International Science Index
Model Reference Adaptive Approach for Power System Stabilizer for Damping of Power Oscillations
In recent years, electricity trade between neighboring countries has become increasingly intense. Increasing power transmission over long distances has resulted in an increase in the oscillations of the transmitted power. The damping of the oscillations can be carried out with the reconfiguration of the network or the replacement of generators, but such solution is not economically reasonable. The only cost-effective solution to improve the damping of power oscillations is to use power system stabilizers. Power system stabilizer represents a part of synchronous generator control system. It utilizes semiconductor’s excitation system connected to the rotor field excitation winding to increase the damping of the power system. The majority of the synchronous generators are equipped with the conventional power system stabilizers with fixed parameters. The control structure of the conventional power system stabilizers and the tuning procedure are based on the linear control theory. Conventional power system stabilizers are simple to realize, but they show non-sufficient damping improvement in the entire operating conditions. This is the reason that advanced control theories are used for development of better power system stabilizers. In this paper, the adaptive control theory for power system stabilizers design and synthesis is studied. The presented work is focused on the use of model reference adaptive control approach. Control signal, which assures that the controlled plant output will follow the reference model output, is generated by the adaptive algorithm. Adaptive gains are obtained as a combination of the "proportional" term and with the σ-term extended "integral" term. The σ-term is introduced to avoid divergence of the integral gains. The necessary condition for asymptotic tracking is derived by means of hyperstability theory. The benefits of the proposed model reference adaptive power system stabilizer were evaluated as objectively as possible by means of a theoretical analysis, numerical simulations and laboratory realizations. Damping of the synchronous generator oscillations in the entire operating range was investigated. Obtained results show the improved damping in the entire operating area and the increase of the power system stability. The results of the presented work will help by the development of the model reference power system stabilizer which should be able to replace the conventional stabilizers in power systems.
Pilot Induced Oscillations Adaptive Suppression in Fly-By-Wire Systems
The present work proposes the development of an adaptive control system which enables the suppression of Pilot Induced Oscillations (PIO) in Digital Fly-By-Wire (DFBW) aircrafts. The proposed system consists of a Modified Model Reference Adaptive Control (M-MRAC) integrated with the Gain Scheduling technique. The PIO oscillations are detected using a Real Time Oscillation Verifier (ROVER) algorithm, which then enables the system to switch between two reference models; one in PIO condition, with low proneness to the phenomenon and another one in normal condition, with high (or medium) proneness. The reference models are defined in a closed loop condition using the Linear Quadratic Regulator (LQR) control methodology for Multiple-Input-Multiple-Output (MIMO) systems. The implemented algorithms are simulated in software implementations with state space models and commercial flight simulators as the controlled elements and with pilot dynamics models. A sequence of pitch angles is considered as the reference signal, named as Synthetic Task (Syntask), which must be tracked by the pilot models. The initial outcomes show that the proposed system can detect and suppress (or mitigate) the PIO oscillations in real time before it reaches high amplitudes.
Model Reference Adaptive Control and LQR Control for Quadrotor with Parametric Uncertainties
A model reference adaptive control and a fixed gain
LQR control were implemented in the height controller of a quadrotor
that has parametric uncertainties due to the act of picking up an
object of unknown dimension and mass. It is shown that an adaptive
controller, unlike the fixed gain controller, is capable of ensuring a
stable tracking performance under such condition, although adaptive
control suffers from several limitations. The combination of both
adaptive and fixed gain control in the controller architecture can
result in an enhanced tracking performance in the presence parametric
CSTR Control by Using Model Reference Adaptive Control and PSO
This paper presents a comparative analysis of
continuously stirred tank reactor (CSTR) control based on adaptive
control and optimal tuning of PID control based on particle swarm
optimization. In the design of adaptive control, Model reference
adaptive control (MRAC) scheme is used, in which the adaptation
law have been developed by MIT rule & Lyapunov’s rule. In PSO
control parameters of PID controller is tuned by using the concept of
particle swarm optimization to get optimized operating point for
minimum integral square error (ISE) condition. The results show the
adjustment of PID parameters converting into the optimal operating
point and the good control response can be obtained by the PSO
Design of Adaptive Controller Based On Lyapunov Stability for a CSTR
Nonlinearity is the inherent characteristics of all the industrial processes. The Classical control approach used for a generation often fails to show better results particularly for non-linear systems and in the systems, whose parameters changes over a period of time for a variety of reasons. Alternatively, adaptive control strategies provide very good performance. The Model Reference Adaptive Control based on Lyapunov stability analysis and classical PI control strategies are designed and evaluated for Continuous Stirred Tank Reactor, which shows appreciable dynamic nonlinear characteristics.
