Alternative Feedback Control Mechanisms To Standard PID
Abstract
Feedback controllers stabilize a system through one or several feedback parameters. This paper focused on
the classical PID controller, used both throughout academia and industry. It is therefore useful to understand
how to optimize the PID controllers for use under dynamic conditions, where spikes are readily encountered.
A line following robot is considered under conditions of widely changing UV light, and speeds of 0 to 8 m/s,
and demonstrates the need for autonomous, dynamic recalibration. This paper contrasts standard Ziegler
Nichols PID tuning with supplementary tuning methods, including tuning by fuzzy logic, and fractional
order parameter tuning. This paper also loosely considers H2 and H Infinity Optimization, and Particle
Swarm Optimization (PSO), which are operable on non-PID controllers and are therefore only loosley being
considered for the purposes of this paper. The results suggest that fractional order, fuzzy logic PIDs (FOFPIDs)
are the best for application in academia and the small electronics industry. FOF-PID performed at 88
percent higher efficiency than Zielger Nichols method on its own, and FPID performed at 71.2 percent higher
efficiency than Ziegler Nichols method.
References
A. Balaguer, Pedro, Norhaliza Abdul Wahab, M. Reza Katebi,
and Ramon Vilanova. Multivariable PID Control Tuning: A
Controller Validation Approach. In Emerging Technologies and
Factory Automation, 2008. ETFA 2008. IEEE International
Conference On, 289294. IEEE, 2008.
Barbosa, Ramiro S., and Isabel S. Jesus. Comparative
Study of Fuzzy Integer and Fractional PID Controller. In Industrial
Electronics Society, IECON 2013-39th Annual Conference of
the IEEE, 33923397. IEEE, 2013.
Chen, Min-Rong, Yong-Zai Lu, and Genke Yang. Multiobjective
Optimization Using Population-Based Extremal Optimization.
Neural Computing and Applications 17, no. 2 (March 2008):
https://doi.org/10.1007/s00521-007-0118-6.
Hackbusch, W., and Gabriel Wittum, eds. Adaptive Methods–
Algorithms, Theory and Applications: Proceedings of the Ninth
GAMM-Seminar, Kiel, January 22-24, 1993. Notes on Numerical
Fluid Mechanics, v. 46. Braunschweig: Vieweg, 1994.
Kosari, Amirreza, Hadi Jahanshahi, and Aliakbar Razavi.
Design of Optimal PID, Fuzzy and New Fuzzy-PID Controller for
CANSAT Carrier System Thrust Vector. International Journal
of Advanced Design and Manufacturing Technology 8, no. 2 (2015).
Lim, Jae Sik, and Young Il Lee. Design of Discrete-Time
Multivariable PID Controllers via LMI Approach. In Control,
Automation and Systems, 2008. ICCAS 2008. International
Conference On, 18671871. IEEE, 2008.
Luan, Xiaoli, Qiang Chen, and Fei Liu. Centralized PI Control
for High Dimensional Multivariable Systems Based on Equivalent
Transfer Function. ISA Transactions 53, no. 5 (September 2014):
https://doi.org/10.1016/j.isatra.2014.05.016. Mahmoodabadi,
M.J., and H. Jahanshahi. Multi-Objective Optimized
Fuzzy-PID Controllers for Fourth Order Nonlinear Systems. Engineering
Science and Technology, an International Journal 19, no. 2
(June 2016): 108498. https://doi.org/10.1016/j.jestch.2016.01.010.
Mahmoodabadi, Mohammad Javad, Mohammad Bagher
Salahshoor Mottaghi, and Ali Mahmodinejad. Optimum
Design of Fuzzy Controllers for Nonlinear Systems Using
Multi-Objective Particle Swarm Optimization. Journal of
Vibration and Control 22, no. 3 (February 2016): 76983.
https://doi.org/10.1177/1077546314532116.
Pegel, Sabine, and Sebastian Engell. Multivariable PID
Controller Design via Approximation of the Attainable Performance.
In Industrial Electronics Society, 2001. IECON01. The
th Annual Conference of the IEEE, 1:724729. IEEE, 2001.
EDNKA, Vladimr, and Zbynk RAIDA. Critical Comparison
of Multi-Objective Optimization Methods: Genetic Algorithms
versus Swarm Intelligence. Radioengineering 19, no. 3 (2010).
Uduehi, D., A. Ordys, and M. J. Grimble. Multivariable
PID Controller Design Using Online Generalised Predictive
Control Optimisation. In Control Applications, 2002. Proceedings
of the 2002 International Conference On, 1:272277. IEEE, 2002.
Vijay Kumar, V., V.S.R. Rao, and M. Chidambaram. Centralized
PI Controllers for Interacting Multivariable Processes by Synthesis
Method. ISA Transactions 51, no. 3 (May 2012): 400409.
https://doi.org/10.1016/j.isatra.2012.02.001.
Wong, Wing Shing, and Roger W. Brockett. Systems
with Finite Communication Bandwidth Constraints. II. Stabilization
with Limited Information Feedback. IEEE Transactions
on Automatic Control 44, no. 5 (1999): 10491053. Zeng,
Guo-Qiang, Jie Chen, Min-Rong Chen, Yu-Xing Dai, Li-Min Li,
Kang-Di Lu, and Chong-Wei Zheng. Design of Multivariable
PID Controllers Using Real-Coded Population-Based Extremal
Optimization. Neurocomputing 151 (March 2015): 134353.
https://doi.org/10.1016/j.neucom.2014.10.060.
Zeng, Guo-Qiang, Jie Chen, Yu-Xing Dai, Li-Min Li, Chong-Wei
Zheng, and Min-Rong Chen. Design of Fractional Order PID Controller
for Automatic Regulator Voltage System Based on Multi-
Objective Extremal Optimization. Neurocomputing 160 (July
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