This paper a new method for the control of Two linkrobotic manipulator systems using Neural Network has been presented, The first method is based on Proportional-Derivative controller, The second method is based on Proportional-IntegralDerivative controller, the third method is based on artificial Neural Network by PD controller, and the forth method is based on artificial Neural Network by PID...

There have been many studies on the methods used to control DC motors, one of which is proportional-integral-derivative (PID) method. The tuning procedure carried out by trial and error whose results are not necessarily correct, overcome this problem an alternative needed that can achieve a better faster value for process. Our contribution explained conventional PID (Ziegler - Nichols) modifica...

In this work, Artificial Intelligence (AI) based Ant Colony Optimization (ACO) algorithm is proposed for Load Frequency Control (LFC) of interconnected multi–area hydrothermal power systems. Area 1&2 are thermal power systems and area 3 is a hydro power system, all the areas are interconnected through the appropriate tie-line. Thermal and hydro power plants are applied with reheat turbine and e...

This paper  presents a nonlinear controller for a Distribution Static Compensator (DSTATCOM) of a microgrid incorporating the Distributed Generation (DG) units. The nonlinear control has been designed based on partial feedback linearization theory and Proportional-Integral-Derivative (PID) controllers try to adjust the voltage and trace the output. This paper has proposed a combination of a fuz...

We present a novel simplest fuzzy PID controller using a modified Takagi – Sugeno (TS) rule base. Mathematical model of the controller is derived and analyzed. The unique features of the controller are: (i) the consequent part of the rule base is closely related to the rule base of Mamdani type controller, and (ii) the proportional, integral and derivative gains vary as the output of the system...

This paper proposes a robust intelligent tracking controller (RITC) for a class of unknown nonlinear systems. The proposed RITC system is comprised of a neural controller and a robust controller. The neural controller is designed to approximate an ideal controller using a proportional–integral–derivative (PID)-type learning algorithm in the sense of Lyapunov function, and the robust controller ...

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Proportional – Integral – Derivative control schemes continue to provide the simplest and effective solutions to most of the control engineering applications today. However PID controller is poorly tuned in practice with most of the tuning done manually which is difficult and time consuming. This research comes up with a soft computing approach involving Genetic Algorithm, Evolutionary Programm...

A strategy was proposed to determine the optimal operating point for the proportional-integral-derivative (PID) controller of a wind turbine, and identify the stability regions in the parameter space. The proposed approach combined particle swarm optimization (PSO) and radial basis function neural network (RBFNN) algorithms. These intelligent algorithms are artificial learning mechanisms that c...

Many areas in power systems require solving one or more nonlinear optimization problems. Particle swarm optimization (PSO), part of the swarm intelligence family, is known to effectively solve large-scale nonlinear optimization problems. This paper presents a detailed overview of the basic concepts of PSO and its variants. The proposed method utilizes the Particle Swarm Optimization (PSO) algor...