Research Article
BibTex RIS Cite

Optimizing PID Gains of a Vehicle using the state-of-the-art Metaheuristic Methods

Year 2023, Volume: 11 Issue: 3, 107 - 117, 30.09.2023
https://doi.org/10.21541/apjess.1266949

Abstract

PID controllers are important control methods that are widely used in industrial processes. Proper tuning of PID gains is critical for achieving the state-of-the-art system performance. Therefore, optimizing PID gains is an important research topic in the field of control engineering. In this study, PID controller gains are automatically tuned using metaheuristic optimization methods. These methods use an iterative approach to calculate optimal values of PID controller gains based on different optimization techniques. The interaction between artificial intelligence and control systems requires a multidimensional approach across different disciplines. In the study, we perform Particle Swarm Optimization, Gray Wolf Optimization, Whale Optimization Algorithm, Firefly Algorithm, Harris Hawks Optimization, Artificial Hummingbird Algorithm and African Vulture Optimization Algorithm to determine PID gains. In the simulation, step input is applied to the dynamic equation of the unmanned free-swimming submersible vehicle. The fitness function is determined with respect to controller integral square error, settling time value, and maximum percent overshoot value. We also evaluate the optimization time of the selected algorithms based on the fitness function. Experimental results present that Artificial Hummingbird Algorithm, Gray Wolf Optimization and Particle Swarm Optimization achieve significant performance. This underlines that using metaheuristic methods in PID gain optimization increase overall system performance.

