### FPID controller design based different optimization Techniques for model order reduction of AVR

#### Abstract

Fractional+ Proportional + Integral + Derivative (FPID) controller configuration is proposed and executed on the decreased request of Automatic Voltage Regulator (AVR) framework utilizing soft-computing(Optimizations) systems, Invasive Weed Optimization (IWO), Differential Evolution (DE), Ant Colony Optimization(ACO), Sine Cosine algorithm(SCA). Minimization of a multi-target work controls these calculations' investigation until the procedure meets with an ideal arrangement. A recreation study is conveyed to analyze the presentation of every one of these methods for controller plan technique. The time-area ideal tuning of model order reduction of (AVR) frameworks were done utilizing Integrated Square Error(ISE),Integrated Time Square Error(ITSE), Integrated Absolute Error (IAE) and Integrated Time Absolute Error(ITAE) as the exhibition lists. The presentation of the FPID controller is approved with best heuristic strategies, (SCA). The aftereffects of FPID controller is additionally contrasted and traditional PID controller. The FPID controller showed strong execution in transient exhibitions, robust performance in transient performances, less settling time, maximum overshot and steady-state error. The FPID controller displays an ISO-damping property (flat reaction).

#### Full Text:

PDF#### References

Vahid. A, Mohammad. B. M and Ahmad. F., Tool position tracking control of a nonlinear uncertain flexible robot manipulator by using robust H2/H∞ controller via T–S fuzzy model, Sadhana,2015, 40(2),p. 307–333.

Vasfi. Emre, , Remzi. Artar, et al., An experimental stationary quadrotor with variable DOF,Sadhana,2013, 38,p. 247–264.

S. N. Deepa and G. Sugumaran, Design of PID controller for higher order continuous systems using MPSO based model formulation technique, Int. J. Electr. Electron. Eng.2011, 5,p. 289–295.

Hassan,K. , Nonlinear Systems, 3, Prentice Hall, Upper Saddle River, 2002.

Suman, S., Saptarshi, D., et al., Design of a fractional order phase shaper for iso-damped control of a PHWR under step-back condition, Nucl. Sci. IEEE Trans.2010, 57,p. 1602–1612.

Suman, S.,Amitava,G,et al., On the selection of tuning methodology of FOPID controllers for the control of higher order processes, ISA Transact.2011, 50,p. 376–388.

MATHWORKS, Simplifying higher-order plant models, http://in.mathworks.com/help/robust/examples/simplifying-higher-order-plant-models.html,2008.

Savo, D., Andrija,T., Dynamic model reduction: An overview of available techniques with application to power systems, Serbian J. Electr. Eng.2012, 9,p. 131–169.

Katsuhiko,O., Modern Control Engineering, Prentice-Hall, Englewood Cliffs, 1970.

Ritu, R.,Rajani,K., Fuzzy Self-tuning of Conventional PID Controller for High-Order Processes, vol. 247 of Advances in Intelligent Systems and Computing, Springer International Publishing, 2014.

S. Vaishnav and Z. Khan, Design and performance of PID and fuzzy logic controller with smaller rule set for higher order system,in Proceedings of the World Congress on Engineering and Computer Science, p. 2426, 2007.

Bruce, M., Principal component analysis in linear systems: Controllability, observability, and model reduction, Automat. Contr.IEEE Trans.,1981, 26(1),p. 17-32.

Michael, S., Richard,C., A Schur method for balanced-truncation model reduction, Automatic Contr. IEEE Transact.,1989, 34 (7),p.729-733.

Amir, H. , Xin-She, Y. , et al., Metaheuristic Algorithms in Modeling and Optimization, in a book Met heuristic Applications in Structures and Infrastructures,2013.

Zhao, D. X.,Yang. Q. C., A fractional order PID tuning algorithm for a class of fractional order plants, in Mechatronics and Automation, 2005 IEEE International Conference, vol. 1, pp. 216221, IEEE, 2005.

Blas, M., Concepcion, A. ,et al., Fractional PID controllers for industry application. A brief introduction, J. Vibr. Contr.,2007, 13(9-10), p.1419-1429.

Concepcion, A., Blas, M., et al., Tuning and auto-tuning of fractional order controllers for industry applications, Contr. Eng. Pract.2008, 16,p.798–812.

Cao, J. Y, Liang, J. et al., Optimization of fractional order PID controllers based on genetic algorithms, in Machine Learning and Cybernetics, Proceedings of 2005 International Conference on, vol. 9, pp. 5686-5689, IEEE.

Chang,L.,Chen,H., Tuning of fractional PID controllers using adaptive genetic algorithm for active magnetic bearing system, WSEA Trans. Syst.2009, 8(1),p. 158-167.

Suman, S.,Amitava,G, et al., Improved model reduction and tuning of fractional-order controllers for analytical rule extraction with genetic programming, ISA Transact.,2012, 51(2), p.237 -261.

Cao,J.Y., Cao,B.J., Design of fractional order controllers based on particle swarm optimization, Int. J. Contr. Automat. Syst,2006.4(6), p.775–782.

