Volume 11 Number 4
August 2014
Article Contents
Hai-Gang Guo and Bao-Jie Zhang. Observer-based Variable Universe Adaptive Fuzzy Controller Without Additional Dynamic Order. International Journal of Automation and Computing, vol. 11, no. 4, pp. 418-425, 2014. doi: 10.1007/s11633-014-0808-3
Cite as: Hai-Gang Guo and Bao-Jie Zhang. Observer-based Variable Universe Adaptive Fuzzy Controller Without Additional Dynamic Order. International Journal of Automation and Computing, vol. 11, no. 4, pp. 418-425, 2014. doi: 10.1007/s11633-014-0808-3

Observer-based Variable Universe Adaptive Fuzzy Controller Without Additional Dynamic Order

  • Received: 2013-01-15
Fund Project:

This work was supported by National Natural Science Foundation of China (No. 61074044), and Basic and Cutting-edge Technology of Science and Technology Department of Henan Province (No. 092300410178).

  • A high-precision fuzzy controller, based on a state observer, is developed for a class of nonlinear single-input-single-output (SISO) systems with system uncertainties and external disturbances. The state observer is introduced to resolve the problem of the unavailability of state variables. Assisted by the observer, a variable universe fuzzy system is designed to approximate the ideal control law. Being auxiliary components, a robust control term and a state feedback control term are designed to suppress the influence of the lumped uncertainties and remove the observation error, respectively. Different from the existing results, no additional dynamic order is required for the control design. All the adaptive laws and the control law are built based on the Lyapunov synthesis approach, and the signals involved in the closed-loop system are guaranteed to be uniformly ultimately bounded. Simulation results performed on Duffing forced oscillation demonstrate the advantages of the proposed control scheme.
  • [1] L. X. Wang, J. M. Mendel. Fuzzy basis functions, universal approximation, and orthogonal least-squares learning. IEEE Transactions on Neural Networks, vol. 3, no. 5, pp. 807-814, 1992.
    [2] A. Boulkroune, M. Tadjine, M. M'Saad, M. Farza. Fuzzy adaptive controller for MIMO nonlinear systems with known and unknown control direction. Fuzzy Sets and Systems, vol. 161, no. 6, pp. 797-820, 2010.
    [3] H. F. Ho, Y. K. Wong, A. B. Rad. Robust fuzzy tracking control for robotic manipulators. Simulation Modelling Practice and Theory, vol. 15, no. 7, pp. 801-816, 2007.
    [4] Y. C. Chang. Adaptive fuzzy-based tracking control for nonlinear SISO systems via VSS and H approaches. IEEE Transactions on Fuzzy Systems, vol. 9, no. 2, pp. 278-292, 2001.
    [5] S. Purwar, I. N. Kar, A. N. Jha. Adaptive control of robot manipulators using fuzzy logic systems under actuator constraints. Fuzzy Sets and Systems, vol. 152, no. 3, pp. 651-664, 2005.
    [6] I. Rojas, H. Pomares, J. Gonzalez, L. J. Herrera, A. Guillen, F. Rojas, O. Valenzuela. Adaptive fuzzy controller: Application to the control of the temperature of a dynamic room in real time. Fuzzy Sets and Systems, vol. 157, no. 16, pp. 2241-2258, 2006.
    [7] M. Roopaei, M. Zolghadri Jahromi, R. John, T. C. Lin. Unknown nonlinear chaotic gyros synchronization using adaptive fuzzy sliding mode control with unknown dead-zone input. Communications in Nonlinear Science and Numerical Simulation, vol. 15, no. 9, pp. 2536-2545, 2010.
    [8] H. X. Li. Interpolation mechanism of fuzzy control. Science in China Series E-Technological Sciences, vol. 41, no. 3, pp. 312-320, 1998.
    [9] A. Sala, T. M. Guerra, R. Babuška. Perspectives of fuzzy systems and control. Fuzzy Sets and Systems, vol. 156, no. 3, pp. 432-444, 2005.
    [10] H. T. Yau, J. J. Yan. Adaptive sliding mode control of a high-precision ball-screw-driven stage. Nonlinear Analysis: Real World Applications, vol. 10, no. 3, pp. 1480-1489, 2009.
