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.

Satellite Tracking Control System Using Optimal Variable Coefficients Controllers Based on Evolutionary Optimization Techniques

“…In this research, Particle Swarm Optimization (PSO) [5], Gravitational Search Algorithm with Particle Swarm Optimization (GSA-PSO) [6], and Eagle Strategy with Particle Swarm Optimization (ES-PSO) [7] are considered for optimal tuning of PID, FOPID and V-FOPID controllers in satellite control system. Performance index is calculated using dynamic performance indices based objective functions [8]. The fundamentals of these suggested performance index and evolutionary algorithms are demonstrated as follows:…”

“…The most essential step optimum controller design is selecting the most appropriate objective function. Time domain objective functions are divided into two main categories: Integral based objective functions and dynamic performance indices based objective functions [8].For controller optimal design this study employed dynamic performance indices based objective function (𝐽). Also, it known as multi objective function given by Eq.…”

Slime Mold Algorithm Based Hybridized Artificial Neural Network Model for Efficient Automatic Voltage Regulation Control

“…In several recent research works, the researchers have updated methodologies to improve AVR control more efficiently such as Particle Swarm Optimization (PSO) [6][7][8], Salp Swarm Optimization algorithm [9,10], Teaching-Learning-Based Optimization (TLBO) [11,12], Cuckoo Search Algorithm [13][14], Sine-cosine algorithm [15][16], and neural network [17][18][19].…”