Modified Sliding Mode Algorithm: Applied in Continuum Robot Manipulator. International Journal of Information Engineering and Electronic Business (IJIEEB), 5(5), 57.
19 Piltan, F., Emamzadeh, S., Heidari, S., Zahmatkesh, S., & Heidari, K. (2013). Design Artificial Intelligent Parallel Feedback Linearization of PID Control with Application to Continuum Robot. International Journal of Engineering and Manufacturing, 3(2), 51-72.
20 Jalali, A., Piltan, F., Hashemzadeh, M., BibakVaravi, F., & Hashemzadeh, H. (2013). Design Parallel Linear PD Compensation by Fuzzy Sliding Compensator for Continuum Robot. International Journal of Information Technology and Computer Science (IJITCS), 5(12), 97.
21 Piltan, F., Hosainpour, A., Emamzadeh, S., Nazari, I., & Mirzaie, M. (2013). Design Sliding Mode Controller of with Parallel Fuzzy Inference System Compensator to Control of Robot Manipulator. IAES International Journal of Robotics and Automation (IJRA), 2(4), 149-162.
22 Moosavi, M., Eram, M., Khajeh, A., Mahmoudi, O., & Piltan, F. (2013). Design New Artificial Intelligence Base Modified PID Hybrid Controller for Highly Nonlinear System. International Journal of Advanced Science and Technology, 57.
23 Piltan, F., Jafari, M., Eram, M., Mahmoudi, O., & Sadrnia, O. R. (2013). Design Artificial Intelligence-Based Switching PD plus Gravity for Highly Nonlinear Second Order System. International Journal of Engineering and Manufacturing (IJEM), 3(1), 38.
24 Piltan, F., Zare, S., ShahryarZadeh, F., & Mansoorzadeh, M. (2013). Supervised Optimization of Fuel Ratio in IC Engine Based on Design Baseline Computed Fuel Methodology. International Journal of Information Technology and Computer Science (IJITCS), 5(4), 76.
25 Shamsodini, M., Piltan, F., Jafari, M., reza Sadrnia, O., & Mahmoudi, O. (2013). Design Modified Fuzzy Hybrid Technique: Tuning By GDO. International Journal of Modern Education and Computer Science (IJMECS), 5(8), 58.
26 Mansoorzadeh, M. (2013). Design Novel Model Reference Artificial Intelligence Based Methodology to Optimized Fuel Ratio in IC Engine. International Journal of Information Engineering and Electronic Business (IJIEEB), 5(2), 44.
27 Piltan, F., Bairami, M. A., Aghayari, F., & Rashidian, M. R. (2013). Stable Fuzzy PD Control with Parallel Sliding Mode Compensation with Application to Rigid Manipulator. International Journal of Information Technology and Computer Science (IJITCS), 5(7), 103.
28 Bazregar, M., Piltan, F., Akbari, M., & Piran, M. (2013). Management of Automotive Engine Based on Stable Fuzzy Technique with Parallel Sliding Mode Optimization. International Journal of Information Technology and Computer Science (IJITCS), 6(1), 101.
29 Piltan, F., Mehrara, S., Meigolinedjad, J., & Bayat, R. (2013). Design Serial Fuzzy Variable Structure Compensator for Linear PD Controller: Applied to Rigid Robot. International Journal of Information Technology and Computer Science (IJITCS), 5(11), 111.
30 Piltan, F., Badri, A., Meigolinedjad, J., & Keshavarz, M. (2013). Adaptive Artificial Intelligence Based Model Base Controller: Applied to Surgical Endoscopy Telemanipulator. International Journal of Intelligent Systems and Applications (IJISA), 5(9), 103.
31 Piltan, F., Bazregar, M., Akbari, M., & Piran, M. (2013). Management of Automotive Engine Based on Stable Fuzzy Technique with Parallel Sliding Mode Optimization. International Journal of Advances in Applied Sciences, 2(4), 171-184.
32 Piltan, F., Yarmahmoudi, M. H., Shamsodini, M., Mazlomian, E., & Hosainpour, A. (2012). PUMA-560 Robot Manipulator Position Computed Torque Control Methods Using MATLAB/SIMULINK and Their Integration into Graduate Nonlinear Control and MATLAB Courses. International Journal of Robotics and Automation, (3), 167-191.