    [11] D. Driankov, H. Hellendoorn, R. Palm. Some research directions in fuzzy control. Theoretical Aspects of Fuzzy Control, John Wiley and Sons, Inc., New York, America, pp. 281-312, 1995.
    [12] A. Green, J. Z. Sasiadek. Heuristic design of a fuzzy controller for a flexible robot. IEEE Transactions on Control Systems Technology, vol. 14, no. 2, pp. 293-300, 2006.
    [13] A. Green, J. Z. Sasiadek. A new optimization method for fuzzy controller's design. Control Theory and Application, vol. 19, no. 2, pp. 279-283, 2002.
    [14] Z. K. Zhang, J. Chang. A fuzzy control algorithm with high controlling precision. Fuzzy Sets and Systems, vol. 140, no. 2, pp. 375-385, 2003.
    [15] H. X. Li. To see the success of fuzzy logic from mathematical essence of fuzzy control. Fuzzy Systems and Mathematics, vol. 9, no. 4, pp. 1-14, 1995. (in Chinese)
    [16] H. X. Li, Z. H. Miao, J. Y. Wang. Variable universe adaptive fuzzy control on the quadruple inverted pendulum. Science in China Series E-Technological Sciences, vol. 45, no. 2, pp. 213-224, 2002.
    [17] H. X. Li, J. Y. Wang, Y. B. Feng, Y. D. Gu. Hardware implementation of the quadruple inverted pendulum with single motor. Progress in Natural Science, vol. 14, no. 9, pp. 822-827, 2004.
    [18] W. Fang, C. S. Jiang. Adaptive variable universe fuzzy predictive control for aerospace vehicle. Control and Decision, vol. 23, no. 12, pp. 1373-1377, 1388, 2008. (in Chinese)
    [19] J. Wang, G. D. Qiao, B. Deng. H variable universe adaptive fuzzy control for chaotic system. Chaos, Solitons and Fractals, vol. 24, no. 4, pp. 1075-1086, 2005.
    [20] H. X. Li, Z. H. Miao, E. S. Lee. Variable universe stable adaptive fuzzy control of a nonlinear system. Computers and Mathematics with Applications vol. 44, no. 5, pp. 799-815, 2002.
    [21] J. Wang, Z. Zhang, H. Y. Li. Synchronization of FitzHugh-Nagumo systems in EES via H variable universe adaptive fuzzy control. Chaos, Solitons and Fractals, vol. 36, no. 5, pp. 1332-1339, 2008.
    [22] Y. F.Wang, C. S. Jiang. Direct adaptive fuzzy sliding mode control with variable universe for near space vehicle. Systems Engineering and Electronics, vol. 33, no. 3, pp. 633-637, 2011. (in Chinese).
    [23] S. C. Tong, H. X. Li, W. Wang. Observer-based adaptive fuzzy control for SISO nonlinear systems. Fuzzy Sets and Systems, vol. 148, no. 3, pp. 355-376, 2004.
    [24] Y. G. Leu, T. T. Lee, W. Y.Wang. Observer-based adaptive fuzzy-neural control for unknown nonlinear dynamical systems. IEEE Transactions on Systems, Man, and Cybernetics. Part B: Cybernetics, vol. 29, no. 5, pp. 583-591, 1999.
    [25] Y. G. Leu, W. Y. Wang, T. T. Lee. Observer-based direct adaptive fuzzy-neural control for nonaffine nonlinear systems. IEEE Transactions on Neural Networks, vol. 16, no. 4, pp. 853-861, 2005.
    [26] W. Y. Wang, C. Y. Cheng, Y. G. Leu. An online GA-based output-feedback direct adaptive fuzzy-neural controller for uncertain nonlinear systems. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 34, no. 1, pp. 334-345, 2004.
    [27] J. H. Park, G. T. Park, S. H. Kim, C. J. Moon. Outputfeedback control of uncertain nonlinear systems using a selfstructuring adaptive fuzzy observer. Fuzzy Sets and Systems, vol. 151, no. 1, pp. 21-42, 2005.
    [28] A. Boulkroune, M. Tadjine, M. MSaad, M. Farza. How to design a fuzzy adaptive controller based on observers for uncertain affine nonlinear systems. Fuzzy Sets and Systems, vol. 159, no. 8, pp. 926-948, 2008.
    [29] A. Boulkroune, M. MSaad. On the design of observer-based fuzzy adaptive controller for nonlinear systems with unknown control gain sign. Fuzzy Sets and Systems, vol. 201, pp. 71-85, 2012.
    [30] C. C. Kung, T. H. Chen. Observer-based indirect adaptive fuzzy sliding mode control with state variable filters for unknown nonlinear dynamical systems. Fuzzy Sets and Systems, vol. 155, no. 2, pp. 292-308, 2005.
