This Paper presents a comparative study of Genetic Algorithm method (GA) and Particle swarm optimization (PSO) method to determine the optimal proportional-integral (PI) and proportional-integral-derivative (PID) controller parameters, for speed control of a Field Oriented Control (FOC) induction motor. The FOC induction motor has been modeled in MATLAB (SIMULINK); the GA and PSO algorithm has ...

Abstract Magnetic levitation is a phenomenon of objects by using magnetic field to defy gravity. an interesting research topic widely used in industry or transportation such as trains, conveyors, lifts, and many more. In general, can be realized regulating the current source. However, due normally unstable power supply source, results instability. Therefore, control system needed stabilize syst...

Molecular beacon deoxyribonucleic acid computing is new research focus of intelligent control theory in recent years, it is also new bionic algorithm. It is well known that a very important problem how to determine or tune the proportional integral derivative controller parameters, because these parameters have a great influence on the stability and the performance of the control system. Parame...

Since random early detection (RED) was proposed in 1993, many active queue management (AQM) algorithms have been proposed to support better end-to-end Transmission Control Protocol (TCP) congestion control. In this article, the authors introduce and analyze a feedback control model of the TCP/AQM dynamics. Then they suggest the concept of an AQM algorithm that can detect and avoid congestion pr...

In this Paper, a novel approach based on Cuckoo search optimization, with dominant eigen value shift for designing robust decentralized load frequency control system for interconnected power system is presented. A proportional integral – derivative (PID) controller is considered to exemplify the optimum parameter search. To demonstrate the robustness of the obtained controller, a two-area non r...

Recently there has been an increasing interest in primal-dual methods for model predictive control (MPC), which require minimizing the (augmented) Lagrangian at each iteration. We propose a novel first order method, termed proportional-integral projected gradient MPC where underlying finite horizon optimal problem both state and input constraints. Instead of Lagrangian, iteration our method onl...