Tuning of a PID Controller using Modified Dynamic Group based TLBO Algorithm

Abstract

This paper presents a new version of Teaching LearningBased Optimization (TLBO) algorithm to find the optimal parameters of Proportional Integral Derivative (PID) controller. The proposed algorithm is an altered version of dynamic group strategy TLBO (DGS-TLBO) and is named as modified dynamic group based TLBO (MDG-TLBO) algorithm. The proposed algorithm is tested on 12 benchmark functions to verify its efficiency over other procedures. The results show that the MDG-TLBO algorithm offers better solution quality and has better convergence rate. Finally, the proposed algorithm is tested on a three-tank liquid-level control system for the optimization of PID gains. The simulation result indicate that the proposed algorithm is an effective method in tuning of PID controllers to obtain better performance measures the error values and the time domain specifications.

Research on Geographic Location Prediction Algorithm Based on Improved Teaching and Learning Optimization ELM

“…Because TLBO has the advantages of few parameters, simple thinking, easy understanding and strong robustness [1][2][3][4], it has attracted the attention of many scholars since it was put forward and has been applied in many fields. Such as reactive power optimization of power system [5], LQR controller optimization [6], IIR digital filter design [7], steelmaking and continuous casting scheduling problem [8], PID controller parameter optimization problem [9,10], feature selection problem [11], HVDC optimization of voltage source converter [12], extension of global optimization technology to constrained optimization [13], analysis of financial time series data [14], neural network optimization [15], etc. Compared with the existing swarm intelligence algorithm, the algorithm obtains better results.…”