    [31] J. Wang, Q. D. Qiao, B. Deng. Observer-based robust adaptive variable universe fuzzy control for chaotic system. Chaos, Solitons and Fractals, vol. 23, no. 3, pp. 1013-1032, 2005.
    [32] H. X. Li. Adaptive fuzzy controllers based on variable universe. Science in China Series E: Technological Sciences, vol. 42, no. 1, pp. 10-20, 1999.
    [33] H. G. Guo, H. X. Li, W. J. Zhao, Z. K. Song. Direct adaptive fuzzy sliding mode control with variable universe fuzzy switching term for a class of MIMO nonlinear systems. Mathematical Problems in Engineering, vol. 2012, Article ID 543039, pp. 1-21, 2012.
    [34] W. W. Shan, D. M. Jin, Y. Liang. Variable universe adaptive fuzzy logic controller CMOS analog chip implementation. Acta Electronica Sinica, vol. 37, no. 5, pp. 913-917, 2009. (in Chinese)
    [35] M. Chemachema. Output feedback direct adaptive neural network control for uncertain SISO nonlinear systems using a fuzzy estimator of the control error. Neural Networks, vol. 36, pp. 25-34, 2012.
    [36] J. H. Park, G. T. Park. Adaptive fuzzy observer with minimal dynamic order for uncertain nonlinear systems. IEE Proceedings: Control Theory and Application, vol. 150, no. 2, pp. 189-197, 2003.
    [37] P. A. Ioannou, J. Sun. Robust Adaptive Control, Prentice-Hall, Englewood Cliffs, NJ, pp. 126-134, 545-576, 1996.
    [38] H. G. Guo, H. X. Li, B. J. Zhang. Observer-based direct adaptive fuzzy controller with no additional dynamic order. ICIC Express Letters, vol. 7, no. 12, pp. 3183-3189, 2013.
    [39] Y. P. Pan, M. J. Er, D. P. Huang, Q. R. Wang. Adaptive fuzzy control with guaranteed convergence of optimal approximation error. IEEE Transactions on Fuzzy Systems, vol. 19, no. 5, pp. 807-818, 2011.
    [40] A. Boulkroune, M. Tadjine, M. MSaad, M. Farza. Adaptive fuzzy observer for uncertain nonlinear systems. Control and Intelligent Systems, vol. 39, no. 3, pp. 145, 2011.
    [41] J. Na, X. M. Ren, G. Herrmann, Z. Qiao. Adaptive neural dynamic surface control for servo systems with unknown dead-zone. Control Engineering Practice, vol. 19, no. 11, pp. 1328-1343, 2011.
    [42] A. Boulkroune, M. MSaad. A practical projective synchronization approach for uncertain chaotic systems with deadzone input. Communications in Nonlinear Science and Numerical Simulation, vol. 16, no. 11, pp. 4487-4500, 2011.
    [43] A. Boulkroune, M. MSaad. A fuzzy adaptive variablestructure control scheme for uncertain chaotic MIMO systems with sector nonlinearities and dead-zones. Expert Systems with Applications, vol. 38, no. 12, pp. 14744-14750, 2011.
    [44] Y. J. Liu, N. Zhou. Observer-based adaptive fuzzy-neural control for a class of uncertain nonlinear systems with unknown dead-zone input. ISA Transactions, vol. 49, no. 4, pp. 462-469, 2010.
  • 加载中
  • [1] Noussaiba Gasmi, Assem Thabet, Mohamed Aoun. New LMI Conditions for Reduced-order Observer of Lipschitz Discrete-time Systems: Numerical and Experimental Results . International Journal of Automation and Computing, 2019, 16(5): 644-654.  doi: 10.1007/s11633-018-1160-9
    [2] Chang-Fan Zhang, Yuan-Yuan Xiao, Jing He, Min Yan. Improvement of Electronic Line-shafting Control in Multi-axis Systems . International Journal of Automation and Computing, 2018, 15(4): 474-481.  doi: 10.1007/s11633-016-1031-1
    [3] Vineet Kumar, K.P. S. Rana, Jitendra Kumar, Puneet Mishra, Sreejith S Nair. A Robust Fractional Order Fuzzy P+Fuzzy I+Fuzzy D Controller for Nonlinear and Uncertain System . International Journal of Automation and Computing, 2017, 14(4): 474-488.  doi: 10.1007/s11633-016-0981-7
    [4] Radhia Garraoui,  Mouna Ben Hamed,  Lassâad Sbita. A Robust Optimization Technique Based on First Order Sliding Mode Approach for Photovoltaic Power Systems . International Journal of Automation and Computing, 2015, 12(6): 620-629.  doi: 10.1007/s11633-015-0902-1
