Abstract: Sufficient linear matrix inequalities (LMI) conditions for regularization of discrete-time singular systems are given. Then a new class of regularizing stabilizing controllers is discussed. The proposed controllers are the sum of predictive and memoryless state feedbacks. The predictive controller aims to regularizing the singular system while the memoryless state feedback is designed to stabilize the resulting regularized system. A systematic procedure is given to calculate the controller gains through linear matrix inequalities.
Abstract: The control of oxygen flow rate during growth of
titanium dioxide by mass flow controller in DC plasma sputtering
growth system is studied. The impedance of TiO2 films for inductance
effect is influenced by annealing time and oxygen flow rate. As
annealing time is increased, the inductance of TiO2 film is the more.
The growth condition of optimum and maximum inductance for TiO2
film to serve as sensing device are oxygen flow rate of 15 sccm and
large annealing time. The large inductance of TiO2 film will be
adopted to fabricate the biosensor to obtain the high sensitivity of
sensing in biology.
Abstract: This paper describes reactive neural control used to
generate phototaxis and obstacle avoidance behavior of walking
machines. It utilizes discrete-time neurodynamics and consists of
two main neural modules: neural preprocessing and modular neural
control. The neural preprocessing network acts as a sensory fusion
unit. It filters sensory noise and shapes sensory data to drive the
corresponding reactive behavior. On the other hand, modular neural
control based on a central pattern generator is applied for locomotion
of walking machines. It coordinates leg movements and can generate
omnidirectional walking. As a result, through a sensorimotor loop this
reactive neural controller enables the machines to explore a dynamic
environment by avoiding obstacles, turn toward a light source, and
then stop near to it.
Abstract: Variable Structure Control (VSC) is one of the most useful tools handling the practical system with uncertainties and disturbances. Up to now, unfortunately, not enough studies on the input-saturated system with linear-growth-bound disturbances via VSC have been presented. Therefore, this paper proposes an asymp¬totic stability condition for the system via VSC. The designed VSC controller consists of two control parts. The linear control part plays a role in stabilizing the system, and simultaneously, the nonlinear control part in rejecting the linear-growth-bound disturbances perfectly. All conditions derived in this paper are expressed with Linear Matrices Inequalities (LMIs), which can be easily solved with an LMI toolbox in MATLAB.
Abstract: Design of an observer based controller for a class of
fractional order systems has been done. Fractional order mathematics
is used to express the system and the proposed observer. Fractional
order Lyapunov theorem is used to derive the closed-loop asymptotic
stability. The gains of the observer and observer based controller are
derived systematically using the linear matrix inequality approach.
Finally, the simulation results demonstrate validity and effectiveness
of the proposed observer based controller.
Abstract: A modified Genetic Algorithm (GA) based optimal selection of parameters for Automatic Generation Control (AGC) of multi-area electric energy systems is proposed in this paper. Simulations on multi-area reheat thermal system with and without consideration of nonlinearity like governor dead band followed by 1% step load perturbation is performed to exemplify the optimum parameter search. In this proposed method, a modified Genetic Algorithm is proposed where one point crossover with modification is employed. Positional dependency in respect of crossing site helps to maintain diversity of search point as well as exploitation of already known optimum value. This makes a trade-off between exploration and exploitation of search space to find global optimum in less number of generations. The proposed GA along with decomposition technique as developed has been used to obtain the optimum megawatt frequency control of multi-area electric energy systems. Time-domain simulations are conducted with trapezoidal integration along with decomposition technique. The superiority of the proposed method over existing one is verified from simulations and comparisons.
Abstract: This paper emphasizes on the application of genetic algorithm (GA) to optimize the parameters of the TMD for achieving the best results in the reduction of the building response under earthquake excitations. The Integral of the Time multiplied Absolute value of the Error (ITAE) based on relative displacement of all floors in the building is taken as a performance index of the optimization criterion. The problem of robustly TMD controller design is formatted as an optimization problem based on the ITAE performance index to be solved using GA that has a story ability to find the most optimistic results. An 11–story realistic building, located in the city of Rasht, Iran is considered as a test system to demonstrate effectiveness of the proposed GA based TMD (GATMD) controller without specifying which mode should be controlled. The results of the proposed GATMD controller are compared with the uncontrolled structure through timedomain simulation and some performance indices. The results analysis reveals that the designed GA based TMD controller has an excellent capability in reduction of the seismically excited example building and the ITAE performance, that is so for remains as unknown, can be introduced a new criteria - method for structural dynamic design.