References

  • Ziegler JG, Nichols NB. Optimum Settings for Automatic Controllers. J Dyn Syst Meas Control 1993; 115:220–2. https://doi.org/10.1115/1.2899060.
  • Cohen GH, Coon GA. Theoretical Consideration of Retarded Control. Trans Am Soc Mech Eng 1953; 75:827–34. https://doi.org/10.1115/1.4015451.
  • Fereidouni, A, Masoum, M. A. S., & Moghbel, M. (2015). A new adaptive configuration of PID type fuzzy logic controller. In ISA Transactions (Vol. 56, pp. 222–240). Elsevier BV. https://doi.org/10.1016/j.isatra.2014.11.010
  • Ali, R., Mohamed, T. H., Qudaih, Y. S., & Mitani, Y. (2014). A new load frequency control approach in an isolated small power systems using coefficient diagram method. In International Journal of Electrical Power & Energy Systems (Vol. 56, pp. 110–116). Elsevier BV. https://doi.org/10.1016/j.ijepes.2013.11.002
  • Mittal, A., Kapoor, A., & Saxena, T. K. (2013). Adaptive tuning of PID controller for a nonlinear constant temperature water bath under set-point disturbances using GANFC. J. Auto Syst. Eng, 7, 143-63.
  • Kim DH, Cho JH. A Biologically Inspired Intelligent PID Controller Tuning for AVR Systems. Int J Control Autom Syst 2006; 4:624–36.
  • Srinivas P, Vijaya Lakshmi K, Kumar VN. A Comparison of PID Controller Tuning Methods for Three Tank Level Process. Int J Adv Res Electr Electron Instrum Eng 2007;3.
  • El-Deen AT, Mahmoud AAH, El-Sawi AR, El-Deen AT, Mahmoud AAH, El-Sawi AR. Optimal PID Tuning for DC Motor Speed Controller Based on Genetic Algorithm. Int Rev Autom Control 2015; 8:80–5. https://doi.org/10.15866/IREACO.V8I1.4839.
  • Bassi JS, Dada EG. Automatic Tuning of Proportional–Integral–Derivative Controller using Genetic Algorithm. Pacific J Sci Technol 2018; 19:51–7.
  • Emmanuel AC, Inyiama H. A SURVEY OF CONTROLLER DESIGN METHODS FOR A ROBOT MANIPULATOR IN HARSH ENVIRONMENTS. Eur J Eng Technol 2015; 3:64–73.
  • Sandoval D, Soto I, Adasme P. Control of direct current motor using Ant Colony optimization. CHILECON 2015 - 2015 IEEE Chil. Conf. Electr. Electron. Eng. Inf. Commun. Technol. Proc. IEEE Chilecon 2015, Institute of Electrical and Electronics Engineers Inc.; 2016, p. 79–82. https://doi.org/10.1109/CHILECON.2015.7400356
  • E.El-Telbany M. Tuning PID Controller for DC Motor: An Artificial Bees Optimization Approach. Int J Comput Appl 2013; 77:18–21. https://doi.org/10.5120/13559-1341.
  • Liao W, Hu Y, Wang H. Optimization of PID control for DC motor based on artificial bee colony algorithm. Int. Conf. Adv. Mechatron. Syst. ICAMechS, IEEE Computer Society; 2014, p. 23–7. https://doi.org/10.1109/ICAMECHS.2014.6911617.
  • Senberber H, Bagis A. Fractional PID controller design for fractional order systems using ABC algorithm. Proc. 21st Int. Conf. Electron., Institute of Electrical and Electronics Engineers Inc.; 2017. https://doi.org/10.1109/ELECTRONICS.2017.7995218.
  • Annisa J, Mat Darus IZ, Tokhi MO, Mohamaddan S. Implementation of PID Based Controller Tuned by Evolutionary Algorithm for Double Link Flexible Robotic Manipulator. 2018 Int. Conf. Comput. Approach Smart Syst. Des. Appl. ICASSDA 2018, Institute of Electrical and Electronics Engineers Inc.; 2018. https://doi.org/10.1109/ICASSDA.2018.8477615.
  • Zhi D. Optimization of PID Controller for Single Phase Inverter Based on ABC. 2019 5th Int. Conf. Control. Autom. Robot. ICCAR 2019, Institute of Electrical and Electronics Engineers Inc.; 2019, p. 501–4. https://doi.org/10.1109/ICCAR.2019.8813361.
  • Kotteeswaran R, Sivakumar L. Optimal partial-retuning of decentralised PI controller of coal gasifier using bat algorithm. Int. Conf. Swarm, Evol. Memetic Comput., Springer; 2013, p. 750–61.
  • Katal N, Kumar P, Narayan S. Optimal PID controller for coupled-tank liquid-level control system using bat algorithm. Int. Conf. Power, Control Embed. Syst. ICPCES 2014, Institute of Electrical and Electronics Engineers Inc.; 2014. https://doi.org/10.1109/ICPCES.2014.7062818.
  • Singh K, Vasant P, Elamvazuthi I, Kannan R. PID Tuning of Servo Motor Using Bat Algorithm. Procedia Comput Sci 2015; 60:1798–808. https://doi.org/10.1016/J.PROCS.2015.08.290.
  • Premkumar K, Manikandan B V. Bat algorithm optimized fuzzy PD based speed controller for brushless direct current motor. Eng Sci Technol an Int J 2016; 19:818–40. https://doi.org/10.1016/J.JESTCH.2015.11.004.
  • Bayoumi EH., Salem F. PID controller for series-parallel resonant converters using bacterial foraging optimization. Electromotion Sci J 2012; 19:64–79.
