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

Majid Moradi Zirkohi; Mohammad Mehdi Fateh; Mahdi Aliyari Shoorehdeli

doi:10.1007/s11633-013-0717-x

June 2013

Article Contents

Majid Moradi Zirkohi, Mohammad Mehdi Fateh and Mahdi Aliyari Shoorehdeli. Type-2 Fuzzy Control for a Flexible-joint Robot Using Voltage Control Strategy. International Journal of Automation and Computing, vol. 10, no. 3, pp. 242-255, 2013. doi: 10.1007/s11633-013-0717-x

Cite as: Majid Moradi Zirkohi, Mohammad Mehdi Fateh and Mahdi Aliyari Shoorehdeli. Type-2 Fuzzy Control for a Flexible-joint Robot Using Voltage Control Strategy. International Journal of Automation and Computing, vol. 10, no. 3, pp. 242-255, 2013. doi: 10.1007/s11633-013-0717-x

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

1.
Department of Electrical and Robotic Engineering, Shahrood University of Technology, Shahrood 3619995161, Iran;
2.
Department of Electrical Engineering, Khaje Nasir Toosi University of Technology, Tehran, Iran

Author Biography:

Mohammad Mehdi Fateh received the B. Sc. degree from Isfahan University of Technology, Iran in 1988, the M. Sc. de-gree in electrical engineering from Tarbiat Modares University, Iran in 1991, and the Ph.D. degree from Southampton Univer-sity, UK in 2001. He is a full professor at Department of Electrical and Robotic En-gineering, Shahrood University of Technol-ogy, Iran. His research interests include nonlinear control, fuzzy control, robotics, intelligent systems, mechatronics and automation. E-mail: mmfateh@sharoodut.ac.ir
Corresponding author: Majid Moradi Zirkohi

Received: 2012-06-27

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.
- Type-2 fuzzy controller,
- flexible-joint robots,
- voltage control strategy,
- particle swarm optimization (PSO),
- actuator

References

[1]	B. Brogliato, R. Ortega, R. Lozano. Global tracking con-trollers for flexible-joint manipulators: A comparative study. Automatica, vol. 31, no. 7, pp. 941-956, 1995.
[2]	M. O. Tokhi, A. K. M. Azad. Flexible Robot Manipulators: Modeling, Simulation and Control, London: The Institu-tion of Engineering and Technology, 2008.
[3]	J. OReilly, P. Kokotovic, H. K. Khalil. Singular Perturba-tion 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 Pro-ceedings 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 manipula-tors: Comments on two papers. Automatica, vol. 31, no. 4, pp. 585-590, 1995.
[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.
[7]	M. W. Spong. Modeling and control of elastic joint robots. Journal of Dynamic Systems, Measurement, and Control, vol. 109, pp. 310-319, 1987.
[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.
[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.
[10]	M. M. Fateh. Robust control of flexible-joint robots using voltage control strategy. Nonlinear Dynamics, vol. 67, no. 2, pp. 1525-1537, 2012.
[11]	M. M. Fateh. Robust voltage control of electrical manip-ulators 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 manip-ulators. 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.
[14]	L. Cheng, Z. G. Hou, M. Tan. Adaptive neural network tracking control for manipulators with uncertain kinemat-ics, dynamics and actuator model. Automatica, vol. 45, no. 10, pp. 2312-2318, 2009.
[15]	F. Karray, W. Gueaieb, S. Al-Sharhan. The hierarchical expert tuning of PID controllers using tools of soft comput-ing. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 32, no. 1, pp. 77-90, 2002.
[16]	M. M. Fateh, S. Fateh. A precise robust fuzzy control of robots using voltage control strategy. International Jour-nal of Automation and Computing, vol. 10, no. 1, pp. 64-72, 2013.
[17]	Q. Zhu, A. G. Song, T. P. Zhang, Y. Q. Yang. Fuzzy adap-tive control of delayed high order nonlinear systems. In-ternational Journal of Automation and Computing, vol. 9, no. 2, pp. 191-199, 2012.
[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 ap-plication to approximate reasoning-1. Information Sciences, vol. 8, no. 3, pp. 199-249, 1975.
[20]	D. R. Wu, W. W. Tan. Genetic learning and performance evaluation of interval type-2 fuzzy logic controllers. Engi-neering Applications of Artificial Intelligence, vol. 19, no. 8, pp. 829-841, 2006.
[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 ar-chitecture for autonomous mobile Robots. IEEE Transac-tions on Fuzzy Systems, vol. 12, no. 4, pp. 524-539, 2004.
[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 Confer-ence 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.
[26]	R. Poli, J. Kennedy, T. Blackwell. Particle swarm optimiza-tion. Swarm Intelligent, vol. 1, no. 1, pp. 33-57, 2007.
[27]	M. M. Fateh, M. M. Zirkohi. Adaptive impedance control of a hydraulic suspension system using particle swarm optimi-sation. Vehicle System Dynamics, vol. 49, no. 12, pp. 1951-1965, 2011.
[28]	M. W. Spong, S. Hutchinson, M. Vidyasagar. Robot Mod-eling and Control, New York: Wiley, 2006.
[29]	Q. L. Liang, J. M. Mendel. Interval type-2 fuzzy logic sys-tems: Theory and design. IEEE Transactions on Fuzzy Sys-tems, vol. 8, no. 5, pp. 535-550, 2000.
[30]	M. Biglarbegian, W. W. Melek, J. M. Mendel. On the sta-bility 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.
[31]	M. M. Fateh. Robust control of flexible-joint robots using voltage control strategy. Nonlinear Dynamics, vol. 67, no. 2, pp. 1525-1537, 2012.
[32]	M. M. Fateh. Robust fuzzy control of electrical manipula-tors. Journal Intelligent & Robotic Systems, vol. 60, no. 3-4, pp. 415-434, 2010.