    [5] Yi-Heng Wei,  Zhen-Yuan Sun,  Yang-Sheng Hu,  Yong Wang. On Fractional Order Adaptive Observer . International Journal of Automation and Computing, 2015, 12(6): 664-670.  doi: 10.1007/s11633-015-0929-3
    [6] Hamid Dahmani,  Mohammed Chadli,  Abdelhamid Rabhi,  Ahmed El Hajjaji. Detection of Impending Vehicle Rollover with Road Bank Angle Consideration Using a Robust Fuzzy Observer . International Journal of Automation and Computing, 2015, 12(1): 93-101.  doi: 10.1007/s11633-014-0836-z
    [7] Yong-Hong Lan, Wen-Jie Li, Yan Zhou, Yi-Ping Luo. Non-fragile Observer Design for Fractional-order One-sided Lipschitz Nonlinear Systems . International Journal of Automation and Computing, 2013, 10(4): 296-302.  doi: 10.1007/s11633-013-0724-y
    [8] Imen Haj Brahim, Maha Bouattour, Driss Mehdi, Mohamed Chaabane, Ghani Hashim. Sensor Faults Observer Design with H Performance for Non-linear T-S systems . International Journal of Automation and Computing, 2013, 10(6): 563-570.  doi: 10.1007/s11633-013-0754-5
    [9] Hassan A. Yousef, Mohamed Hamdy. Observer-based Adaptive Fuzzy Control for a Class of Nonlinear Time-delay Systems . International Journal of Automation and Computing, 2013, 10(4): 275-280.  doi: 10.1007/s11633-013-0721-1
    [10] State Observer Based Dynamic Fuzzy Logic System for a Class of SISO Nonlinear Systems . International Journal of Automation and Computing, 2013, 10(2): 118-124.  doi: 10.1007/s11633-013-0704-2
    [11] Khalid Jebari, Abdelaziz Bouroumi, Aziz Ettouhami. Fuzzy Genetic Sharing for Dynamic Optimization . International Journal of Automation and Computing, 2012, 9(6): 616-626 .  doi: 10.1007/s11633-012-0687-4
    [12] Qing Zhu, Ai-Guo Song, Tian-Ping Zhang, Yue-Quan Yang. Fuzzy Adaptive Control of Delayed High Order Nonlinear Systems . International Journal of Automation and Computing, 2012, 9(2): 191-199.  doi: 10.1007/s11633-012-0633-5
    [13] 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
    [14] Wei-Sheng Chen,  Rui-Hong Li,  Jing Li. Observer-based Adaptive Iterative Learning Control for Nonlinear Systems with Time-varying Delays . International Journal of Automation and Computing, 2010, 7(4): 438-446.  doi: 10.1007/s11633-010-0525-5
    [15] Shao-Cheng Tong,  Ning Sheng. Adaptive Fuzzy Observer Backstepping Control for a Class of Uncertain Nonlinear Systems with Unknown Time-delay . International Journal of Automation and Computing, 2010, 7(2): 236-246.  doi: 10.1007/s11633-010-0236-y
    [16] Lin-Na Zhou, Chun-Yu Yang, Qing-Ling Zhang. Observers for Descriptor Systems with Slope-restricted Nonlinearities . International Journal of Automation and Computing, 2010, 7(4): 472-478.  doi: 10.1007/s11633-010-0529-1
    [17] Xiao-Yuan Luo,  Zhi-Hao Zhu,  Xin-Ping Guan. Adaptive Fuzzy Dynamic Surface Control for Uncertain Nonlinear Systems . International Journal of Automation and Computing, 2009, 6(4): 385-390.  doi: 10.1007/s11633-009-0385-z
    [18] Fatima El Haoussi,  El Houssaine Tissir. Robust H Controller Design for Uncertain Neutral Systems via Dynamic Observer Based Output Feedback . International Journal of Automation and Computing, 2009, 6(2): 164-170.  doi: 10.1007/s11633-009-0164-x
    [19] Bin Zhou, Guang-Ren Duan, Yun-Li Wu. Parametric Approach for the Normal Luenberger Function Observer Design in Second-order Descriptor Linear Systems . International Journal of Automation and Computing, 2008, 5(2): 125-131.  doi: 10.1007/s11633-008-0125-9
    [20] Min-Ze Chen,  Qi Zhao,  Dong-Hua Zhou. A Robust Fault Detection Approach for Nonlinear Systems . International Journal of Automation and Computing, 2006, 3(1): 23-28.  doi: 10.1007/s11633-006-0023-y
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

