Print ISSN: 1681-6900

Online ISSN: 2412-0758

Keywords : fuzzy logic controller

Intelligent Controller for Robot Manipulator

Ivan I. G

Engineering and Technology Journal, 2016, Volume 34, Issue 13, Pages 2551-2565

This paper suggests an intelligent controller to control the manipulator movement in an environment of two and three – dimensional. The fuzzy logic controller of planning structure locally approach constructs of multi-unit. The aim is to transmit or guide the manipulator from the elected to a desired configuration. Modeling, scenarios and simulations are presented clearly in two dimensions and three dimensions together with their analysis which be done using MATLAB software. In addition, the results of the robot navigation in two-dimensional environments also compared with the results of the navigation in three-dimensional environments to clarify the strength of the suggested intelligent controller, where results (in rad) for the third link for both two and three- dimensional environments are minimum: 1.9548×〖10〗^(-4) and -7.452147499×〖10〗^(-4) in the scenario 1 also minimum results in scenario 2 as the following: -0.0061 and -0.0018. Simulation results indicate this manipulator successfully reached the desired goal configuration.

Comparative Study of Temperature Control in a Heat Exchanger Process

Afraa H. Al-Tae; Safa A. Al-Naimi

Engineering and Technology Journal, 2012, Volume 30, Issue 10, Pages 1707-1731

In the present work the dynamic behavior of a plate heat exchanger (PHE)
(single pass counter current consists of 24 plates) studied experimentally and
theoretically to control the system. Different control strategies; conventional
feedback control, classical fuzzy logic control, artificial neural network (NARMAL2)
control and PID fuzzy logic control were implemented to control the outlet
cold water temperature. A step change was carried in the hot water flow rate which
was considered as a manipulated variable. The experimental heat transfer
measurements of the PHE showed that the overall heat transfer coefficient (U) is
related to the hot water flow rate (mh) by a correlation having the form:
U mh
0.7158 =11045
In this work the PHE model was found theoretically as a first order lead and
second order overdamped lag while the experimental PHE represented dynamically
(by PRC method) as a first order with negligible dead time value. A comparison
between the experimental and the theoretical model is carried out and good
agreement is obtained. The performance criteria used for different control modes
are the integral square error (ISE) and integral time-weighted absolute error (ITAE)
where the ITAE gave better performance. As well as the parameters of the step
performance of the system such as overshoot value, settling time and rise time are
used to evaluate the performance of different control strategies. The PID fuzzy
controller gave better control results of temperature rather than PI, PID and
artificial neural network controller since PID fuzzy controller combines the
advantages of a fuzzy logic controller and a PID controller. MATLAB program
version 7.10 was used as a tool of simulation for all the studies mentioned in this