Modified neural network algorithm based robust design of AVR system using the Kharitonov theorem

This paper proposes a new control law for designing an optimal proportional-integral-derivative (PID) controller for flexible automatic voltage regulator (AVR) system using modified neural network algorithm (MNNA). First, the exploration capability of neural network algorithm (NNA) is enhanced by addition of a learning factor, alpha in MNNA. Then, to evaluate the performance of MNNA in terms of its exploration and exploitation capabilities, extensive statistical analysis has been carried out on 23 benchmark functions consisting of unimodal, multimodal and fixed dimension multimodal functions against 12 state of the art algorithms. The results are encouraging and marks the superiority of MNNA against NNA as well as 11 other state of the art techniques. It is followed by application of Kharitonov theorem as a design tool to derive the Interval AVR system. Next, NNA and MNNA have been used for tuning of PID controller parameters in such a way that maximum value of the closed loop eigen values of K-extreme polynomials is minimized. Further to show the robustness of the proposed methods, the results are taken on set point tracking, noise suppression, load disturbance rejection, and minimum controller effort utilized by these controllers and compared against seven state of the art techniques namely PSO, GA, ABC, MOEO, NSGA-II, FSA and variants of constrained GA. The results demonstrate the high performance of the PID controller optimized using MNNA for control of I-AVR and guarantees stability for a wider range of system parameter uncertainty.