33 Piltan, F., Hosainpour, A., Mazlomian, E., Shamsodini, M., & Yarmahmoudi, M. H. (2012). Online Tuning Chattering Free Sliding Mode Fuzzy Control Design: Lyapunov Approach. International Journal of Robotics and Automation, 3(3), 77-105.
34 Piltan, F., Nazari, I., Siamak, S., & Ferdosali, P. (2012). Methodology of FPGA-based mathematical error-based tuning sliding mode controller. International Journal of Control and Automation, 5(1), 89-118.
35 Piltan, F., Boroomand, B., Jahed, A., & Rezaie, H. (2012). Methodology of Mathematical Error-Based Tuning Sliding Mode Controller. International Journal of Engineering, 6(2), 96-117.
36 Piltan, F., Dialame, M., Zare, A., & Badri, A. (2012). Design Novel Lookup Table Changed Auto Tuning FSMC: Applied to Robot Manipulator. International Journal of Engineering, 6(1), 25-41.
37 Piltan, F., Mirzaei, M., Shahriari, F., Nazari, I., & Emamzadeh, S. (2012). Design Baseline Computed Torque Controller. International Journal of Engineering, 6(3), 129-141.
38 Piltan, F., Boroomand, B., Jahed, A., & Rezaie, H. (2012). Performance-Based Adaptive Gradient Descent Optimal Coefficient Fuzzy Sliding Mode Methodology. International Journal of Intelligent Systems and Applications (IJISA), 4(11), 40.
39 Piltan, F., Meigolinedjad, J., Mehrara, S., & Rahmdel, S. (2012). Evaluation Performance of 2nd Order Nonlinear System: Baseline Control Tunable Gain Sliding Mode Methodology. International Journal of Robotics and Automation, 3(3), 192-211.
40 Piltan, F., Aghayari, F., Rashidian, M. R., & Shamsodini, M. (2012). A New Estimate Sliding Mode Fuzzy Controller for Robotic Manipulator. International Journal of Robotics and Automation, 3(1), 45-58.
41 Piltan, F., Jahed, A., Rezaie, H., & Boroomand, B. (2012). Methodology of Robust Linear On-line High Speed Tuning for Stable Sliding Mode Controller: Applied to Nonlinear System. International Journal of Control and Automation, 5(3), 217-236.
42 Piltan, F., Akbari, M., Piran, M., & Bazregar, M. (2012). Design Model Free Switching Gain Scheduling Baseline Controller with Application to Automotive Engine. International Journal of Information Technology and Computer Science (IJITCS), 5(1), 65.
43 Piltan, F., Bayat, R., Aghayari, F., & Boroomand, B. (2012). Design Error-Based Linear Model-Free Evaluation Performance Computed Torque Controller. International Journal of Robotics and Automation, 3(3), 151-166.
44 Piltan, F., Siamak, S., Bairami, M. A., & Nazari, I. (2012). Gradient descent optimal chattering free sliding mode fuzzy control design: LYAPUNOV approach. International Journal of Advanced Science and Technology, 43, 73-90.
45 Piltan, F., Bayat, R., Mehara, S., & Meigolinedjad, J. (2012). GDO Artificial Intelligence-Based Switching PID Baseline Feedback Linearization Method: Controlled PUMA Workspace. International Journal of Information Engineering and Electronic Business (IJIEEB), 4(5), 17.
46 Piltan, F., & Haghighi, S. T. (2012). Design Gradient Descent Optimal Sliding Mode Control of Continuum Robots. IAES International Journal of Robotics and Automation (IJRA), 1(4), 175-189.
47 Piltan, F., Mehrara, S., Bayat, R., & Rahmdel, S. (2012). Design New Control Methodology of Industrial Robot Manipulator: Sliding Mode Baseline Methodology.
48 Al Mashhadany, Y. I., & MIEEE, H. (2012). ANFIS-Inverse-Controlled PUMA 560 Workspace Robot with Spherical Wrist. Procedia Engineering, 41, 700-709.