Abstract: This work presents a recursive identification algorithm. This algorithm relates to the identification of closed loop system with Variable Structure Controller. The approach suggested includes two stages. In the first stage a genetic algorithm is used to obtain the parameters of switching function which gives a control signal rich in commutations (i.e. a control signal whose spectral characteristics are closest possible to those of a white noise signal). The second stage consists in the identification of the system parameters by the instrumental variable method and using the optimal switching function parameters obtained with the genetic algorithm. In order to test the validity of this algorithm a simulation example is presented.
Abstract: The Wind Turbine Modeling in Wind Energy Conversion System (WECS) using Doubly-Fed Induction Generator (DFIG) PI Controller based design is presented. To study about the variable wind speed. The PI controller performs responding to the dynamic performance. The objective is to study the characteristic of wind turbine and finding the optimum wind speed suitable for wind turbine performance. This system will allow the specification setting (2.5MW). The output active power also corresponding same the input is given. And the reactive power produced by the wind turbine is regulated at 0 Mvar. Variable wind speed is optimum for drive train performance at 12.5 m/s (at maximum power coefficient point) from the simulation of DFIG by Simulink is described.
Abstract: In this paper, based on steady-state models of Flexible
AC Transmission System (FACTS) devices, the sizing of static
synchronous series compensator (SSSC) controllers in transmission
network is formed as an optimization problem. The objective of this
problem is to reduce the transmission losses in the network. The
optimization problem is solved using particle swarm optimization
(PSO) technique. The Newton-Raphson load flow algorithm is
modified to consider the insertion of the SSSC devices in the
network. A numerical example, illustrating the effectiveness of the
proposed algorithm, is introduced. In addition, a novel model of a 3-
phase voltage source converter (VSC) that is suitable for series
connected FACTS a controller is introduced. The model is verified
by simulation using Power System Blockset (PSB) and Simulink
software.
Abstract: In this work, we suggested a new approach for the
control of a mobile robot capable of being a building block of an
intelligent agent. This approach includes obstacle avoidance and goal
tracking implemented as two different sliding mode controllers. A
geometry based behavior arbitration is proposed for fusing the two
outputs. Proposed structure is tested on simulations and real robot.
Results have confirmed the high performance of the method.
Abstract: An approach of design of stable of control systems with ultimately wide ranges of uncertainly disturbed parameters is offered. The method relies on using of nonlinear structurally stable functions from catastrophe theory as controllers. Theoretical part presents an analysis of designed nonlinear second-order control systems. As more important the integrators in series, canonical controllable form and Jordan forms are considered. The analysis resumes that due to added controllers systems become stable and insensitive to any disturbance of parameters. Experimental part presents MATLAB simulation of design of control systems of epidemic spread, aircrafts angular motion and submarine depth. The results of simulation confirm the efficiency of offered method of design. KeywordsCatastrophes, robust control, simulation, uncertain parameters.
Abstract: An optimal control of Reverse Osmosis (RO) plant is
studied in this paper utilizing the auto tuning concept in conjunction
with PID controller. A control scheme composing an auto tuning
stochastic technique based on an improved Genetic Algorithm (GA) is
proposed. For better evaluation of the process in GA, objective
function defined newly in sense of root mean square error has been
used. Also in order to achieve better performance of GA, more
pureness and longer period of random number generation in operation
are sought. The main improvement is made by replacing the uniform
distribution random number generator in conventional GA technique
to newly designed hybrid random generator composed of Cauchy
distribution and linear congruential generator, which provides
independent and different random numbers at each individual steps in
Genetic operation. The performance of newly proposed GA tuned
controller is compared with those of conventional ones via simulation.
Abstract: In this paper a method for designing of nonlinear controller for a fuzzy model of Double Inverted Pendulum is proposed. This system can be considered as a fuzzy large-scale system that includes offset terms and disturbance in each subsystem. Offset terms are deterministic and disturbances are satisfied a matching condition that is mentioned in the paper. Based on Lyapunov theorem, a nonlinear controller is designed for this fuzzy system (as a model reference base) which is simple in computation and guarantees stability. This idea can be used for other fuzzy large- scale systems that include more subsystems Finally, the results are shown.