Influence of Adaptation Gain and Reference Model Parameters on System Performance for Model Reference Adaptive Control
This article presents a detailed analysis and comparative
performance evaluation of model reference adaptive control systems.
In contrast to classical control theory, adaptive control methods allow
to deal with time-variant processes. Inspired by the works  and
, two methods based on the MIT rule and Lyapunov rule are
applied to a linear first order system. The system is simulated and
it is investigated how changes to the adaptation gain affect the
system performance. Furthermore, variations in the reference model
parameters, that is changing the desired closed-loop behaviour are
Simulating Discrete Time Model Reference Adaptive Control System with Great Initial Error
This article is based on the technique which is called
Discrete Parameter Tracking (DPT). First introduced by A. A. Azab
 which is applicable for less order reference model. The order of
the reference model is (n-l) and n is the number of the adjustable
parameters in the physical plant.
The technique utilizes a modified gradient method  where the
knowledge of the exact order of the nonadaptive system is not
required, so, as to eliminate the identification problem. The
applicability of the mentioned technique (DPT) was examined
through the solution of several problems.
This article introduces the solution of a third order system with
three adjustable parameters, controlled according to second order
reference model. The adjustable parameters have great initial error
which represent condition.
Computer simulations for the solution and analysis are provided
to demonstrate the simplicity and feasibility of the technique.
Effect of Adaptation Gain on system Performance for Model Reference Adaptive Control Scheme using MIT Rule
Adaptive control involves modifying the control law
used by the controller to cope with the fact that the parameters of the
system being controlled change drastically due to change in
environmental conditions or in system itself. This technique is based
on the fundamental characteristic of adaptation of living organism.
The adaptive control process is one that continuously and
automatically measures the dynamic behavior of plant, compares it
with the desired output and uses the difference to vary adjustable
system parameters or to generate an actuating signal in such a way so
that optimal performance can be maintained regardless of system
changes. This paper deals with application of model reference
adaptive control scheme in first order system. The rule which is used
for this application is MIT rule. This paper also shows the effect of
adaptation gain on the system performance. Simulation is done in
MATLAB and results are discussed in detail.
Variable Structure Model Reference Adaptive Control for Vehicle Steering System
A variable structure model reference adaptive control
(VS-MRAC) strategy for active steering assistance of a two wheel
steering car is proposed. An ideal steering system with fixed
properties and moving on an ideal road is used as the reference
model, and the active steering assistance system is forced to attain
the same behavior as the reference model. The proposed system can
treat the nonlinear relationships between the side slip angles and
lateral forces on tire, and the uncertainties on friction of the road
surface, whose compensation are very important under critical
situations. Simulation results show improvements on yaw rate and
A Variable Structure MRAC for a Class of MIMO Systems
A Variable Structure Model Reference Adaptive Controller using state variables is proposed for a class of multi input-multi output systems. Adaptation law is of variable structure type and switching functions is designed based on stability requirements. Global exponential stability is proved based on Lyapunov criterion. Transient behavior is analyzed using sliding mode control and shows perfect model following at a finite time.