Extra control coefficient additive ECCA-PID for control optimization of electrical and mechanic system
DOI:
https://doi.org/10.14311/AP.2022.62.0522Keywords:
ECCA-PID, decision-making unit, satisfactory responseAbstract
Proportional Integral Derivative (PID) controllers are frequently used control methods for mechanical and electrical systems. Controller values are chosen either by calculation or by experimentation to obtain a satisfactory response in the system and to optimise the response. Sometimes the controller values do not quite capture the desired system response due to incorrect calculations or approximate entered values. In this case, it is necessary to add features that can make a comparison with the existing traditional system and add decision-making features to optimise the response of the system. In this article, the decision-making unit created for these control systems to provide a better control response and the PID system that contributes an extra control coefficient called ECCA-PID is presented. First, the structure and design of the traditional PID control system and the ECCA-PID control system are presented. After that, ECCA-PID and traditional PID methods’ step response of a quadratic system
are examined. The results obtained show the effectiveness of the proposed control method.
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H. Wang, L. Jinbo. Research on fractional order fuzzy PID control of the pneumatic-hydraulic upper limb rehabilitation training system based on PSO. International Journal of Control, Automation and Systems 20(3):210–320, 2022. https://doi.org/10.1007/s12555-020-0847-1.
M. N. Muftah, A. A. M. Faudzi, S. Sahlan, M. Shouran. Modeling and fuzzy FOPID controller tuned by PSO for pneumatic positioning system. Energies 15(10):3757, 2022. https://doi.org/10.3390/en15103757.
E. Can, H. H. Sayan. The performance of the DC motor by the PID controlling PWM DC-DC boost converter. Tehnički glasnik 11(4):182–187, 2017.
E. Can, M. S. Toksoy. A flexible closed-loop (fcl) pid and dynamic fuzzy logic + pid controllers for optimization of dc motor. Journal of Engineering Research 2021. Online first, https://doi.org/10.36909/jer.13813.
J. Crowe, K. K. Tan, T. H. Lee, et al. PID control: New identification and design methods. Springer-Verlag London Limited, 2005.
C. Knospe. PID control. IEEE Control Systems Magazine 26(1):30–31, 2006. https://doi.org/10.1109/MCS.2006.1580151.
M.-T. Ho, C.-Y. Lin. PID controller design for robust performance. IEEE Transactions on Automatic Control 48(8):1404–1409, 2003. https://doi.org/10.1109/TAC.2003.815028.
C. Zhao, L. Guo. Towards a theoretical foundation of PID control for uncertain nonlinear systems. Automatica 142:110360, 2022. https://doi.org/10.1016/j.automatica.2022.110360.
P. Patil, S. S. Anchan, C. S. Rao. Improved PID controller design for an unstable second order plus time delay non-minimum phase systems. Results in Control and Optimization 7:100117, 2022. https://doi.org/10.1016/j.rico.2022.100117.
E. S. Tognetti, G. A. de Oliveira. Robust state feedback-based design of PID controllers for high-order systems with time-delay and parametric uncertainties. Journal of Control, Automation and Electrical Systems 33(2):382–392, 2022. https://doi.org/10.1007/s40313-021-00846-2.
C. Cruz-Díaz, B. del Muro-Cuéllar, G. Duchén-Sánchez, et al. Observer-based PID control strategy for the stabilization of delayed high order systems with up to three unstable poles. Mathematics 10(9):1399, 2022. https://doi.org/10.3390/math10091399.
J. G. Ziegler, N. B. Nichols. Optimum settings for automatic controllers. Journal of Dynamic Systems, Measurement, and Control 115(2B):220–222, 1993. https://doi.org/10.1115/1.2899060.
S. Skogestad. Simple analytic rules for model reduction and PID controller tuning. Journal of Process Control 13(4):291–309, 2003. https://doi.org/10.1016/S0959-1524(02)00062-8.
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Copyright (c) 2022 Erol Can
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