  • Benbouabdallah K, Zhu QD. Bacterial foraging oriented by particle swarm optimization of a Lyapunov-based controller for mobile robot target tracking. Proc. - Int. Conf. Nat. Comput., IEEE Computer Society; 2013, p. 506–11. https://doi.org/10.1109/ICNC.2013.6818029.
  • Sivakumar R, Deepa P, Sankaran D. A Study on BFO Algorithm based PID Controller Design for MIMO Process using Various Cost Functions. Indian J Sci Technol 2016; 9:1–6. https://doi.org/10.17485/IJST/2016/V9I12/89942.
  • Agarwal S, Yadav D, Verma A. Speed control of PMSM drive using bacterial foraging optimization. 4th IEEE Uttar Pradesh Sect. Int. Conf. Electr. Comput. Electron. UPCON 2017, vol. 2018- January, Institute of Electrical and Electronics Engineers Inc.; 2017, p. 84–90. https://doi.org/10.1109/UPCON.2017.8251027.
  • Jasim MH. Tuning of a PID Controller by Bacterial Foraging Algorithm for Position Control of DC Servo Motor. Eng Technol J 2018; 36:287–94. https://doi.org/10.30684/etj.36.3A.7.
  • Chang W Der, Chen CY. PID controller design for MIMO processes using improved particle swarm optimization. Circuits, Syst Signal Process 2014;33:1473–90. https://doi.org/10.1007/S00034-013-9710-4/FIGURES/8.
  • Rajesh RJ, Ananda CM. PSO tuned PID controller for controlling camera position in UAV using 2-axis gimbal. Proc. 2015 IEEE Int. Conf. Power Adv. Control Eng. ICPACE 2015, Institute of Electrical and Electronics Engineers Inc.; 2015, p. 128–33. https://doi.org/10.1109/ICPACE.2015.7274930.
  • Lodhi RS, Saraf A. Survey on PID Controller Based Automatic Voltage Regulator. Int J Adv Res Electr Electron Instrum Eng 2016; 5:7424–9.
  • Fister D, Fister I, Fister I, Šafarič R. Parameter tuning of PID controller with reactive nature-inspired algorithms. Rob Auton Syst 2016; 84:64–75. https://doi.org/10.1016/J.ROBOT.2016.07.005.
  • Joseph SB, Dada EG. Proportional-integral-derivative (PID) controller tuning for an inverted pendulum using particle swarm optimisation (PSO) algorithm. FUDMA J Sci 2018; 2:73–9.
  • Kumar P, Nema S, Padhy PK. PID controller for nonlinear system using cuckoo optimization. Int. Conf. Control. Instrumentation, Commun. Comput. Technol. ICCICCT 2014, Institute of Electrical and Electronics Engineers Inc.; 2014, p. 711–6. https://doi.org/10.1109/ICCICCT.2014.6993052.
  • Gholap V, Dessai CN, Bagyaveereswaran V. PID controller tuning using metaheuristic optimization algorithms for benchmark problems. IOP Conf Ser Mater Sci Eng 2017; 263:052021. https://doi.org/10.1088/1757-899X/263/5/052021.
  • Bingul Z, Karahan O. A novel performance criterion approach to optimum design of PID controller using cuckoo search algorithm for AVR system. J Franklin Inst 2018; 355:5534–59. https://doi.org/10.1016/J.JFRANKLIN.2018.05.056.
  • Bansal R, Jain M, Bhushan B. Designing of Multi-objective Simulated Annealing Algorithm tuned PID controller for a temperature control system. 6th IEEE Power India Int. Conf., Institute of Electrical and Electronics Engineers (IEEE); 2015, p. 1–6. https://doi.org/10.1109/POWERI.2014.7117716.
  • Vijay D, Banu US. PID controller tuned using Simulated Annealing for assured crew re-entry vehicle with PWPF thruster firing. Proc. 2016 Online Int. Conf. Green Eng. Technol. IC-GET 2016, Institute of Electrical and Electronics Engineers Inc.; 2017. https://doi.org/10.1109/GET.2016.7916804.
  • Lahcene R, Abdeldjalil S, Aissa K. Optimal tuning of fractional order PID controller for AVR system using simulated annealing optimization algorithm. 5th Int. Conf. Electr. Eng. - Boumerdes, ICEE-B 2017, vol. 2017- January, Institute of Electrical and Electronics Engineers Inc.; 2017, p. 1–6. https://doi.org/10.1109/ICEE-B.2017.8192194.
  • Shatnawi M, Bayoumi E. Brushless DC motor controller optimization using simulated annealing. Int. Conf. Electical Drives Power Electron., vol. 2019- September, KoREMA; 2019, p. 292–7. https://doi.org/10.1109/EDPE.2019.8883924.
  • ŞEN MA, KALYONCU M. Optimal Tuning of PID Controller Using Grey Wolf Optimizer Algorithm for Quadruped Robot. Balk J Electr Comput Eng 2018; 6:29–35. https://doi.org/10.17694/BAJECE.401992.
  • Agarwal J, Parmar G, Gupta R, Sikander A. Analysis of grey wolf optimizer based fractional order PID controller in speed control of DC motor. Microsyst Technol 2018; 24:4997–5006. https://doi.org/10.1007/S00542-018-3920-4/FIGURES/13.
