CONTROLLER TUNING APPROACH WITH TLBO ALGORITHM FOR THE AUTOMATIC VOLTAGE REGULATOR SYSTEM

Abstract

In this paper, an optimal parameter set tuning method for proportional-integral-derivative (PID) controller and fractional order PID controller is proposed using teaching-learning based optimization (TLBO) algorithm. During the optimization of the PID and FOPID controller parameters, an objective function consisting of overshoot, rise time, settling time and steady state error is formulated to achieve a satisfactory trade-off between the dynamic response characteristics. TLBO algorithm is used as the optimizer to find the best parameters of the proposed controllers. The designed PID and FOPID controllers are applied to an automatic voltage regulator (AVR) system. The performances of the proposed controllers are confirmed by comparing their results with those obtained from different optimized PID and FOPID controllers previously published in the literature for the same AVR system. The numerical simulations and comparisons show that the proposed controllers provide the better dynamic response characteristics as well as more robust to model uncertainties than the other different optimized controllers. The results obtained with the proposed controllers show a better trade-off between the set point tracking performance, robustness and stability.

Keywords

• AVR system,TLBO Algorithm,PID control,FOPID control

References

1. Wong C-C, Li S-A, Wang H-Y. Hybrid evolutionary algorithm for PID controller design of AVR system, Journal of the Chinese Institute of Engineers, 2009; 32 (2): 251-264.
2. Gaing Z-L. A particle swarm optimization approach for optimum design of PID controller in AVR system, IEEE Trans. Energy Convers., 2004; 19 (2): 384-391.
3. Mohanty P-K, Sahu B-K, Panda S. Tuning and assessment of proportional–integral– derivative controller for an automatic voltage regulator system employing local unimodal sampling algorithm, Electr. Power Compon. Syst., 2014; 42 (9): 959-969.
4. Zhu H, Li L, Zhao Y, Guo Y, Yang Y. CAS algorithm-based optimum design of PID controller in AVR system, Chaos Solitons Fractals, 2009; 42 (2): 792-800.
5. Panda S, Sahu B-K, Mohanty P-K. Design and performance analysis of PID controller for an automatic voltage regulator system using simplified particle swarm optimization, Journal of the Franklin Institute, 2012; 349 (8): 2609-2625.
6. Gozde H, Taplamacioglu M C. Comparative performance analysis of artificial bee colony algorithm for automatic voltage regulator (AVR) system, J. Frankl. Inst., 2011; 348: 1927-1946.
7. Kim D H. Hybrid GA–BF based intelligent PID controller tuning for AVR system, Applied Soft Computing, 2011; 11 (1); 11-22.
8. Chatterjee S, Mukherjee V. PID controller for automatic voltage regulator using teaching–learning based optimization technique, Electr. Power Energy Syst., 2016; 77: 418-429.

9. Blondin M J, Sanchis J, Sicard P, Herrero J M. New optimal controller tuning method for an AVR system using a simplified ant colony optimization with a new constrained Nelder–Mead algorithm, Appl. Soft Comput., 2018; 62: 216-229.
10. Mouayad A S. A novel optimal PID plus second order derivative controller for AVR system, Engineering Science and Technology, an International Journal, 2015; 18 (2): 194-206,
11. Bingul Z, Karahan O. A novel performance criterion approach to optimum design of PID controller using cuckoo search algorithm for AVR system, Journal of the Franklin Institute, 2018; 355 (13): 5534-5559.
12. Podlubny I. Fractional-order systems and PI^λ D^μ controllers, IEEE Transactions on Automatic Control, 1999; 44 (1): 208-2014.
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 (12): 1380-1387.
14. Tang Y, Cui M, Hua C, Li L, Yang Y. Optimum design of fractional order PI^λ D^μ controller for AVR system using chaotic ant swarm, Expert Systems with Applications, 2012; 39 (8): 6887-6896.
15. Pan I, Das S. Chaotic multi-objective optimization based design of fractional order PI^λ D^μ controller in AVR system, International Journal of Electrical Power & Energy Systems, 2012; 43 (1): 393-407.
16. Sikander A, Thakur P, Bansal R C, Rajasekar S. A novel technique to design cuckoo search based FOPID controller for AVR in power systems, Computers & Electrical Engineering, 2018; 70: 261-274.
17. Suri babu A G, Chiranjeevi B T. Implementation of Fractional Order PID Controller for an AVR System Using GA and ACO Optimization Techniques, IFAC-PapersOnLine, 2016; 49 (1): 456-461.
18. Zhang D L, Tang Y G, Guan X P. Optimum Design of Fractional Order PID Controller for an AVR System Using an Improved Artificial Bee Colony Algorithm, Acta Automatica Sinica, 2014; 40 (5): 973-979.
19. Rao R V, Savsani V J, Vakharia D P. Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems, Computer-Aided Design, 2011; 43 (3), 303-315.
20. Oustaloup A. La commande CRONE: commande robuste d’ordre non entire, Herme’s, Paris, 1991.