Tawfiq Hussein Elmenfy

Abstract— The main contribution of this proposed study is to control the robot's arm angle by changing the torque of the each joints .The dynamic model of the robot manipulator contain from equations, these equations are coupled differential equations , hardy nonlinear, highly complex, multiple inputs and multiple outputs (MIMO), coupled differential equations ,strong model uncertainties and time-variant . The conventional computed torque controllers are not suitable for nonlinear systems, complex, time-variant systems with delay. In this paper, the suggested control law consists of Fuzzy Logic Control (FLC) tuning via Genetic Algorithm (GA). The FLC used, because it is efficient tools for control of nonlinear and uncertain parameters systems. In this design, GA is mainly presented to find a simultaneous near optimum design of the membership functions, scaling factors ,defuzzification Method ,Inference enginee and control rules. GA with a fitness function in a form of a cumulative response error which is widely utilized as an efficient optimization technique. This paper proposed a methodology to optimize fuzzy logic parameters based on GA. The whole system is simulated using MATLAB Simulink. Through this study it is proved that the optimized fuzzy controller gives near optimum performance in the time response behavior ,When the uncertainty added to the system, it maintained roughly the same level of controller performance .

In this paper, determining the optimal proportional-integral-derivative (PID) controller gains of an single-area load frequency control (LFC) system using genetic algorithm (GA) is presented. The LFC is notoriously difficult to control optimally using conventionally tuning a PID controller because the system parameters are constantly changing. It is for this reason the GA as tuning strategy was applied. The simulation has been conducted in MATLAB Simulink package for single area power system. the simulation results shows the effectiveness performance of under various disturbance.

ABSTRACT This article presents a dynamic simulation of an exothermic batch process using SIMULINK and Mat lab. The work considers design of a standard PID (Proportional- Integral-Derivative) controller with two scenarios of tuning setting. The designed control law is incorporated as a reactor temperature control loop, which usually represents a prime task in the batch process design. The paper shows that in some particular situations it is not necessary to comprise an advanced control methodology to provide efficient performance. The work demonstrates that with the standard control techniques, it is possible to drive the system to follow up a challenging set point trajectory. On the other hand, the simulation files are presented in a way that might be utilized as a benchmark for further studies, whereas the results are illustrated in a comparative manner referred to several previous studies.

Our purpose is to compare the performance of the IEEE standard multi-band power system stabilizer to that of a proposed self-tuned fuzzy stabilizer. The comparison is based on a bench mark system that consists of a 4-machine two-area power system. The objective is to damp local- and inter-area modes of oscillations that appear following large disturbances. The proposed stabilizer is

This paper introduces an indirect adaptive fuzzy controller as a power system stabilizer used to damp inter-area modes of oscillation following disturbances in power systems. Compared to the IEEE standard multi-band power system stabilizer (MB-PSS), indirect adaptive fuzzy-based stabilizers are more efficient because they can cope with oscillations at different operating points. A nominal model of the power system is