Print ISSN: 1681-6900

Online ISSN: 2412-0758

Keywords : Adaptive Control

Comparison between Using FLC and Auto-Tuning FLC in Synchronous Generator Transient Voltage Stability Enhancement

Majli Nema Hawas; Abdullah Yaseen Abbas

Engineering and Technology Journal, 2013, Volume 31, Issue 8, Pages 1474-1482

This paper compares between responses of Fuzzy Logic Controller (FLC) based exciter and auto-tuning FLC based exciter. Both of the exciters are simulated separately with synchronous generator connected to infinite bus through a short transmission line. The systems where subjected to three phase fault at the infinite bus, maximum Integral of Square Error (ISE) of generator terminal voltage response and critical clearing time were taken as performance indices of the exciters. The systems then studied under normal operating condition to justify the need of auto-tuning.

Control on 3-D Fixable Wing Flutter Using an Adaptive Neural Controller

Mauwafak Ali Tawfik; Mohammed Idris Abu-Tabikh; Hayder Sabah Abd Al-Amir

Engineering and Technology Journal, 2012, Volume 30, Issue 16, Pages 2858-2874

An adaptive neural controller to control on flutter in 3-D flexible wing is
proposed. The aeroelastic model was based on the coupling between structure-of the
equivalent plate (wing) and the aerodynamic model that is based on a hybrid unsteady
panel methodTime domain simulations were used to examine the dynamic aeroelastic
instabilities of the system (e.g. the onset of flutter and limit cycle oscillation). The
structure of the controller consists of two models namely modified Elman neural
network (MENN) and feedforward multi-layer Perceptron (MLP). The MENN model
is trained with off-line and on-line stages to guarantee that the outputs of the model
accurately represent the plunge motion of the wing and this neural model acts as the
identifier. The feedforward neural controller is trained off-line and adaptive weights
are implemented on-line to find the generalized control action (function of addition
lift force), which controls the plunge motion of the wing. The general back
propagation algorithm is used to learn the feedforward neural controller and the
neural identifier. The simulation results show the effectiveness of the proposed
control algorithm; this is demonstrated by the minimized tracking error to zero
approximation with very acceptable settling time.

Operation and Ph Control of A Wastewater Treatment Unit Using Labview

Adnan Abdual Arazak; Farooq A. Mehdi; Ghanim M. Alwan

Engineering and Technology Journal, 2010, Volume 28, Issue 17, Pages 5524-5546

LABVIEW is a powerful and versatile graphical programming language that
had its roots in operation, automation control and data acquisition of the system. The pH control system of a non-linear wastewater treatment unit, contains heavy metals (Cu, Cr, Cd, Fe, Ni and Zn), had been developed depending on dynamics behavior of the process. The pH value of wastewater is change by addition chemicals (lime or Na2S). The semi-batch pH process system dynamically behaved as a first order lag with dead time. The tuning of control parameters was carried by
several methods; Internal Model Control (IMC), Minimum (ITAE) criteria and Adaptive mode. Since the process was fast, the Integral of Absolute of Error (IAE) criteria was used to compare between the above tuning methods. Adaptive control was the best and effective to determining the values of proportional gain (Kc), Integral time constant (t I) and Derivative time constant (t D ).PI mode was found to be the best for control the fast pH process.