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Abstract Views (5084) PDF downloads (1748) Citations (0)

Observer-based Variable Universe Adaptive Fuzzy Controller Without Additional Dynamic Order

Fund Project:

This work was supported by National Natural Science Foundation of China (No. 61074044), and Basic and Cutting-edge Technology of Science and Technology Department of Henan Province (No. 092300410178).

Abstract: A high-precision fuzzy controller, based on a state observer, is developed for a class of nonlinear single-input-single-output (SISO) systems with system uncertainties and external disturbances. The state observer is introduced to resolve the problem of the unavailability of state variables. Assisted by the observer, a variable universe fuzzy system is designed to approximate the ideal control law. Being auxiliary components, a robust control term and a state feedback control term are designed to suppress the influence of the lumped uncertainties and remove the observation error, respectively. Different from the existing results, no additional dynamic order is required for the control design. All the adaptive laws and the control law are built based on the Lyapunov synthesis approach, and the signals involved in the closed-loop system are guaranteed to be uniformly ultimately bounded. Simulation results performed on Duffing forced oscillation demonstrate the advantages of the proposed control scheme.

Hai-Gang Guo and Bao-Jie Zhang. Observer-based Variable Universe Adaptive Fuzzy Controller Without Additional Dynamic Order. International Journal of Automation and Computing, vol. 11, no. 4, pp. 418-425, 2014. doi: 10.1007/s11633-014-0808-3
Citation: Hai-Gang Guo and Bao-Jie Zhang. Observer-based Variable Universe Adaptive Fuzzy Controller Without Additional Dynamic Order. International Journal of Automation and Computing, vol. 11, no. 4, pp. 418-425, 2014. doi: 10.1007/s11633-014-0808-3
Reference (44)

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return