Metrics

[1]	Hong-Tao Ye, Zhen-Qiang Li. PID Neural Network Decoupling Control Based on Hybrid Particle Swarm Optimization and Differential Evolution . International Journal of Automation and Computing, 2020, 17(6): 867-872. doi: 10.1007/s11633-015-0917-7
[2]	Ali Darvish Falehi. Optimal Design of Fuzzy-AGC Based on PSO & RCGA to Improve Dynamic Stability of Interconnected Multi-area Power Systems . International Journal of Automation and Computing, 2020, 17(4): 599-609. doi: 10.1007/s11633-017-1064-0
[3]	Atlas Khan, Yan-Peng Qu, Zheng-Xue Li. Convergence Analysis of a New MaxMin-SOMO Algorithm . International Journal of Automation and Computing, 2019, 16(4): 534-542. doi: 10.1007/s11633-016-0996-0
[4]	Xian-Xia Zhang, Zhi-Qiang Fu, Shao-Yuan Li, Tao Zou, Bing Wang. A Time/Space Separation Based 3D Fuzzy Modeling Approach for Nonlinear Spatially Distributed Systems . International Journal of Automation and Computing, 2018, 15(1): 52-65. doi: 10.1007/s11633-017-1080-0
[5]	Guo-Han Lin, Jing Zhang, Liu Zhao-Hua. Hybrid Particle Swarm Optimization with Differential Evolution for Numerical and Engineering Optimization . International Journal of Automation and Computing, 2018, 15(1): 103-114. doi: 10.1007/s11633-016-0990-6
[6]	Sulaiman Ayobami Lawal, Jie Zhang. Actuator Fault Monitoring and Fault Tolerant Control in Distillation Columns . International Journal of Automation and Computing, 2017, 14(1): 80-92. doi: 10.1007/s11633-016-1037-8
[7]	Jia-Can Geng, Zhe Cui, Xing-Sheng Gu. Scatter Search Based Particle Swarm Optimization Algorithm for Earliness/Tardiness Flowshop Scheduling with Uncertainty . International Journal of Automation and Computing, 2016, 13(3): 285-295. doi: 10.1007/s11633-016-0964-8
[8]	Zhen-Guo Liu, Jin-Ming Huang. A New Adaptive Tracking Control Approach for Uncertain Flexible Joint Robot System . International Journal of Automation and Computing, 2015, 12(5): 559-566. doi: 10.1007/s11633-015-0898-6
[9]	Zhong-Da Tian, Xian-Wen Gao, Bi-Lian Gong, Tong Shi. Time-delay Compensation Method for Networked Control System Based on Time-delay Prediction and Implicit PIGPC . International Journal of Automation and Computing, 2015, 12(6): 648-656. doi: 10.1007/s11633-015-0897-7
[10]	Alessandro di Gaeta, Umberto Montanaro. Application of a Robust Model Reference Adaptive Control Algorithm to a Nonlinear Automotive Actuator . International Journal of Automation and Computing, 2014, 11(4): 377-391. doi: 10.1007/s11633-014-0803-8
[11]	Yu-Yan Zhang, Jun-Ling Zhang, Xiao-Yuan Luo, Xin-Ping Guan. Sensor/Actuator Faults Detection for Networked Control Systems via Predictive Control . International Journal of Automation and Computing, 2013, 10(3): 173-180. doi: 10.1007/s11633-013-0710-4