  • Yadav S, Nagar SK, Mishra A. Tuning of parameters of PID controller using Grey Wolf Optimizer. Proc. Int. Conf. Adv. Electron. Electr. Comput. Intell. 2019, Elsevier BV; 2019. https://doi.org/10.2139/SSRN.3575432.
  • Sule AH, Mokhtar AS, Jamian JJ Bin, Khidrani A, Larik RM. Optimal tuning of proportional integral controller for fixed-speed wind turbine using grey wolf optimizer. Int J Electr Comput Eng 2020; 10:5251–61. https://doi.org/10.11591/IJECE.V10I5.PP5251-5261.
  • Verma SK, Devarapalli R. Fractional order PID controller with optimal parameters using Modified Grey Wolf Optimizer for AVR system. Arch Control Sci 2022; 32:429–50. https://doi.org/10.24425/acs.2022.141719.
  • Hekimoǧlu B, Ekinci S, Kaya S. Optimal PID Controller Design of DC-DC Buck Converter using Whale Optimization Algorithm. Int. Conf. Artif. Intell. Data Process. IDAP 2018, Institute of Electrical and Electronics Engineers Inc.; 2018. https://doi.org/10.1109/IDAP.2018.8620833.
  • Kumar AA, Kumar G, Anil A, Dr K, Giriraj Kumar SM. Application of Whale Optimization Algorithm for tuning of a PID controller for a drilling machine Atal Anil Kumar, S M Giriraj Kumar. Application of Whale Optimization Algorithm for tuning of a PID controller for a drilling machine Application of Whale Optimization Algorithm for tuning of a PID controller for a drilling machine. ICAARS 2018.
  • Mosaad AM, Attia MA, Abdelaziz AY. Whale optimization algorithm to tune PID and PIDA controllers on AVR system. Ain Shams Eng J 2019; 10:755–67. https://doi.org/10.1016/J.ASEJ.2019.07.004.
  • Loucif F, Kechida S, Sebbagh A. Whale optimizer algorithm to tune PID controller for the trajectory tracking control of robot manipulator. J Brazilian Soc Mech Sci Eng 2020; 42:1–11. https://doi.org/10.1007/S40430-019-2074-3/FIGURES/11.
  • Bendjeghaba, O., Boushaki, S. I., & Zemmour, N. (2013, May). Firefly algorithm for optimal tuning of PID controller parameters. 4th International Conference on Power Engineering, Energy and Electrical Drives. Presented at the 2013 IV International Conference on Power Engineering, Energy and Electrical Drives (POWERENG), Istanbul, Turkey. doi:10.1109/powereng.2013.663579
  • Coelho, L. dos S., & Mariani, V. C. (2012). Firefly algorithm approach based on chaotic Tinkerbell map applied to multivariable PID controller tuning. Computers & Mathematics with Applications (Oxford, England: 1987), 64(8), 2371–2382. doi: 10.1016/j.camwa.2012.05.007
  • Ekinci, S., Izci, D., & Hekimoglu, B. (2020, June). PID speed control of DC motor using Harris hawks optimization algorithm. 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE). Presented at the 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE), Istanbul, Turkey. doi:10.1109/icecce49384.2020.9179308
  • Nise NS. Control Systems Engineering. 7th Edition. John Wiley & Sons; 2013.
  • Introduction: PID Controller Design n.d. https://ctms.engin.umich.edu/CTMS/index.php?example=Introduction&section=ControlPID (accessed November 13, 2022).
  • Kennedy J, Eberhart R. Particle swarm optimization. Proc. ICNN’95 - Int. Conf. Neural Networks, vol. 4, 1995, p. 1942–8 vol.4. https://doi.org/10.1109/ICNN.1995.488968.
  • Mirjalili S, Mirjalili SM, Lewis A. Grey Wolf Optimizer. Adv Eng Softw 2014; 69:46–61. https://doi.org/https://doi.org/10.1016/j.advengsoft.2013.12.007.
  • Mirjalili S, Lewis A. The Whale Optimization Algorithm. Adv Eng Softw 2016; 95:51–67. https://doi.org/https://doi.org/10.1016/j.advengsoft.2016.01.008.
  • Yang X-S. Firefly Algorithms for Multimodal Optimization. In: Watanabe O, Zeugmann T, editors. Stoch. Algorithms Found. Appl., Berlin, Heidelberg: Springer Berlin Heidelberg; 2009, p. 169–78.
  • Heidari AA, Mirjalili S, Faris H, Aljarah I, Mafarja M, Chen H. Harris hawks optimization: Algorithm and applications. Futur Gener Comput Syst 2019; 97:849–72. https://doi.org/https://doi.org/10.1016/j.future.2019.02.028.
  • Zhao W, Wang L, Mirjalili S. Artificial hummingbird algorithm: A new bio-inspired optimizer with its engineering applications. Comput Methods Appl Mech Eng 2022; 388:114194. https://doi.org/https://doi.org/10.1016/j.cma.2021.114194.
  • Abdollahzadeh B, Gharehchopogh FS, Mirjalili S. African vultures optimization algorithm: A new nature-inspired metaheuristic algorithm for global optimization problems. Comput Ind Eng 2021; 158:107408. https://doi.org/https://doi.org/10.1016/j.cie.2021.107408.
Year 2023, Volume: 11 Issue: 3, 107 - 117, 30.09.2023
https://doi.org/10.21541/apjess.1266949

