Design Discrete Robust Adaptive Fuzzy Control for Asymptotic Tracking of Articulated Robot Manipulator
Subject Areas : Electronics Engineering
moslem zarei
1
,
siamak azargoshasb
2
,
najmeh cheraghi shirazi
3
1 - islamic azad university , bushehr
2 - Yasouj University
3 - department of electrical engineering, bushehr branch, islamic azad university, bushehr, iran
Keywords: Asymptotic tracking, Discrete time control, robot manipulators, Voltage Control Strategy, adaptive fuzzy estimator,
Abstract :
Robot manipulators are nonlinear multivariable systems with high couplings and various uncertainties. Although, adaptive and robust control methods are suggested to overcome the uncertainties including parametric uncertainty, un-modeled dynamics, external disturbances and discretization error, they face many challenges because of the complexity in robot dynamics. A fuzzy system can be used as a universal approximator for any nonlinear system. This feature has been efficiently used to design the adaptive fuzzy controllers. Adaptive fuzzy control systems are designed based on guaranteeing stability. Since practical implementation of the control law is carried out using digital processors, designing a discrete-time adaptive fuzzy controller for robot manipulators based on the voltage control strategy and proposed control systems stability analysis is suggested in this paper. In this paper, a new method is developed for compensating the approximation error of the fuzzy system which does not needed integration of tracking error. Moreover, the proposed discrete-time adaptive fuzzy with position feedback control law requires feedbacks of joint positions only. On the other hand, the fuzzy system approximation error and the discretization error are well compensated for asymptotic tracking of the desired path. The proposed robust Adaptive Fuzzy control law is simulated on an articulated robot. The simulation results show that the tracking error is negligible and the value of the second joint tracking error with the highest error at the end point of the simulation time is about radians. The parameters are well matched and the motors behave well under the maximum allowable voltage.
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[2] P. Francesco and G. Oaplo,”An Example o Collaborative Robot for Automotive and General Industry Application ”, Procedia Manufacturing, Vol. 11, pp. 338-345, 2017.
[3] Y. Guotao, Z. Zhenghe, G. Hu, L. Zhenfeng, Y. Huashan and L. Lei,”Flexible Punching System using Industrial Robots for Automotive Panels”,Robotics and Computer-Integrated Manufacturing, Vol. 52, pp. 92-99, 2018.
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[6] O. Akhrif, F. A. Okou, L. A. Dessaint and R. Champagne, “Application of a multivariable feedback linearization scheme for rotor angle stability and voltage regulation of power systems”, IEEE Transaction on power systems, Vol. 14, No. 2, pp. 620-628, 1999.
[7] G. Zheng, Y. zhou and M. Ju, “Robust control of a silicone soft robot using neural networks”, ISA Transactions, Vol. 100, pp. 38-45, 2020.
[8] Y. Zhou, H. Hu, L. Xia and Y. Chen, “A distributed approach to robust control of multi-robot systems”, Automatica, Vol. 98, pp. 1-13, 2018.
[9] Fateh M.M. and Soltanpour M. R, “Robust task-space control of robot manipulators under imperfect transportation of control space”, International Journal of Innovative Computing, Information and Control,Vol. 5, No. 11, pp. 3949-3960, 2009.
[10] R. Qi and M. A Brdys, “Indirect adaptive fuzzy control for nonlinear systems with online modeling”, in: Proc. Internat. Conf. Control, Glasgow,Scotland, 2006,pp.23-28.
[11] R. Subramaniam, D. Song and Y. H. Joo, ” T-S Fuzzy Based Sliding Mode Controller Design for Discrete Nonlinear Model and its Applications ”, Information Siences, Vol. 519, pp. 183–199, 2020.
[12] T. K. J. Koo, “Model reference adaptive fuzzy control of robot manipulator”, IEEE International Conference on Systems, Man and Cybernetics, Vol. 1, pp. 424-429,1995.
[13] M. Bahita and K. Belarbi, “ Model Reference Neural-Fuzzy Adaptive Control of the Concentration in a Chemical Reactor (CSTR)”, IFAC-Papers Online, Vol. 49, No. 29, pp. 158–162, 2016.
[14] M. M. Fateh, “On the voltage based control of electrical manipulators”, International Journal of Control”, Automation and System, Vol. 6, No.5, pp. 702–712, 2008.
[15] K. Yi, J. Han, X. Liang and Y. He, “Contact Transition Control with Acceleration Feedback Enhancement for a Quadrotor”, ISA Transactions, vol.109,pp.288-294, March 2021.
[16] V. Helma, M. Goubej and O. Jezek, “ Acceleration Feedback in PID Controlled Elastic Drive Systems”, IFAC-Papers Online, Vol. 51, No. 4, pp. 214–219, 2018.
[17] R. Ortega and M. W. Spong, “Adaptive motion control of rigid robots: a tutorial”, Proceedings of the 27th conference on decision and control, 1988, pp. 1575-1584.
[18] M. M. Fateh, “Robust control of electrical manipulators by joint acceleration”, International Journal of Innovative Computing, Information and Control, Vol. 6, No. 12, pp. 5501-5510, 2010.
[19] M. M. Fateh, “Robust control of electrical manipulators by reducing the effects of uncertainties”, World Applied Sciences Journal, Vol. 7, Special Issue, pp.161–167, 2009.
[20] M. M. Fateh, “Robust fuzzy control of electrical manipulators”, Journal of Intelligent and Robotic Systems, Vol. 60, No. 3, pp. 415-434, 2010.
[21] M. R. Soltanpour and M. M. Fateh, “Adaptive robust tracking control of robot manipulators in the task-space under uncertainties”, Australian Journal of Basic and Applied Sciences, Vol. 3, No. 1, pp. 308–322, 2009.