[12]	Meriem Benbrahim, Najib Essounbouli, Abdelaziz Hamzaoui, Ammar Betta. Adaptive Type-2 Fuzzy Sliding Mode Controller for SISO Nonlinear Systems Subject to Actuator Faults . International Journal of Automation and Computing, 2013, 10(4): 335-342. doi: 10.1007/s11633-013-0729-6
[13]	Mohammad Mehdi Fateh, Sara Fateh. A Precise Robust Fuzzy Control of Robots Using Voltage Control Strategy . International Journal of Automation and Computing, 2013, 10(1): 64-72 . doi: 10.1007/s11633-013-0697-x
[14]	Daniel M. Wonohadidjojo, Ganesh Kothapalli, Mohammed Y. Hassan. Position Control of Electro-hydraulic Actuator System Using Fuzzy Logic Controller Optimized by Particle Swarm Optimization . International Journal of Automation and Computing, 2013, 10(3): 181-193. doi: 10.1007/s11633-013-0711-3
[15]	Xin-Quan Zhang, Jun Zhao. L₂-gain Analysis and Anti-windup Design of Discrete- time Switched Systems with Actuator Saturation . International Journal of Automation and Computing, 2012, 9(4): 369-377. doi: 10.1007/s11633-012-0657-x
[16]	Xue-Li Wu, Xiao-Jing Wu, Xiao-Yuan Luo, Quan-Min Zhu. Design of Observer-based Adaptive Controller for Nonlinear Systems with Unmodeled Dynamics and Actuator Dead-zone . International Journal of Automation and Computing, 2011, 8(2): 201-208. doi: 10.1007/s11633-011-0574-4
[17]	Qiang Lu, Ping Luo. A Learning Particle Swarm Optimization Algorithm for Odor Source Localization . International Journal of Automation and Computing, 2011, 8(3): 371-380. doi: 10.1007/s11633-011-0594-0
[18]	Muhammad Ilyas Menhas, Ling Wang, Min-Rui Fei, Cheng-Xi Ma. Coordinated Controller Tuning of a Boiler Turbine Unit with New Binary Particle Swarm Optimization Algorithm . International Journal of Automation and Computing, 2011, 8(2): 185-192. doi: 10.1007/s11633-011-0572-6
[19]	Tao Tao, Yuan-Chang Liang, Minoru Taya. Bio-inspired Actuating System for Swimming Using Shape Memory Alloy Composites . International Journal of Automation and Computing, 2006, 3(4): 366-373. doi: 10.1007/s11633-006-0366-4
[20]	Jian-Lin Wei, Ji-Hong Wang, Q. H. Wu, Nan Lu. Power System Aggregate Load Area Modelling by Particle Swarm Optimization . International Journal of Automation and Computing, 2005, 2(2): 171-178. doi: 10.1007/s11633-005-0171-5

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

Cite This Article

PDF

XML

Entire Issue PDF

用微信扫码二维码

分享至好友和朋友圈

Metrics

Abstract Views (5996) PDF downloads (1967) Citations (0)

Related Articles

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

1. Department of Electrical and Robotic Engineering, Shahrood University of Technology, Shahrood 3619995161, Iran;

2. Department of Electrical Engineering, Khaje Nasir Toosi University of Technology, Tehran, Iran

Corresponding author: Majid Moradi Zirkohi

Received: 2012-06-27

Key words:

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.