49 Piltan, F., Piran, M., Akbari, M., & Barzegar, M. (2012). Baseline Tuning Methodology Supervisory Sliding Mode Methodology: Applied to IC Engine. International Journal of Advances in Applied Sciences, 1(3), 116-124.
50 Seven Tir Ave, S. Effect of Rule Base on the Fuzzy-Based Tuning Fuzzy Sliding Mode Controller: Applied to 2 nd Order Nonlinear System.
51 Piltan, F., Bairami, M. A., Aghayari, F., & Allahdadi, S. (2011). Design adaptive artificial inverse dynamic controller: Design sliding mode fuzzy adaptive new inverse dynamic fuzzy controller. International Journal of Robotics and Automation (IJRA), 3(1), 13.
52 Piltan, F., Allahdadi, S., Mohammad, A. B., & Nasiri, H. (2011). Design Auto Adjust Sliding Surface Slope: Applied to Robot Manipulator. International Journal of Robotics and Automation, 3(1), 27-44.
53 Nashed, M. N. High Dynamic Performance of PMSM Drive Using MIMO Fuzzy Controller.
1 Google Scholar
2 CiteSeerX
3 refSeek
4 Scribd
5 SlideShare
6 PdfSR
1 T. R. Kurfess, Robotics and automation handbook: CRC, 2005.
2 J. J. E. Slotine and W. Li, Applied nonlinear control vol. 461: Prentice hall Englewood Cliffs, NJ,1991.
3 Piltan, F., et al., “Design sliding mode controller for robot manipulator with artificial tunable gain,”Canaidian Journal of pure and applied science, 5 (2): 1573-1579, 2011.
4 L. Cheng, et al., "Multi-agent based adaptive consensus control for multiple manipulators with kinematic uncertainties," 2008, pp. 189-194.
5 J. J. D'Azzo, et al., Linear control system analysis and design with MATLAB: CRC, 2003.
6 B. Siciliano and O. Khatib, Springer handbook of robotics: Springer-Verlag New York Inc, 2008.
7 I. Boiko, et al., "Analysis of chattering in systems with second-order sliding modes," IEEE Transactions on Automatic Control, vol. 52, pp. 2085-2102, 2007.
8 J. Wang, et al., "Indirect adaptive fuzzy sliding mode control: Part I: fuzzy switching," Fuzzy Sets and Systems, vol. 122, pp. 21-30, 2001.
9 Farzin Piltan, A. R. Salehi and Nasri B Sulaiman.,” Design artificial robust control of second order system based on adaptive fuzzy gain scheduling,” world applied science journal (WASJ), 13 (5):1085-1092, 2011
10 F. Piltan, et al., "Artificial Control of Nonlinear Second Order Systems Based on AFGSMC,"Australian Journal of Basic and Applied Sciences, 5(6), pp. 509-522, 2011.
11 Piltan, F., et al., “Design Artificial Nonlinear Robust Controller Based on CTLC and FSMC with Tunable Gain,” International Journal of Robotic and Automation, 2 (3): 205-220, 2011.
12 Piltan, F., et al., “Design Mathematical Tunable Gain PID-Like Sliding Mode Fuzzy Controller with Minimum Rule Base,” International Journal of Robotic and Automation, 2 (3): 146-156, 2011.
13 Piltan, F., et al., “Design of FPGA based sliding mode controller for robot manipulator,”International Journal of Robotic and Automation, 2 (3): 183-204, 2011.
14 Piltan, F., et al., “A Model Free Robust Sliding Surface Slope Adjustment in Sliding Mode Control for Robot Manipulator,” World Applied Science Journal, 12 (12): 2330-2336, 2011.
15 A. Vivas and V. Mosquera, "Predictive functional control of a PUMA robot," 2005.
16 D. Nguyen-Tuong, et al., "Computed torque control with nonparametric regression models," 2008,pp. 212-217.
17 V. Utkin, "Variable structure systems with sliding modes," Automatic Control, IEEE Transactions on, vol. 22, pp. 212-222, 2002.