Abstract

References

  • Ziegler JG, Nichols NB. Optimum Settings for Automatic Controllers. J Dyn Syst Meas Control 1993; 115:220–2. https://doi.org/10.1115/1.2899060.
  • Cohen GH, Coon GA. Theoretical Consideration of Retarded Control. Trans Am Soc Mech Eng 1953; 75:827–34. https://doi.org/10.1115/1.4015451.
  • Fereidouni, A, Masoum, M. A. S., & Moghbel, M. (2015). A new adaptive configuration of PID type fuzzy logic controller. In ISA Transactions (Vol. 56, pp. 222–240). Elsevier BV. https://doi.org/10.1016/j.isatra.2014.11.010
  • Ali, R., Mohamed, T. H., Qudaih, Y. S., & Mitani, Y. (2014). A new load frequency control approach in an isolated small power systems using coefficient diagram method. In International Journal of Electrical Power & Energy Systems (Vol. 56, pp. 110–116). Elsevier BV. https://doi.org/10.1016/j.ijepes.2013.11.002
  • Mittal, A., Kapoor, A., & Saxena, T. K. (2013). Adaptive tuning of PID controller for a nonlinear constant temperature water bath under set-point disturbances using GANFC. J. Auto Syst. Eng, 7, 143-63.
  • Kim DH, Cho JH. A Biologically Inspired Intelligent PID Controller Tuning for AVR Systems. Int J Control Autom Syst 2006; 4:624–36.
  • Srinivas P, Vijaya Lakshmi K, Kumar VN. A Comparison of PID Controller Tuning Methods for Three Tank Level Process. Int J Adv Res Electr Electron Instrum Eng 2007;3.
  • El-Deen AT, Mahmoud AAH, El-Sawi AR, El-Deen AT, Mahmoud AAH, El-Sawi AR. Optimal PID Tuning for DC Motor Speed Controller Based on Genetic Algorithm. Int Rev Autom Control 2015; 8:80–5. https://doi.org/10.15866/IREACO.V8I1.4839.
  • Bassi JS, Dada EG. Automatic Tuning of Proportional–Integral–Derivative Controller using Genetic Algorithm. Pacific J Sci Technol 2018; 19:51–7.
  • Emmanuel AC, Inyiama H. A SURVEY OF CONTROLLER DESIGN METHODS FOR A ROBOT MANIPULATOR IN HARSH ENVIRONMENTS. Eur J Eng Technol 2015; 3:64–73.
  • Sandoval D, Soto I, Adasme P. Control of direct current motor using Ant Colony optimization. CHILECON 2015 - 2015 IEEE Chil. Conf. Electr. Electron. Eng. Inf. Commun. Technol. Proc. IEEE Chilecon 2015, Institute of Electrical and Electronics Engineers Inc.; 2016, p. 79–82. https://doi.org/10.1109/CHILECON.2015.7400356
  • E.El-Telbany M. Tuning PID Controller for DC Motor: An Artificial Bees Optimization Approach. Int J Comput Appl 2013; 77:18–21. https://doi.org/10.5120/13559-1341.
  • Liao W, Hu Y, Wang H. Optimization of PID control for DC motor based on artificial bee colony algorithm. Int. Conf. Adv. Mechatron. Syst. ICAMechS, IEEE Computer Society; 2014, p. 23–7. https://doi.org/10.1109/ICAMECHS.2014.6911617.
  • Senberber H, Bagis A. Fractional PID controller design for fractional order systems using ABC algorithm. Proc. 21st Int. Conf. Electron., Institute of Electrical and Electronics Engineers Inc.; 2017. https://doi.org/10.1109/ELECTRONICS.2017.7995218.
  • Annisa J, Mat Darus IZ, Tokhi MO, Mohamaddan S. Implementation of PID Based Controller Tuned by Evolutionary Algorithm for Double Link Flexible Robotic Manipulator. 2018 Int. Conf. Comput. Approach Smart Syst. Des. Appl. ICASSDA 2018, Institute of Electrical and Electronics Engineers Inc.; 2018. https://doi.org/10.1109/ICASSDA.2018.8477615.
  • Zhi D. Optimization of PID Controller for Single Phase Inverter Based on ABC. 2019 5th Int. Conf. Control. Autom. Robot. ICCAR 2019, Institute of Electrical and Electronics Engineers Inc.; 2019, p. 501–4. https://doi.org/10.1109/ICCAR.2019.8813361.
