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Artificial Control of PUMA Robot Manipulator: A-Review of Fuzzy Inference Engine and Application to Classical Controller
Farzin Piltan, SH. Tayebi HAGHIGHI, N. Sulaiman, Iman Nazari, Sobhan Siamak
Pages - 401 - 425     |    Revised - 01-11-2011     |    Published - 15-12-2011
Volume - 2   Issue - 5    |    Publication Date - November / December 2011  Table of Contents
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KEYWORDS
PUMA Robot Manipulator, Classical Controller, Artificial Intelligence Controller, Fuzzy logic Theory
ABSTRACT
One of the most important challenges in the field of robotics is robot manipulators control with acceptable performance, because these systems are multi-input multi-output (MIMO), nonlinear and uncertainty. Presently, robot manipulators are used in different (unknown and/or unstructured) situation consequently caused to provide complicated systems, as a result strong mathematical theory are used in new control methodologies to design nonlinear robust controller with acceptable performance (e.g., minimum error, good trajectory, disturbance rejection). Classical and non-classical methods are two main categories of robot manipulators control, where the conventional (classical) control theory uses the classical method and the non-classical control theory (e.g., fuzzy logic, neural network, and neuro fuzzy) uses the artificial intelligence methods. However both of conventional and artificial intelligence theories have applied effectively in many areas, but these methods also have some limitations. This paper is focused on review of fuzzy logic controller and applied to PUMA robot manipulator.
CITED BY (69)  
1 Piltan, F., Yekband, S., Mirzaie, R., Soltani, S., Sulaiman, N., & Jalali, A. (2015). Design Active Robot Controller for Dental Automation. International Journal of U-& E-Service, Science & Technology, 8(4).
2 Sahamijoo, G., Avatefipour, O., Nasrabad, M. R. S., Taghavi, M., & Piltan, F. (2015). Research on Minimum Intelligent Unit for Flexible Robot.
3 Mohammed, A. M., & Li, S. (2015). Dynamic Neural Networks for Kinematic Redundancy Resolution of Parallel Stewart Platforms.
4 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.
5 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.
6 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.
7 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.
8 Wall, D. G., Economou, J., Goyder, H., Knowles, K., Silson, P., & Lawrance, M. (2014). Mobile robot arm trajectory generation for operation in confined environments. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 0959651814559760.
9 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.
10 Hatamleh, K. S., Al-Shabi, M., Khasawneh, Q. A., & Al-Asal, M. A. (2014, November). Application of SMC and NLFC Into a PRRR Robotic Arm. In ASME 2014 International Mechanical Engineering Congress and Exposition (pp. V04AT04A040-V04AT04A040). American Society of Mechanical Engineers.
11 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.
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15 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.
16 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.
17 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.
18 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.
19 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.
20 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.
21 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).
22 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.
23 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.
24 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.
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26 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.
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28 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.
29 Jalali, A., Piltan, F., Hashemzadeh, H., Hasiri, A., & Hashemzadeh, M. (2013). Design Novel Soft Computing Backstepping Controller with Application to Nonlinear Dynamic Uncertain System. International Journal of Intelligent Systems and Applications (IJISA), 5(10), 93.
30 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.
31 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.
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39 Rakhodaei, H. (2013). Design and analysis of a 9 DOF: hybrid parallel robot (Doctoral dissertation, University of Birmingham).
40 Fardoun, H. M., Mashat, A. S., & Gonzaléz, L. C. (2013). Emergency Alert Management by means of Distributed User Interfaces. Distributed User Interfaces, 31.
41 Esmaili, P., & Esmaili, P. (2013). Modeling of two cooperative manipulators to handle an object. International Journal of Engineering, 2(1).
42 Brusic, V. (2013).Artificial intelligence in bioinformatics.
43 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.
44 Piltan, F., Emamzadeh, S., Hivand, Z., Shahriyari, F., & Mirazaei, M. (2012). PUMA-560 Robot Manipulator Position Sliding Mode Control Methods Using MATLAB/SIMULINK and Their Integration into Graduate/Undergraduate Nonlinear Control, Robotics and MATLAB Courses. International Journal of Robotics and Automation, 3(3), 106-150.
45 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.
46 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.
47 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.
