An efficient memetic algorithm for parameter tuning of PID controller in AVR system

In recent years, there has been a growing interest in real world application of heuristic methods. Memetic Algorithm (MA) is one of such effective heuristics. In this paper, we represent an efficient MA for determining optimal proportional-integral-derivative (PID) controller parameters of an AVR system. This MA is developed by combining a competitive variant of Deferential Evolution (DE) and a Local Search method. The proposed method has excellent features, such as easy implementation, stable convergence characteristic and good computational efficiency. Fast tuning of PID controller parameters results in far better performance of the controller. Performance of our proposed algorithm is compared with other famous heuristics and the simulation results clearly indicate that our proposed approach is indeed more efficient and robust in improving the step response of an AVR system.

[1]  Jing J. Liang,et al.  Comprehensive learning particle swarm optimizer for global optimization of multimodal functions , 2006, IEEE Transactions on Evolutionary Computation.

[2]  Hadi Saadat,et al.  Power System Analysis , 1998 .

[3]  Francisco Herrera,et al.  MA-SW-Chains: Memetic algorithm based on local search chains for large scale continuous global optimization , 2010, IEEE Congress on Evolutionary Computation.

[4]  A. Kai Qin,et al.  Self-adaptive Differential Evolution Algorithm for Constrained Real-Parameter Optimization , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[5]  Janez Brest,et al.  Self-Adaptive Differential Evolution Algorithm in Constrained Real-Parameter Optimization , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[6]  A. Visioli Tuning of PID controllers with fuzzy logic , 2001 .

[7]  E. Ebrahimi,et al.  Self-adaptive memetic algorithm: an adaptive conjugate gradient approach , 2004, IEEE Conference on Cybernetics and Intelligent Systems, 2004..

[8]  Marzuki Khalid,et al.  Tuning of a neuro-fuzzy controller by genetic algorithm , 1999, IEEE Trans. Syst. Man Cybern. Part B.

[9]  Yoshikazu Fukuyama,et al.  A particle swarm optimization for reactive power and voltage control considering voltage security assessment , 2000 .

[10]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[11]  Roger J.-B. Wets,et al.  Minimization by Random Search Techniques , 1981, Math. Oper. Res..

[12]  Renato A. Krohling,et al.  Design of optimal disturbance rejection PID controllers using genetic algorithms , 2001, IEEE Trans. Evol. Comput..