  • Kotteeswaran R, Sivakumar L. Optimal partial-retuning of decentralised PI controller of coal gasifier using bat algorithm. Int. Conf. Swarm, Evol. Memetic Comput., Springer; 2013, p. 750–61.
  • Katal N, Kumar P, Narayan S. Optimal PID controller for coupled-tank liquid-level control system using bat algorithm. Int. Conf. Power, Control Embed. Syst. ICPCES 2014, Institute of Electrical and Electronics Engineers Inc.; 2014. https://doi.org/10.1109/ICPCES.2014.7062818.
  • Singh K, Vasant P, Elamvazuthi I, Kannan R. PID Tuning of Servo Motor Using Bat Algorithm. Procedia Comput Sci 2015; 60:1798–808. https://doi.org/10.1016/J.PROCS.2015.08.290.
  • Premkumar K, Manikandan B V. Bat algorithm optimized fuzzy PD based speed controller for brushless direct current motor. Eng Sci Technol an Int J 2016; 19:818–40. https://doi.org/10.1016/J.JESTCH.2015.11.004.
  • Bayoumi EH., Salem F. PID controller for series-parallel resonant converters using bacterial foraging optimization. Electromotion Sci J 2012; 19:64–79.
  • Benbouabdallah K, Zhu QD. Bacterial foraging oriented by particle swarm optimization of a Lyapunov-based controller for mobile robot target tracking. Proc. - Int. Conf. Nat. Comput., IEEE Computer Society; 2013, p. 506–11. https://doi.org/10.1109/ICNC.2013.6818029.
  • Sivakumar R, Deepa P, Sankaran D. A Study on BFO Algorithm based PID Controller Design for MIMO Process using Various Cost Functions. Indian J Sci Technol 2016; 9:1–6. https://doi.org/10.17485/IJST/2016/V9I12/89942.
  • Agarwal S, Yadav D, Verma A. Speed control of PMSM drive using bacterial foraging optimization. 4th IEEE Uttar Pradesh Sect. Int. Conf. Electr. Comput. Electron. UPCON 2017, vol. 2018- January, Institute of Electrical and Electronics Engineers Inc.; 2017, p. 84–90. https://doi.org/10.1109/UPCON.2017.8251027.
  • Jasim MH. Tuning of a PID Controller by Bacterial Foraging Algorithm for Position Control of DC Servo Motor. Eng Technol J 2018; 36:287–94. https://doi.org/10.30684/etj.36.3A.7.
  • Chang W Der, Chen CY. PID controller design for MIMO processes using improved particle swarm optimization. Circuits, Syst Signal Process 2014;33:1473–90. https://doi.org/10.1007/S00034-013-9710-4/FIGURES/8.
  • Rajesh RJ, Ananda CM. PSO tuned PID controller for controlling camera position in UAV using 2-axis gimbal. Proc. 2015 IEEE Int. Conf. Power Adv. Control Eng. ICPACE 2015, Institute of Electrical and Electronics Engineers Inc.; 2015, p. 128–33. https://doi.org/10.1109/ICPACE.2015.7274930.
  • Lodhi RS, Saraf A. Survey on PID Controller Based Automatic Voltage Regulator. Int J Adv Res Electr Electron Instrum Eng 2016; 5:7424–9.
  • Fister D, Fister I, Fister I, Šafarič R. Parameter tuning of PID controller with reactive nature-inspired algorithms. Rob Auton Syst 2016; 84:64–75. https://doi.org/10.1016/J.ROBOT.2016.07.005.
  • Joseph SB, Dada EG. Proportional-integral-derivative (PID) controller tuning for an inverted pendulum using particle swarm optimisation (PSO) algorithm. FUDMA J Sci 2018; 2:73–9.
  • Kumar P, Nema S, Padhy PK. PID controller for nonlinear system using cuckoo optimization. Int. Conf. Control. Instrumentation, Commun. Comput. Technol. ICCICCT 2014, Institute of Electrical and Electronics Engineers Inc.; 2014, p. 711–6. https://doi.org/10.1109/ICCICCT.2014.6993052.
  • Gholap V, Dessai CN, Bagyaveereswaran V. PID controller tuning using metaheuristic optimization algorithms for benchmark problems. IOP Conf Ser Mater Sci Eng 2017; 263:052021. https://doi.org/10.1088/1757-899X/263/5/052021.