Abstract: Active power filter continues to be a powerful tool to control harmonics in power systems thereby enhancing the power quality. This paper presents a fuzzy tuned PID controller based shunt active filter to diminish the harmonics caused by non linear loads like thyristor bridge rectifiers and imbalanced loads. Here Fuzzy controller provides the tuning of PID, based on firing of thyristor bridge rectifiers and variations in input rms current. The shunt APF system is implemented with three phase current controlled Voltage Source Inverter (VSI) and is connected at the point of common coupling for compensating the current harmonics by injecting equal but opposite filter currents. These controllers are capable of controlling dc-side capacitor voltage and estimating reference currents. Hysteresis Current Controller (HCC) is used to generate switching signals for the voltage source inverter. Simulation studies are carried out with non linear loads like thyristor bridge rectifier along with unbalanced loads and the results proved that the APF along with fuzzy tuned PID controller work flawlessly for different firing angles of non linear load.
Abstract: A measurement system was successfully fabricated to
detect ion concentrations (hydrogen and chlorine) in this study.
PIC18F4520, the microcontroller was used as the control unit in the
measurement system. The measurement system was practically used
to sense the H+ and Cl- in different examples, and the pH and pCl
values were exhibited on real-time LCD display promptly. In the study,
the measurement method is used to judge whether the response voltage
is stable. The change quantity is smaller than 0.01%, that the present
response voltage compares with next response voltage for H+
measurement, and the above condition is established only 6 sec.
Besides, the change quantity is smaller than 0.01%, that the present
response voltage compares with next response voltage for Clmeasurement,
and the above condition is established only 5 sec.
Furthermore, the average error quantities would also be considered,
and they are 0.05 and 0.07 for measurements of pH and pCl values,
respectively.
Abstract: In this paper an algorithm based on the adaptive
neuro-fuzzy controller is provided to enhance the tipover stability of
mobile manipulators when they are subjected to predefined
trajectories for the end-effector and the vehicle. The controller
creates proper configurations for the manipulator to prevent the robot
from being overturned. The optimal configuration and thus the most
favorable control are obtained through soft computing approaches
including a combination of genetic algorithm, neural networks, and
fuzzy logic. The proposed algorithm, in this paper, is that a look-up
table is designed by employing the obtained values from the genetic
algorithm in order to minimize the performance index and by using
this data base, rule bases are designed for the ANFIS controller and
will be exerted on the actuators to enhance the tipover stability of the
mobile manipulator. A numerical example is presented to
demonstrate the effectiveness of the proposed algorithm.
Abstract: This Paper presents a particle swarm optimization (PSO) method for determining the optimal proportional-integral-derivative (PID) controller parameters, for speed control of a linear brushless DC motor. The proposed approach has superior features, including easy implementation, stable convergence characteristic and good computational efficiency. The brushless DC motor is modelled in Simulink and the PSO algorithm is implemented in MATLAB. Comparing with Genetic Algorithm (GA) and Linear quadratic regulator (LQR) method, the proposed method was more efficient in improving the step response characteristics such as, reducing the steady-states error; rise time, settling time and maximum overshoot in speed control of a linear brushless DC motor.
Abstract: The typical coupled-tanks process that is TITO
plant has the difficulty in controller design because changing
of system dynamics and interacting of process. This paper
presents design methodology of auto-adjustable PI controller
using MRAC technique. The proposed method can adjust the
controller parameters in response to changes in plant and
disturbance real time by referring to the reference model that
specifies properties of the desired control system.
Abstract: This paper presents a fuzzy control system for a three degree of freedom (3-DOF) stabilized platform with explicit decoupling scheme. The system under consideration is a system with strong interactions between three channels. By using the concept of decentralized control, a control structure is developed that is composed of three control loops, each of which is associated with a single-variable fuzzy controller and a decoupling unit. Takagi-Sugeno (TS) fuzzy control algorithm is used to implement the fuzzy controller. The decoupling units design is based on the adaptive theory reasoning. Simulation tests were established using Simulink of Matlab. The obtained results have demonstrated the feasibility and effectiveness of the proposed approach. Simulation results are represented in this paper.