Research Article
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Year 2019, Volume: 20 Issue: 1, 1 - 16, 01.01.2019
https://doi.org/10.18038/aubtda.476952

Abstract

References

  • [1] Wang Z, Su Q, Luo X. A novel HTD-CS based PID controller tuning method for time delay continuous systems with multi-objective and multi-constraint optimization. Chemical Engineering Research and Design 2016; 115: 98-106.
  • [2] Srivastava S, Pandit V.S. A PI/PID controller for time delay systems with desired closed loop time response and guaranteed gain and phase margins. Journal of Process Control 2016; 37: 70-77.
  • [3] Ziegler JG, Nichols NB. Optimum settings for automatic controllers. J Dyn Syst Meas Contr 1993; 115(2B): 220–222.
  • [4] Bahavarnia M, Tavazoei MS. A new view to Ziegler–Nichols step response tuning method: analytic non-fragility justification. J Process Control 2013; 23: 23–33.
  • [5] Jin QB, Liu Q, Huang B. New results on the robust stability of PID controllers with gain and phase margins for UFOPTD processes. ISA Trans 2016; 61: 240–250.
  • [6] Mousakazemi SMH, Ayoobian N, Ansarifar GR. Control of the reactor core power in PWR using optimized PID controller with the real-coded GA. Annals of Nuclear Energy 2018; 118: 107-121.
  • [7] Gaing ZL. A particle swarm optimization approach for optimum design of PID controller in AVR system. IEEE Trans. Energy Convers 2004; 19: 384–391.
  • [8] Kao CC, Chuang CV, Fung RF. The self-tuning PID control in a slider–crank mechanism system by applying particle swarm optimization approach. Mechatronics 2006; 16: 513-522.
  • [9] Gozde H, Taplamacioglu MC, Kocaarslan I. Comparative performance analysis of Artificial Bee Colony algorithm in automatic generation control for interconnected reheat thermal power system. International Journal of Electrical Power & Energy Systems 2012; 42: 167-178.
  • [10] Gozde H, Taplamacioglu MC. Comparative performance analysis of artificial bee colony algorithm for automatic voltage regulator (AVR) system. J Frankl Inst 2011; 348: 1927–1946.
  • [11] Blondin MJ, Sanchis J, Sicard P, Herrero JM. New optimal controller tuning method for an AVR system using a simplified Ant Colony Optimization with a new constrained Nelder–Mead algorithm. Applied Soft Computing 2018; 62: 216-229.
  • [12] Dash P, Saikia CL, Sinha N. Comparison of performances of several Cuckoo search algorithm based 2DOF controllers in AGC of multi-area thermal system. International Journal of Electrical Power & Energy Systems 2014; 55: 429-436.
  • [13] Zamani M, Karimi-Ghartemani M, Sadati N, Parniani M. Design of a fractional order PID controller for an AVR using particle swarm optimization. Control Engineering Practice 2009; 17: 1380-1387.
  • [14] Aguila-Camacho N, Duarte-Mermoud MA. Fractional adaptive control for an automatic voltage regulator. ISA Transactions 2013; 52: 807-815.
  • [15] Reynoso-Meza G, Sanchis J, Blasco X, García-Nieto S. Physical programming for preference driven evolutionary multi-objective optimization. Applied Soft Computing 2014; 24: 341-362.
  • [16] Zhou A, Qu BY, Li H, Zhao SZ, Suganthan PN, Zhang Q. Multi-objective evolutionary algorithms: A survey of the state of the art. Swarm and Evolutionary Computation 2011; 1: 32-49.
  • [17] Coello CAC, Lamont GB. Evolutionary Algorithms for Solving Multi-Objective Problems, Boston, MA, USA: Springer, 2007.
  • [18] Zwe-Lee G. A particle swarm optimization approach for optimum design of PID controller in AVR system. IEEE Trans Energy Convers 2004; 19(2): 384–391.
  • [19] Yang X.-S., Deb S. Cuckoo search via Levy flights, in Proc. of World Congress on Nature & Biologically Inspired Computing (NaBIC) IEEE Publications 2009; USA, 210-214.
  • [20] Coello CAC, Pulido GT, Lechuga MS. Handling multiple objectives with particle swarm optimization. IEEE Trans Evol Comput 2004; 8(3): 256–279.
  • [21] Sahib M.A., Ahmed B.S. A new multi-objective performance criterion used in PID tuning optimization algorithms. Journal of Advanced Research 2016; 7 (1): 125-134.
  • [22] Sánchez HS, Visioli A, Vilanova R. Optimal Nash tuning rules for robust PID controllers. Journal of the Franklin Institute 2017; 354: 3945-3970.

APPLICATION OF MULTI-OBJECTIVE CONTROLLER TO OPTIMAL TUNING OF PID PARAMETERS FOR DIFFERENT PROCESS SYSTEMS USING CUCKOO SEARCH ALGORITHM

Year 2019, Volume: 20 Issue: 1, 1 - 16, 01.01.2019
https://doi.org/10.18038/aubtda.476952

Abstract

A time domain performance criterion based on the multi-objective Pareto front solutions is proposed to tune the Proportional-Integral-Derivative (PID) controller parameters with the Cuckoo Search (CS) algorithm for different process systems: first order plus dead time (FOPDT) and high order dynamics. The proposed multi-objective cost function consists of conflicting objective functions including the overshoot, rise time, settling time and steady state error. In this paper, multi-objective genetic algorithm (MOGA) is used for obtaining the Pareto optimal solutions of the conflicting objective functions. The weights in the proposed multi-objective cost function are calculated by way of nondominated solutions of the obtained Pareto fronts based on the four conflicting objective functions. Also, the optimal tuning parameters of the PID controller are obtained by minimizing the integral based objective functions commonly introduced in the literature using the CS algorithm. The obtained results show that the CS optimized approach based on the proposed objective cost function outperforms than that of the integral based objective functions with higher efficiency and better quality no matter whether the process systems are employed under unload or load conditions.