  • Bingul Z, Karahan O. A novel performance criterion approach to optimum design of PID controller using cuckoo search algorithm for AVR system. J Franklin Inst 2018; 355:5534–59. https://doi.org/10.1016/J.JFRANKLIN.2018.05.056.
  • Bansal R, Jain M, Bhushan B. Designing of Multi-objective Simulated Annealing Algorithm tuned PID controller for a temperature control system. 6th IEEE Power India Int. Conf., Institute of Electrical and Electronics Engineers (IEEE); 2015, p. 1–6. https://doi.org/10.1109/POWERI.2014.7117716.
  • Vijay D, Banu US. PID controller tuned using Simulated Annealing for assured crew re-entry vehicle with PWPF thruster firing. Proc. 2016 Online Int. Conf. Green Eng. Technol. IC-GET 2016, Institute of Electrical and Electronics Engineers Inc.; 2017. https://doi.org/10.1109/GET.2016.7916804.
  • Lahcene R, Abdeldjalil S, Aissa K. Optimal tuning of fractional order PID controller for AVR system using simulated annealing optimization algorithm. 5th Int. Conf. Electr. Eng. - Boumerdes, ICEE-B 2017, vol. 2017- January, Institute of Electrical and Electronics Engineers Inc.; 2017, p. 1–6. https://doi.org/10.1109/ICEE-B.2017.8192194.
  • Shatnawi M, Bayoumi E. Brushless DC motor controller optimization using simulated annealing. Int. Conf. Electical Drives Power Electron., vol. 2019- September, KoREMA; 2019, p. 292–7. https://doi.org/10.1109/EDPE.2019.8883924.
  • ŞEN MA, KALYONCU M. Optimal Tuning of PID Controller Using Grey Wolf Optimizer Algorithm for Quadruped Robot. Balk J Electr Comput Eng 2018; 6:29–35. https://doi.org/10.17694/BAJECE.401992.
  • Agarwal J, Parmar G, Gupta R, Sikander A. Analysis of grey wolf optimizer based fractional order PID controller in speed control of DC motor. Microsyst Technol 2018; 24:4997–5006. https://doi.org/10.1007/S00542-018-3920-4/FIGURES/13.
  • Yadav S, Nagar SK, Mishra A. Tuning of parameters of PID controller using Grey Wolf Optimizer. Proc. Int. Conf. Adv. Electron. Electr. Comput. Intell. 2019, Elsevier BV; 2019. https://doi.org/10.2139/SSRN.3575432.
  • Sule AH, Mokhtar AS, Jamian JJ Bin, Khidrani A, Larik RM. Optimal tuning of proportional integral controller for fixed-speed wind turbine using grey wolf optimizer. Int J Electr Comput Eng 2020; 10:5251–61. https://doi.org/10.11591/IJECE.V10I5.PP5251-5261.
  • Verma SK, Devarapalli R. Fractional order PID controller with optimal parameters using Modified Grey Wolf Optimizer for AVR system. Arch Control Sci 2022; 32:429–50. https://doi.org/10.24425/acs.2022.141719.
  • Hekimoǧlu B, Ekinci S, Kaya S. Optimal PID Controller Design of DC-DC Buck Converter using Whale Optimization Algorithm. Int. Conf. Artif. Intell. Data Process. IDAP 2018, Institute of Electrical and Electronics Engineers Inc.; 2018. https://doi.org/10.1109/IDAP.2018.8620833.
  • Kumar AA, Kumar G, Anil A, Dr K, Giriraj Kumar SM. Application of Whale Optimization Algorithm for tuning of a PID controller for a drilling machine Atal Anil Kumar, S M Giriraj Kumar. Application of Whale Optimization Algorithm for tuning of a PID controller for a drilling machine Application of Whale Optimization Algorithm for tuning of a PID controller for a drilling machine. ICAARS 2018.
  • Mosaad AM, Attia MA, Abdelaziz AY. Whale optimization algorithm to tune PID and PIDA controllers on AVR system. Ain Shams Eng J 2019; 10:755–67. https://doi.org/10.1016/J.ASEJ.2019.07.004.