18 R. A. DeCarlo, et al., "Variable structure control of nonlinear multivariable systems: a tutorial,"Proceedings of the IEEE, vol. 76, pp. 212-232, 2002.
19 K. D. Young, et al., "A control engineer's guide to sliding mode control," 2002, pp. 1-14.
20 O. Kaynak, "Guest editorial special section on computationally intelligent methodologies and sliding-mode control," IEEE Transactions on Industrial Electronics, vol. 48, pp. 2-3, 2001.
21 J. J. Slotine and S. Sastry, "Tracking control of non-linear systems using sliding surfaces, with application to robot manipulators†," International Journal of Control, vol. 38, pp. 465-492, 1983.
22 J. J. E. Slotine, "Sliding controller design for non-linear systems," International Journal of Control,vol. 40, pp. 421-434, 1984.
23 R. Palm, "Sliding mode fuzzy control," 2002, pp. 519-526.
24 C. C. Weng and W. S. Yu, "Adaptive fuzzy sliding mode control for linear time-varying uncertain systems," 2008, pp. 1483-1490.
25 M. Ertugrul and O. Kaynak, "Neuro sliding mode control of robotic manipulators," Mechatronics,vol. 10, pp. 239-263, 2000.
26 P. Kachroo and M. Tomizuka, "Chattering reduction and error convergence in the sliding-mode control of a class of nonlinear systems," Automatic Control, IEEE Transactions on, vol. 41, pp.1063-1068, 2002.
27 H. Elmali and N. Olgac, "Implementation of sliding mode control with perturbation estimation(SMCPE)," Control Systems Technology, IEEE Transactions on, vol. 4, pp. 79-85, 2002.
28 J. Moura and N. Olgac, "A comparative study on simulations vs. experiments of SMCPE," 2002,pp. 996-1000.
29 Y. Li and Q. Xu, "Adaptive Sliding Mode Control With Perturbation Estimation and PID Sliding Surface for Motion Tracking of a Piezo-Driven Micromanipulator," Control Systems Technology,IEEE Transactions on, vol. 18, pp. 798-810, 2010.
30 B. Wu, et al., "An integral variable structure controller with fuzzy tuning design for electro-hydraulic driving Stewart platform," 2006, pp. 5-945.
31 L. A. Zadeh, "Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic," Fuzzy Sets and Systems, vol. 90, pp. 111-127, 1997.
32 L. Reznik, Fuzzy controllers: Butterworth-Heinemann, 1997.
33 J. Zhou and P. Coiffet, "Fuzzy control of robots," 2002, pp. 1357-1364.
34 S. Banerjee and P. Y. Woo, "Fuzzy logic control of robot manipulator," 2002, pp. 87-88.
35 K. Kumbla, et al., "Soft computing for autonomous robotic systems," Computers and Electrical Engineering, vol. 26, pp. 5-32, 2000.
36 C. C. Lee, "Fuzzy logic in control systems: fuzzy logic controller. I," IEEE Transactions on systems, man and cybernetics, vol. 20, pp. 404-418, 1990.
37 R. J. Wai, et al., "Implementation of artificial intelligent control in single-link flexible robot arm,"2003, pp. 1270-1275.
38 R. J. Wai and M. C. Lee, "Intelligent optimal control of single-link flexible robot arm," Industrial Electronics, IEEE Transactions on, vol. 51, pp. 201-220, 2004.
39 M. B. Menhaj and M. Rouhani, "A novel neuro-based model reference adaptive control for a two link robot arm," 2002, pp. 47-52.
40 S. Mohan and S. Bhanot, "Comparative study of some adaptive fuzzy algorithms for manipulator control," International Journal of Computational Intelligence, vol. 3, pp. 303–311, 2006.
41 F. Barrero, et al., "Speed control of induction motors using a novel fuzzy sliding-mode structure,"Fuzzy Systems, IEEE Transactions on, vol. 10, pp. 375-383, 2002.
42 Y. C. Hsu and H. A. Malki, "Fuzzy variable structure control for MIMO systems," 2002, pp. 280-285.
43 Y. C. Hsueh, et al., "Self-tuning sliding mode controller design for a class of nonlinear control systems," 2009, pp. 2337-2342.