References

  • [1] Wang Z, Su Q, Luo X. A novel HTD-CS based PID controller tuning method for time delay continuous systems with multi-objective and multi-constraint optimization. Chemical Engineering Research and Design 2016; 115: 98-106.
  • [2] Srivastava S, Pandit V.S. A PI/PID controller for time delay systems with desired closed loop time response and guaranteed gain and phase margins. Journal of Process Control 2016; 37: 70-77.
  • [3] Ziegler JG, Nichols NB. Optimum settings for automatic controllers. J Dyn Syst Meas Contr 1993; 115(2B): 220–222.
  • [4] Bahavarnia M, Tavazoei MS. A new view to Ziegler–Nichols step response tuning method: analytic non-fragility justification. J Process Control 2013; 23: 23–33.
  • [5] Jin QB, Liu Q, Huang B. New results on the robust stability of PID controllers with gain and phase margins for UFOPTD processes. ISA Trans 2016; 61: 240–250.
  • [6] Mousakazemi SMH, Ayoobian N, Ansarifar GR. Control of the reactor core power in PWR using optimized PID controller with the real-coded GA. Annals of Nuclear Energy 2018; 118: 107-121.
  • [7] Gaing ZL. A particle swarm optimization approach for optimum design of PID controller in AVR system. IEEE Trans. Energy Convers 2004; 19: 384–391.
  • [8] Kao CC, Chuang CV, Fung RF. The self-tuning PID control in a slider–crank mechanism system by applying particle swarm optimization approach. Mechatronics 2006; 16: 513-522.
  • [9] Gozde H, Taplamacioglu MC, Kocaarslan I. Comparative performance analysis of Artificial Bee Colony algorithm in automatic generation control for interconnected reheat thermal power system. International Journal of Electrical Power & Energy Systems 2012; 42: 167-178.
  • [10] Gozde H, Taplamacioglu MC. Comparative performance analysis of artificial bee colony algorithm for automatic voltage regulator (AVR) system. J Frankl Inst 2011; 348: 1927–1946.
  • [11] Blondin MJ, Sanchis J, Sicard P, Herrero JM. New optimal controller tuning method for an AVR system using a simplified Ant Colony Optimization with a new constrained Nelder–Mead algorithm. Applied Soft Computing 2018; 62: 216-229.
  • [12] Dash P, Saikia CL, Sinha N. Comparison of performances of several Cuckoo search algorithm based 2DOF controllers in AGC of multi-area thermal system. International Journal of Electrical Power & Energy Systems 2014; 55: 429-436.
  • [13] Zamani M, Karimi-Ghartemani M, Sadati N, Parniani M. Design of a fractional order PID controller for an AVR using particle swarm optimization. Control Engineering Practice 2009; 17: 1380-1387.
  • [14] Aguila-Camacho N, Duarte-Mermoud MA. Fractional adaptive control for an automatic voltage regulator. ISA Transactions 2013; 52: 807-815.
  • [15] Reynoso-Meza G, Sanchis J, Blasco X, García-Nieto S. Physical programming for preference driven evolutionary multi-objective optimization. Applied Soft Computing 2014; 24: 341-362.
  • [16] Zhou A, Qu BY, Li H, Zhao SZ, Suganthan PN, Zhang Q. Multi-objective evolutionary algorithms: A survey of the state of the art. Swarm and Evolutionary Computation 2011; 1: 32-49.
  • [17] Coello CAC, Lamont GB. Evolutionary Algorithms for Solving Multi-Objective Problems, Boston, MA, USA: Springer, 2007.
  • [18] Zwe-Lee G. A particle swarm optimization approach for optimum design of PID controller in AVR system. IEEE Trans Energy Convers 2004; 19(2): 384–391.
  • [19] Yang X.-S., Deb S. Cuckoo search via Levy flights, in Proc. of World Congress on Nature & Biologically Inspired Computing (NaBIC) IEEE Publications 2009; USA, 210-214.
  • [20] Coello CAC, Pulido GT, Lechuga MS. Handling multiple objectives with particle swarm optimization. IEEE Trans Evol Comput 2004; 8(3): 256–279.
  • [21] Sahib M.A., Ahmed B.S. A new multi-objective performance criterion used in PID tuning optimization algorithms. Journal of Advanced Research 2016; 7 (1): 125-134.
  • [22] Sánchez HS, Visioli A, Vilanova R. Optimal Nash tuning rules for robust PID controllers. Journal of the Franklin Institute 2017; 354: 3945-3970.
There are 22 citations in total.

Details

Primary Language English
Journal Section Articles
Authors

Oğuzhan Karahan 0000-0003-3588-0354

Banu Ataşlar Ayyıldız 0000-0002-0841-4385

Publication Date January 1, 2019
Published in Issue Year 2019 Volume: 20 Issue: 1

Cite

AMA Karahan O, Ataşlar Ayyıldız B. APPLICATION OF MULTI-OBJECTIVE CONTROLLER TO OPTIMAL TUNING OF PID PARAMETERS FOR DIFFERENT PROCESS SYSTEMS USING CUCKOO SEARCH ALGORITHM. Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering. January 2019;20(1):1-16. doi:10.18038/aubtda.476952