48 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.
49 Piltan, F., Keshavarz, M., Badri, A., & Zargari, A. (2012). Design Novel Nonlinear Controller Applied to RobotManipulator: Design New Feedback Linearization Fuzzy Controller with Minimum Rule Base Tuning Method. International Journal of Robotics and Automation, 3(1), 1-12.
50 Piltan, F., Mirzaei, M., Shahriari, F., Nazari, I., & Emamzadeh, S. (2012). Design Baseline Computed Torque Controller. International Journal of Engineering, 6(3), 129-141.
51 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.
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53 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.
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57 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.
58 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.
59 Piltan, F., Mehrara, S., Bayat, R., & Rahmdel, S. (2012). Design New Control Methodology of Industrial Robot Manipulator: Sliding Mode Baseline Methodology.
60 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.
61 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.
62 Seven Tir Ave, S. Design New Control Methodology of Industrial Robot Manipulator: Sliding Mode Baseline Methodology.
63 Seven Tir Ave, S. Effect of Rule Base on the Fuzzy-Based Tuning Fuzzy Sliding Mode Controller: Applied to 2 nd Order Nonlinear System.
64 Piltan, F., Sulaiman, N., Gavahian, A., Roosta, S., & Soltani, S. (2011). On line Tuning Premise and Consequence FIS: Design Fuzzy Adaptive Fuzzy Sliding Mode Controller Based on Lyaponuv Theory. International Journal of Robotics and Automation, 2(5), 381-400.
65 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.
66 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.
67 Seven Tir Ave, S. (2011). Artificial Robust Control of Robot Arm: Design a Novel SISO Backstepping Adaptive Lyapunov Based Variable Structure Control.
68 Seven Tir Ave, S. Artificial Robust Control of Robot Arm: Design a Novel SISO Backstepping Adaptive Lyapunov Based Variable Structure Control.
69 Chang, Y. L., & Yeh, C. K. Manipulator Synthesis Based on Workspace Analysis.
1 Google Scholar
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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 K. Ogata, Modern control engineering: Prentice Hall, 2009.
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 C. Wu, "Robot accuracy analysis based on kinematics," IEEE Journal of Robotics and Automation, vol. 2, pp. 171-179, 1986.
10 H. Zhang and R. P. Paul, "A parallel solution to robot inverse kinematics," 2002, pp. 1140-1145.
11 J. Kieffer, "A path following algorithm for manipulator inverse kinematics," 2002, pp. 475-480.
12 Z. Ahmad and A. Guez, "On the solution to the inverse kinematic problem(of robot)," 1990,pp. 1692-1697.
13 F. T. Cheng, et al., "Study and resolution of singularities for a 6-DOF PUMA manipulator,"Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, vol. 27, pp.332-343, 2002.
14 M. W. Spong and M. Vidyasagar, Robot dynamics and control: Wiley-India, 2009.
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 engineers 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 slidingmode 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 electrohydraulic 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 Self-tuning 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 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.
63 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
64 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.
65 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.
66 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.
67 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.
68 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.
69 Piltan, F., et al., “Design Adaptive Fuzzy Robust Controllers for Robot Manipulator,” World Applied Science Journal, 12 (12): 2317-2329, 2011.
70 B. S. R. Armstrong, "Dynamics for robot control: friction modeling and ensuring excitation during parameter identification," 1988.
71 C. L. Clover, "Control system design for robots used in simulating dynamic force and moment interaction in virtual reality applications," 1996.
72 K. R. Horspool, Cartesian-space Adaptive Control for Dual-arm Force Control Using Industrial Robots: University of New Mexico, 2003.
73 B. Armstrong, et al., "The explicit dynamic model and inertial parameters of the PUMA 560 arm," 2002, pp. 510-518.
74 P. I. Corke and B. Armstrong-Helouvry, "A search for consensus among model parameters reported for the PUMA 560 robot," 2002, pp. 1608-1613.
Mr. Farzin Piltan
UPM - Malaysia
SSP.ROBOTIC@yahoo.com
Mr. SH. Tayebi HAGHIGHI
- Iran
Mr. N. Sulaiman
- Malaysia
Mr. Iman Nazari
- Iran
Mr. Sobhan Siamak
- Iran