An adaptive PID controller for precisely angular DC motor control
Corressponding author's email:
badx@hcmute.edu.vnDOI:
https://doi.org/10.54644/jte.68.2022.1108Keywords:
Position Control, DC Motor, PID Controller, Intelligent Control, Nonlinear LearningAbstract
In industrial production as well as academic research, developing a precise automatic control system is one of the most concerned topics. Moreover, applicability of optimizing controllers with low-cost hardware is now still on going. However, time-varying uncertainties and external disturbances in the system dynamics are barriers in development of outstanding controllers. In this paper, we propose a simple-yet-efficient adaptive control method for accurate tracking-control problems of DC motors. Structure of the controller is designed based on a Proportional-Integral-Derivative (PID) control framework. For significantly improving quality of the control system, a nonlinear-spatial adaptation law is proposed to reasonably adjust the control parameters based on information of the control error acquired. The effectiveness of this method has been verified using proper theoretical analyses based on the Lyapunov approach. The possibility of the proposed controller was first carefully investigated on simulation environment. Comparative experiments were then performed on a real-time test rig and were analyzed to confirm the feasibility of the intelligent control method for practical applications.
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References
N. M. Tri, D. X. Ba, and K. K. Ahn, “A Gain-Adaptive Intelligent Nonlinear Control for an Electrohydraulic Rotary Actuator,” International Journal of Precision Engineering and Manufacturing, vol. 19, no. 5, pp. 665-673, 2018. DOI: https://doi.org/10.1007/s12541-018-0080-5
D. X. Ba, T. Q. Dinh, and K. K. Ahn, “An Integrated Intelligent Nonlinear Control Method for Pneumatic Artificial Muscle,” IEEE/ASME Trans. on Mechatronics, vol. 21, no. 4, 2016. DOI: https://doi.org/10.1109/TMECH.2016.2558292
M. FLIESS, and C. JOIN, “Model-free Control and Intelligent PID Controller: Toward a Possible Trivialization of Nonlinear Control,” 15th IFAC Symposium on System Identification, vol. 42, no. 10, pp. 1531-1550, 2009. DOI: https://doi.org/10.3182/20090706-3-FR-2004.00256
M. Dorigo, M. Birattari, and T. Stutzle, “Ant Colony Optimization,” IEEE Computational Intelligence Magazine, vol. 1, no. 4, pp. 28-39, 2006. DOI: https://doi.org/10.1109/CI-M.2006.248054
G. Z. TAN, Q. D. ZEN, and W. B. LI, “Intelligent PID controller based on ant system algorithm and fuzzy inference and its application to bionic artificial leg,” Journal of Central South University of Technology, vol. 11, no. 3, pp. 316 – 322, 2004. DOI: https://doi.org/10.1007/s11771-004-0065-7
S. Das, A. Biswas, S. Dasgupta, and A. Abraham, “Bacterial Foraging Optimization Algorithm: Theoretical Foundations, Analysis, and Applications,” Foundations of Computational Intelligence, vol. 3, SCI 203, pp. 23-55, 2009.
D. H. Kim, and J. H. Cho, “A Biological Inspired Intelligent PID Controller Tuning for AVR Systems,” International Journal of Control Automation and Systems, vol. 4, no. 5, pp. 624 – 636, 2006.
T. D. C. Thanh, and K. K. Ahn, “Nonlinear PID control to improve the control performance of 2 axes pneumatic artificial muscle manipulator using neural network,” Mechatronics, vol. 16, no. 9, pp. 577 – 587, 2006. DOI: https://doi.org/10.1016/j.mechatronics.2006.03.011
N. V. D. Hai and N. V. Thuyen, “Building up PID-Neural Controller for Rotary Inverted Pendulum System,” Ho Chi Minh City University of Technology and Education, Feb. 2012, doi: 10.13140/RG.2.2.30061.79848.
N. T. P. Ha, and H. T. Hoang, “Ly Thuyet Dieu Khien Tu Dong,” NXB ĐHQG TP. HCM, 2005.
Y. Li, J. Zhang, and Q. Wu, “Adaptive Sliding Mode Neural Network Control for Nonlinear Systems,” Academic Press, 2019.
S. Das, A. Biswas, S. Dasgupta, and A. Abraham, “Bacterial Foraging Optimization Algorithm: Theoretical Foundations, Analysis, and Applications,” Foundations of Computational Intelligence, vol. 3, SCI 203, pp. 23-55, 2009. DOI: https://doi.org/10.1007/978-3-642-01085-9_2
M. Ruderman, J. Krettek, F. Hoffmann, T. Bertram, “Optimal State Space Control of DC Motor,” The International Federation of Automatic Control Proceedings Volumes, vol. 41, no. 2, pp. 5796-5801, 2008. DOI: https://doi.org/10.3182/20080706-5-KR-1001.00977
Y. Yang, Y. Wang, and P. Jia, “Adaptive robust control with extended disturbance observer for motion control of DC motors,” Electronic Letters, vol. 51, no. 22, pp. 1761-1763, 2015. DOI: https://doi.org/10.1049/el.2015.1009
P. Roco, “Stability of PID control for industrial robot arms,” IEEE Transactions on Robotics and Automation, vol. 12, no. 4, pp. 606-614, 1996. DOI: https://doi.org/10.1109/70.508444
S. Skoczowski, S. Domesk, K. Pietrusewicz, and B. Broel-Plater, “A method for improving the robustness of PID control,” IEEE Trans. Ind. Electron., vol. 58, no. 6, pp. 1669-1676, 2005. DOI: https://doi.org/10.1109/TIE.2005.858705
B. C. Kuo, “Automatic Control System,” Third edition, 1975 Prentice-Hall, Inc.
N. S. Nise, “Control Systems Engineering,” Sixth Edition, 2015, John Wiley and Sons, Inc.
N. Q. Lin, D. M. Xuan, and D. X. Ba, “Advanced control design for a high-precision heating furnace using combination of PI/Neural network,” JTES, vol. 55, 2019.
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