  • Loucif F, Kechida S, Sebbagh A. Whale optimizer algorithm to tune PID controller for the trajectory tracking control of robot manipulator. J Brazilian Soc Mech Sci Eng 2020; 42:1–11. https://doi.org/10.1007/S40430-019-2074-3/FIGURES/11.
  • Bendjeghaba, O., Boushaki, S. I., & Zemmour, N. (2013, May). Firefly algorithm for optimal tuning of PID controller parameters. 4th International Conference on Power Engineering, Energy and Electrical Drives. Presented at the 2013 IV International Conference on Power Engineering, Energy and Electrical Drives (POWERENG), Istanbul, Turkey. doi:10.1109/powereng.2013.663579
  • Coelho, L. dos S., & Mariani, V. C. (2012). Firefly algorithm approach based on chaotic Tinkerbell map applied to multivariable PID controller tuning. Computers & Mathematics with Applications (Oxford, England: 1987), 64(8), 2371–2382. doi: 10.1016/j.camwa.2012.05.007
  • Ekinci, S., Izci, D., & Hekimoglu, B. (2020, June). PID speed control of DC motor using Harris hawks optimization algorithm. 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE). Presented at the 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE), Istanbul, Turkey. doi:10.1109/icecce49384.2020.9179308
  • Nise NS. Control Systems Engineering. 7th Edition. John Wiley & Sons; 2013.
  • Introduction: PID Controller Design n.d. https://ctms.engin.umich.edu/CTMS/index.php?example=Introduction&section=ControlPID (accessed November 13, 2022).
  • Kennedy J, Eberhart R. Particle swarm optimization. Proc. ICNN’95 - Int. Conf. Neural Networks, vol. 4, 1995, p. 1942–8 vol.4. https://doi.org/10.1109/ICNN.1995.488968.
  • Mirjalili S, Mirjalili SM, Lewis A. Grey Wolf Optimizer. Adv Eng Softw 2014; 69:46–61. https://doi.org/https://doi.org/10.1016/j.advengsoft.2013.12.007.
  • Mirjalili S, Lewis A. The Whale Optimization Algorithm. Adv Eng Softw 2016; 95:51–67. https://doi.org/https://doi.org/10.1016/j.advengsoft.2016.01.008.
  • Yang X-S. Firefly Algorithms for Multimodal Optimization. In: Watanabe O, Zeugmann T, editors. Stoch. Algorithms Found. Appl., Berlin, Heidelberg: Springer Berlin Heidelberg; 2009, p. 169–78.
  • Heidari AA, Mirjalili S, Faris H, Aljarah I, Mafarja M, Chen H. Harris hawks optimization: Algorithm and applications. Futur Gener Comput Syst 2019; 97:849–72. https://doi.org/https://doi.org/10.1016/j.future.2019.02.028.
  • Zhao W, Wang L, Mirjalili S. Artificial hummingbird algorithm: A new bio-inspired optimizer with its engineering applications. Comput Methods Appl Mech Eng 2022; 388:114194. https://doi.org/https://doi.org/10.1016/j.cma.2021.114194.
  • Abdollahzadeh B, Gharehchopogh FS, Mirjalili S. African vultures optimization algorithm: A new nature-inspired metaheuristic algorithm for global optimization problems. Comput Ind Eng 2021; 158:107408. https://doi.org/https://doi.org/10.1016/j.cie.2021.107408.
There are 58 citations in total.

Details

Primary Language English
Subjects Artificial Intelligence, Software Engineering (Other)
Journal Section Research Articles
Authors

Mustafa Atakan Afşar 0000-0001-7176-6781

Hilal Arslan 0000-0002-6449-6952

Early Pub Date September 30, 2023
Publication Date September 30, 2023
Submission Date March 17, 2023
Published in Issue Year 2023 Volume: 11 Issue: 3

Cite

IEEE M. A. Afşar and H. Arslan, “Optimizing PID Gains of a Vehicle using the state-of-the-art Metaheuristic Methods”, APJESS, vol. 11, no. 3, pp. 107–117, 2023, doi: 10.21541/apjess.1266949.

Academic Platform Journal of Engineering and Smart Systems