[22] L. X. Wang, “Adaptive fuzzy systems and control”, Prentice Hall, 1994.
[23] Z. Qu and D. M. Dawson, “Robust tracking control of robot manipulators”, IEEE Press, Inc., New York, 1996.
[24] M. M. Fateh, “Robust control of flexible-joint robots using voltage control strategy”, Nonlinear Dynamics, Vol. 67, No. 2, pp.1525–1537, 2012.
[25] M. M. Fateh and S. Khorashadizadeh, “Robust control of electrically driven robots by adaptive fuzzy estimation of uncertainty,” Nonlinear Dynamics (ND), Vol. 63, No. 4, pp. 1465-1477, 2012.
_||_[1] M. W. Spong and M. Vidyasagar, “Robot dynamic and control”, Wiley, New York, 1989.
[2] P. Francesco and G. Oaplo,”An Example o Collaborative Robot for Automotive and General Industry Application ”, Procedia Manufacturing, Vol. 11, pp. 338-345, 2017.
[3] Y. Guotao, Z. Zhenghe, G. Hu, L. Zhenfeng, Y. Huashan and L. Lei,”Flexible Punching System using Industrial Robots for Automotive Panels”,Robotics and Computer-Integrated Manufacturing, Vol. 52, pp. 92-99, 2018.
[4] K. Ogata, “Discrete-Time Control Systems”, Prentice-Hall, NJ, 1987.
[5] J.S.H. Tsai, C.M. Chen and L.S Shieh,”Digital Modelling Ideal State reconstructor and Control for Time-Delay Sampled-Data systems”, Applied Mathematical Modelling, Vol. 15, pp. 576-585,1991.
[6] O. Akhrif, F. A. Okou, L. A. Dessaint and R. Champagne, “Application of a multivariable feedback linearization scheme for rotor angle stability and voltage regulation of power systems”, IEEE Transaction on power systems, Vol. 14, No. 2, pp. 620-628, 1999.
[7] G. Zheng, Y. zhou and M. Ju, “Robust control of a silicone soft robot using neural networks”, ISA Transactions, Vol. 100, pp. 38-45, 2020.
[8] Y. Zhou, H. Hu, L. Xia and Y. Chen, “A distributed approach to robust control of multi-robot systems”, Automatica, Vol. 98, pp. 1-13, 2018.
[9] Fateh M.M. and Soltanpour M. R, “Robust task-space control of robot manipulators under imperfect transportation of control space”, International Journal of Innovative Computing, Information and Control,Vol. 5, No. 11, pp. 3949-3960, 2009.
[10] R. Qi and M. A Brdys, “Indirect adaptive fuzzy control for nonlinear systems with online modeling”, in: Proc. Internat. Conf. Control, Glasgow,Scotland, 2006,pp.23-28.
[11] R. Subramaniam, D. Song and Y. H. Joo, ” T-S Fuzzy Based Sliding Mode Controller Design for Discrete Nonlinear Model and its Applications ”, Information Siences, Vol. 519, pp. 183–199, 2020.
[12] T. K. J. Koo, “Model reference adaptive fuzzy control of robot manipulator”, IEEE International Conference on Systems, Man and Cybernetics, Vol. 1, pp. 424-429,1995.
[13] M. Bahita and K. Belarbi, “ Model Reference Neural-Fuzzy Adaptive Control of the Concentration in a Chemical Reactor (CSTR)”, IFAC-Papers Online, Vol. 49, No. 29, pp. 158–162, 2016.
[14] M. M. Fateh, “On the voltage based control of electrical manipulators”, International Journal of Control”, Automation and System, Vol. 6, No.5, pp. 702–712, 2008.
[15] K. Yi, J. Han, X. Liang and Y. He, “Contact Transition Control with Acceleration Feedback Enhancement for a Quadrotor”, ISA Transactions, vol.109,pp.288-294, March 2021.
[16] V. Helma, M. Goubej and O. Jezek, “ Acceleration Feedback in PID Controlled Elastic Drive Systems”, IFAC-Papers Online, Vol. 51, No. 4, pp. 214–219, 2018.
[17] R. Ortega and M. W. Spong, “Adaptive motion control of rigid robots: a tutorial”, Proceedings of the 27th conference on decision and control, 1988, pp. 1575-1584.
[18] M. M. Fateh, “Robust control of electrical manipulators by joint acceleration”, International Journal of Innovative Computing, Information and Control, Vol. 6, No. 12, pp. 5501-5510, 2010.
[19] M. M. Fateh, “Robust control of electrical manipulators by reducing the effects of uncertainties”, World Applied Sciences Journal, Vol. 7, Special Issue, pp.161–167, 2009.
[20] M. M. Fateh, “Robust fuzzy control of electrical manipulators”, Journal of Intelligent and Robotic Systems, Vol. 60, No. 3, pp. 415-434, 2010.
[21] M. R. Soltanpour and M. M. Fateh, “Adaptive robust tracking control of robot manipulators in the task-space under uncertainties”, Australian Journal of Basic and Applied Sciences, Vol. 3, No. 1, pp. 308–322, 2009.
[22] L. X. Wang, “Adaptive fuzzy systems and control”, Prentice Hall, 1994.
[23] Z. Qu and D. M. Dawson, “Robust tracking control of robot manipulators”, IEEE Press, Inc., New York, 1996.
[24] M. M. Fateh, “Robust control of flexible-joint robots using voltage control strategy”, Nonlinear Dynamics, Vol. 67, No. 2, pp.1525–1537, 2012.
[25] M. M. Fateh and S. Khorashadizadeh, “Robust control of electrically driven robots by adaptive fuzzy estimation of uncertainty,” Nonlinear Dynamics (ND), Vol. 63, No. 4, pp. 1465-1477, 2012.