H-GA-PSO Method for Tuning of a PID Controller for a Buck-Boost Converter Modeled with a New Method of Signal Flow Graph Technique

Leila Mohammadian, Ebrahim Babaei, Mohammad Bagher Bannae Sharifian


In this paper, a new method of signal flow graph technique and Mason’s gain formula are applied for extracting the model and transfer functions from control to output and from input to output of a buck-boost converter. In order to investigate necessity of a controller for the converter with assumed parameters, the frequency and time domain analysis is done and the open loop system characteristics are verified. In addition, the needed closed loop controlled system specifications are determined. Moreover, designing a controller for the mentioned converter system based on the extracted model is discussed. For this purpose, a proportional-integral-derivative (PID) controller is designed and the hybrid of genetic algorithm (GA) and particle swarm optimization (PSO), called H-GA-PSO method is used for tuning of the PID controller. Finally, the simulation results are used to show the performance of the proposed modeling and regulation methods.


Buck-boost converter; genetic algorithm; particle swarm optimization; model based controller; PID controller; signal flow graph


Wei X, Tsang KM, Chan WL. DC/DC buck converter using internal model control. Journal of Electr. Power Compo. Sys. 2009; 37(3): 320-330.

Yalamanchili KP, Ferdowsi M, Lu Sh, Xiao P, Corzine K. Derivation of double-input dc-dc power electronic converters. Journal of Electr. Power Compo. Sys. 2011; 39(5): 478-490.

Luo FL, Ye H. Small signal analysis of energy factor and mathematical modeling of power dc-dc converters. IEEE Trans. Power Electron. 2007; 22(1): 69-79.

Priewasser R. Modeling, control and digital implementation of dc-dc converters under variable switching frequency operation. Ph.D. Thesis. 2012; Klagenfurt university.

Kapat S. Control methods for improving the performance of dc-dc converters. Ph.D. Thesis. 2009; Kharagpur, India.

Wong LK, Man TK. Small signal modeling of open-loop SEPIC converters. IET Power Electron. 2010; 3(6): 858-868.

Mashinchi Mahery H, Babaei E. Mathematical modeling of buck–boost dc–dc converter and investigation of converter elements on transient and steady state responses. Electr. Power and Ener. Sys. 2013; 44: 949-963.

Babaei E, Mashinchi Maheri H. Analytical solution for steady and transient states of buck dc–dc converter in CCM. Arab. Journal Sci. and Eng. 2013; 38(12): 3383-3397.

Veerachary M. General rules for signal flow graph modeling and analysis of dc-dc converters. IEEE Trans. Aerospace Electron. Sys. 2004; 40(1): 259–271.

Veerachary M. Signal flow graph modeling of multi-state boost dc–dc converters. IEE -Electr. Power Appl. 2004; 151(5): 583-589.

Veerachary M. Analysis of fourth-order dc-dc converters: A flow graph approach. IEEE Trans. Ind. Electron. 2008; 55(1): 133-141.

Veerachary M, Senjyu T, Uezato K. Signal flow graph nonlinear modeling analysis of IDB converter. in Proc. ISIE, Korea, 2001: 1066-1070.

Stefani RT, Shahian B, Savant CJ, Hostetter GH. Design of feedback control systems. 4th ed. New York, NY: Oxford University Press, 2002.

Zhu M, Luo FL. Super-lift dc-dc converters: graphical analysis and modeling. Journal Power Electron. 2009; 9(6): 854-865.

Luo FL, Ye H. Advanced dc/dc converters. CRC Press, London.

Kamri D, Larbes Ch. Observer-based control for dc–dc converters. Arab. Journal Sci. and Eng. 2014; 39(5): 4089-4102.

Ogata K. Modern Control Engineering. Prentice-Hall of India Press, New Delhi.

Salimi M, Soltani J, Markadeh GhA, Abjadi NR. Adaptive nonlinear control of the dc-dc buck converters operating in CCM and DCM. Int. Trans. Electr. Energy Sys. 2013; 23(8): 1536–1547.

Tsang KM, Chan WL. Non-linear cascade control of dc-dc buck converter. Journal of Electr Power Compo. and Sys. 2008; 36(9): 977-989.

Renaudineau H, Martin J, Mobarakeh BN, Pierfederici S. DC-DC converters dynamic modeling with state observer-based parameter estimation. IEEE Trans. Power Electron. 2014; 99(1): 1-9.

Mohammadian L, Khani S, Mohammadian A, Tarafdar Hagh M, Babaei E. Using a hybrid evolutionary method for optimal planning, and reducing loss of distribution networks. Intl. Res. J. Appl. Basic. Sci. 2012; 3: 2734-2744.

Feng LCh, Feng JCh. Evolutionary fuzzy control of flexible AC transmission system. IEE Proc.-Gener. Transm. Distrib. 2005; 152(4): 441-448.

Full Text: PDF


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.

Bulletin of EEI Stats