HTML

Reference (32)

[1]	B. Brogliato, R. Ortega, R. Lozano. Global tracking con-trollers for flexible-joint manipulators: A comparative study. Automatica, vol. 31, no. 7, pp. 941-956, 1995.
[2]	M. O. Tokhi, A. K. M. Azad. Flexible Robot Manipulators: Modeling, Simulation and Control, London: The Institu-tion of Engineering and Technology, 2008.
[3]	J. OReilly, P. Kokotovic, H. K. Khalil. Singular Perturba-tion 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 Pro-ceedings 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 manipula-tors: Comments on two papers. Automatica, vol. 31, no. 4, pp. 585-590, 1995.
[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.
[7]	M. W. Spong. Modeling and control of elastic joint robots. Journal of Dynamic Systems, Measurement, and Control, vol. 109, pp. 310-319, 1987.
[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.
[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.
[10]	M. M. Fateh. Robust control of flexible-joint robots using voltage control strategy. Nonlinear Dynamics, vol. 67, no. 2, pp. 1525-1537, 2012.
[11]	M. M. Fateh. Robust voltage control of electrical manip-ulators 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 manip-ulators. 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.
[14]	L. Cheng, Z. G. Hou, M. Tan. Adaptive neural network tracking control for manipulators with uncertain kinemat-ics, dynamics and actuator model. Automatica, vol. 45, no. 10, pp. 2312-2318, 2009.
[15]	F. Karray, W. Gueaieb, S. Al-Sharhan. The hierarchical expert tuning of PID controllers using tools of soft comput-ing. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 32, no. 1, pp. 77-90, 2002.
[16]	M. M. Fateh, S. Fateh. A precise robust fuzzy control of robots using voltage control strategy. International Jour-nal of Automation and Computing, vol. 10, no. 1, pp. 64-72, 2013.
[17]	Q. Zhu, A. G. Song, T. P. Zhang, Y. Q. Yang. Fuzzy adap-tive control of delayed high order nonlinear systems. In-ternational Journal of Automation and Computing, vol. 9, no. 2, pp. 191-199, 2012.
[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 ap-plication to approximate reasoning-1. Information Sciences, vol. 8, no. 3, pp. 199-249, 1975.
[20]	D. R. Wu, W. W. Tan. Genetic learning and performance evaluation of interval type-2 fuzzy logic controllers. Engi-neering Applications of Artificial Intelligence, vol. 19, no. 8, pp. 829-841, 2006.
[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 ar-chitecture for autonomous mobile Robots. IEEE Transac-tions on Fuzzy Systems, vol. 12, no. 4, pp. 524-539, 2004.
[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 Confer-ence 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.
[26]	R. Poli, J. Kennedy, T. Blackwell. Particle swarm optimiza-tion. Swarm Intelligent, vol. 1, no. 1, pp. 33-57, 2007.
[27]	M. M. Fateh, M. M. Zirkohi. Adaptive impedance control of a hydraulic suspension system using particle swarm optimi-sation. Vehicle System Dynamics, vol. 49, no. 12, pp. 1951-1965, 2011.
[28]	M. W. Spong, S. Hutchinson, M. Vidyasagar. Robot Mod-eling and Control, New York: Wiley, 2006.
[29]	Q. L. Liang, J. M. Mendel. Interval type-2 fuzzy logic sys-tems: Theory and design. IEEE Transactions on Fuzzy Sys-tems, vol. 8, no. 5, pp. 535-550, 2000.
[30]	M. Biglarbegian, W. W. Melek, J. M. Mendel. On the sta-bility 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.
[31]	M. M. Fateh. Robust control of flexible-joint robots using voltage control strategy. Nonlinear Dynamics, vol. 67, no. 2, pp. 1525-1537, 2012.
[32]	M. M. Fateh. Robust fuzzy control of electrical manipula-tors. Journal Intelligent & Robotic Systems, vol. 60, no. 3-4, pp. 415-434, 2010.

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

Abstract

References

Metrics

Related Articles

通讯作者: 陈斌, bchen63@163.com

Metrics