44 R. Shahnazi, et al., "Position control of induction and DC servomotors: a novel adaptive fuzzy PI sliding mode control," Energy Conversion, IEEE Transactions on, vol. 23, pp. 138-147, 2008.
45 C. C. Chiang and C. H. Wu, "Observer-Based Adaptive Fuzzy Sliding Mode Control of Uncertain Multiple-Input Multiple-Output Nonlinear Systems," 2007, pp. 1-6.
46 H. Temeltas, "A fuzzy adaptation technique for sliding mode controllers," 2002, pp. 110-115.
47 C. L. Hwang and S. F. Chao, "A fuzzy-model-based variable structure control for robot arms:theory and experiments," 2005, pp. 5252-5258.
48 C. G. Lhee, et al., "Sliding mode-like fuzzy logic control with self-tuning the dead zone parameters," Fuzzy Systems, IEEE Transactions on, vol. 9, pp. 343-348, 2002.
49 Lhee. C. G., J. S. Park, H. S. Ahn, and D. H. Kim, "Sliding-Like Fuzzy Logic Control with Selftuning the Dead Zone Parameters," IEEE International fuzzy systems conference proceeding,1999,pp.544-549.
50 X. Zhang, et al., "Adaptive sliding mode-like fuzzy logic control for high order nonlinear systems,"pp. 788-792.
51 M. R. Emami, et al., "Development of a systematic methodology of fuzzy logic modeling," IEEE Transactions on Fuzzy Systems, vol. 6, 1998.
52 H.K.Lee, K.Fms, "A Study on the Design of Self-Tuning Sliding Mode Fuzzy Controller. Domestic conference," IEEE Conference, 1994, vol. 4, pp. 212-218.
53 Z. Kovacic and S. Bogdan, Fuzzy controller design: theory and applications: CRC/Taylor & Francis, 2006.
54 F. Y. Hsu and L. C. Fu, "Nonlinear control of robot manipulators using adaptive fuzzy sliding mode control," 2002, pp. 156-161.
55 R. G. Berstecher, et al., "An adaptive fuzzy sliding-mode controller," Industrial Electronics, IEEE Transactions on, vol. 48, pp. 18-31, 2002.
56 V. Kim, "Independent joint adaptive fuzzy control of robot manipulator," 2002, pp. 645-652.
57 Y. Wang and T. Chai, "Robust adaptive fuzzy observer design in robot arms," 2005, pp. 857-862.
58 B. K. Yoo and W. C. Ham, "Adaptive control of robot manipulator using fuzzy compensator," Fuzzy Systems, IEEE Transactions on, vol. 8, pp. 186-199, 2002.
59 H. Medhaffar, et al., "A decoupled fuzzy indirect adaptive sliding mode controller with application to robot manipulator," International Journal of Modelling, Identification and Control, vol. 1, pp. 23-29, 2006.
60 Y. Guo and P. Y. Woo, "An adaptive fuzzy sliding mode controller for robotic manipulators,"Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on, vol. 33, pp.149-159, 2003.
61 C. M. Lin and C. F. Hsu, "Adaptive fuzzy sliding-mode control for induction servomotor systems,"Energy Conversion, IEEE Transactions on, vol. 19, pp. 362-368, 2004.
62 Iordanov, H. N., B. W. Surgenor, 1997. Experimental evaluation of the robustness of discrete sliding mode control versus linear quadratic control, IEEE Trans. On control system technology,5(2):254-260.
63 Harashima F., Hashimoto H., and Maruyama K, 1986. Practical robust control of robot arm using variable structure system, IEEE conference, P.P:532-539
64 Piltan, F., et al., “Design Adaptive Fuzzy Robust Controllers for Robot Manipulator,” World Applied Science Journal, 12 (12): 2317-2329, 2011.
Mr. Farzin Piltan
UPM - Malaysia
SSP.ROBOTIC@yahoo.com
Mr. N. Sulaiman
- Malaysia
Mr. Payman Ferdosali
- Iran
Mr. Mehdi Rashidi
- Iran
Mr. Zahra Tajpeikar
- Iran