Karimi,G., Zamani, M., et al., An optimal fractional order controller for an AVR system using particle swarm optimization algorithm, in Power Engineering, 2007 Large Engineering Systems Conference on, pp. 244249, IEEE,2007.

Biswas, A., Das, S.,et al., Design of fractional-order PIDcontrollers with an improved differential evolution, Eng. Appl. Artific. Intel.,2009, 22(2),p. 343-350.

Lee, C.H., Chang, F., Fractional-order PID controller optimization via improved electromagnetism-like algorithm, Exper.Syst. Appl. 37(12), 8871-8878 (2010).

Yang,Q. , Tripti,B., et al., Practical tuning rule development for fractional order proportional and integral controllers, Journal of Computational and Nonlinear Dynamics.,2008, 3(2), p. 021403-1-021403-8.

De Keyser, R., Muresan, C., et al., A novel auto-tuning method for fractional order PI/PD controllers, ISA Transact.2016,62,p. 268–275.

Godweena,A., Sundaravadivu,K., IMC based tuning of fractional order controller PIDcontroller, in Circuit, Power and Computing Technologies (ICCPCT), 2015 International Conference on, pp. 18, IEEE, 2015.

Prabha,K., Power System Stability and Control,NewYork,McGraw-Hill,1994.

Zamani,M., Karimi,G. ,et al., Design of a fractional order PID controller for an AVR using particle swarm optimization, Journal of IFAC, the International Federation of Automatic Control, Control Engineering Practice,2009,17,p.1380-1386.

Nasir A. Alawad,Nora G. Rahman,Particle Swarm Optimization with Pade Approximation Based-Model Reduction of Automatic Voltage Regulator,2019,2(5),p. 1-11.

Sreelakshmi,A., Riya, S., Biogeography-Based Optimization for the solution of the Combined Heat and Power Economic Dispatch Problem, International Journal of Engineering and Innovative echnology,2013,3(1),p.429-432.

Xue D. and Chen Y. A comparative introduction of four fractional order controllers. Intelligent Control and Automation, 2002.Proceedings of the 4th World Congress on, IEEE, 4,p. 3228-3235.

Podlubny I. Fractional-order systems and fractional-order controllers. Institute of Experimental Physics, Slovak Academy of Sciences, Kosice, 1994,12(3),p.1-18.

Merrikh,B., Rules for selecting the parameters of oustaloup recursive approximation for the simulation of linear feedback systems containing PIDcontroller, Commun. Nonlin. Sci. Numer. Simul.,2012, 17(4),p. 1852 - 1861.

Alain,O., Francois, L., et al., Frequency-band complex noninteger differentiator: characterization and synthesis, Circuits and Systems I: Fundamental Theory and Applications, IEEE Transact. 47(1), 25-39 (2000).

Monje C.A., Vinagre B.M., et al. Tuning and auto-tuning of fractional order controllers for industry applications, Control Engineering Practice.,2008, 16,p. 798–812.

Das S., Saha S., Das S., Gupta A., On the selection of tuning methodology of FOPID controllers for the control of higher order processes, ISA Transactions.,2011, 50,p. 376–388.

Pan I., Das S., Chaotic multi-objective optimization based design of fractional order PIλDμ controller in AVR system, International Journal of Electrical Power & Energy Systems.,2012,43(1),p. 393–407.

J. Y. Cao, J. Liang and B. G. Cao, Optimization of fractional order PID controllers based on genetic algorithms, in Machine Learning and Cybernetics, Proceedings of 2005 International Conference on, vol. 9, p. 5686-5689, IEEE.

Cao,J.Y., Cao,B.J, Design of fractional order controllers based on particle swarm optimization, Int. J. Contr. Automat. Syst.,2006,4(6),p. 775–782.

Arijit, B. , Swagatam D. ,et al., Design of fractional-order PIl Dm controllers with an improved differential evolution, Engineering Applications of Artificial Intelligence,2009,22,p. 343–350.

Khalilpour ,R., Razmjooy ,A., et al., Optimal Control of DC motor using Invasive Weed Optimization (IWO) Algorithm, Majlesi Conference on Electrical Engineering, Majlesi New Town, Isfahan, Iran,2011.

Rainer. S.,Kenneth,P.,Differential Evolution, A Simple and Efficient Heuristic Strategy for Global Optimization over Continuous Spaces, Journal of Global Optimization,1997, 11, Dordrecht, p. 341-359.

Zhang, J., Hu, X.,et al., Implementation of an Ant Colony Optimization technique for job shop scheduling problem Transactions of the Institute of Measurement and Control.,2006 28, p.1 93-108.

Seyedali, M., SCA: A Sine Cosine Algorithm for Solving Optimization Problems, Knowledge-Based Systems, 2016,96,p. 120-133,doi: 10.1016/j.knosys.2015.12.022

### Refbacks

- There are currently no refbacks.

Abava Absolutech Convergent 2020

ISSN: 2307-8162