Abstract: In this paper performance of Puma 560
manipulator is being compared for hybrid gradient descent
and least square method learning based ANFIS controller with
hybrid Genetic Algorithm and Generalized Pattern Search
tuned radial basis function based Neuro-Fuzzy controller.
ANFIS which is based on Takagi Sugeno type Fuzzy
controller needs prior knowledge of rule base while in radial
basis function based Neuro-Fuzzy rule base knowledge is not
required. Hybrid Genetic Algorithm with generalized Pattern
Search is used for tuning weights of radial basis function
based Neuro- fuzzy controller. All the controllers are checked
for butterfly trajectory tracking and results in the form of
Cartesian and joint space errors are being compared. ANFIS
based controller is showing better performance compared to
Radial Basis Function based Neuro-Fuzzy Controller but rule
base independency of RBF based Neuro-Fuzzy gives it an
edge over ANFIS
Abstract: The main objective of this paper is to investigate the
enhancement of power system stability via coordinated tuning of
Power System Stabilizers (PSSs) in a multi-machine power system.
The design problem of the proposed controllers is formulated as an
optimization problem. Chaotic catfish particle swarm optimization
(C-Catfish PSO) algorithm is used to minimize the ITAE objective
function. The proposed algorithm is evaluated on a two-area, 4-
machines system. The robustness of the proposed algorithm is
verified on this system under different operating conditions and
applying a three-phase fault. The nonlinear time-domain simulation
results and some performance indices show the effectiveness of the
proposed controller in damping power system oscillations and this
novel optimization algorithm is compared with particle swarm
optimization (PSO).
Abstract: In this paper, we introduce a robust state feedback controller design using Linear Matrix Inequalities (LMIs) and guaranteed cost approach for Takagi-Sugeno fuzzy systems. The purpose on this work is to establish a systematic method to design controllers for a class of uncertain linear and non linear systems. Our approach utilizes a certain type of fuzzy systems that are based on Takagi-Sugeno (T-S) fuzzy models to approximate nonlinear systems. We use a robust control methodology to design controllers. This method not only guarantees stability, but also minimizes an upper bound on a linear quadratic performance measure. A simulation example is presented to show the effectiveness of this method.
Abstract: This work describes a CACSD tool for automatic design of robust controllers for hydraulic turbines. The tool calculates the optimal controller using the MATLAB hinfopt function and it
serves as a practical and effective solution for the laborious task of
designing a different controller for each type of turbine and generator, and different parameters and conditions of the plant. Results of the simulation of a generating unit subject to parameters
variation show the accuracy and efficiency of the obtained robust
controllers.
Abstract: FACTS devices are used to control the power flow, to
increase the transmission capacity and to optimize the stability of the
power system. One of the most widely used FACTS devices is
Unified Power Flow Controller (UPFC). The controller used in the
control mechanism has a significantly effects on controlling of the
power flow and enhancing the system stability of UPFC. According
to this, the capability of UPFC is observed by using different control
mechanisms based on P, PI, PID and fuzzy logic controllers (FLC) in
this study. FLC was developed by taking consideration of Takagi-
Sugeno inference system in the decision process and Sugeno-s
weighted average method in the defuzzification process. Case studies
with different operating conditions are applied to prove the ability of
UPFC on controlling the power flow and the effectiveness of
controllers on the performance of UPFC. PSCAD/EMTDC program
is used to create the FLC and to simulate UPFC model.
Abstract: This paper focuses on a critical component of the situational awareness (SA), the neural control of autonomous constant depth flight of an autonomous underwater vehicle (AUV). Autonomous constant depth flight is a challenging but important task for AUVs to achieve high level of autonomy under adverse conditions. The fundamental requirement for constant depth flight is the knowledge of the depth, and a properly designed controller to govern the process. The AUV, named VORAM, is used as a model for the verification of the proposed hybrid control algorithm. Three neural network controllers, named NARMA-L2 controllers, are designed for fast and stable diving maneuvers of chosen AUV model. This hybrid control strategy for chosen AUV model has been verified by simulation of diving maneuvers using software package Simulink and demonstrated good performance for fast SA in real-time searchand- rescue operations.
Abstract: this paper focuses on designing of PSS and SVC
controller based on chaos and PSO algorithms to improve the
stability of power system. Single machine infinite bus (SMIB) system
with SVC located at the terminal of generator has been considered to
evaluate the proposed controllers where both SVC and PSS have the
same controller. The coefficients of PSS and SVC controller have
been optimized by chaos and PSO algorithms. Finally the system
with proposed controllers has been simulated for the special
disturbance in input power of generator, and then the dynamic
responses of generator have been presented. The simulation results
showed that the system composed with recommended controller has
outstanding operation in fast damping of oscillations of power system.
Abstract: This paper deals with the application of artificial
neural network (ANN) and fuzzy based Adaptive Neuro Fuzzy
Inference System(ANFIS) approach to Load Frequency Control
(LFC) of multi unequal area hydro-thermal interconnected power
system. The proposed ANFIS controller combines the advantages of
fuzzy controller as well as quick response and adaptability nature of
ANN. Area-1 and area-2 consists of thermal reheat power plant
whereas area-3 and area-4 consists of hydro power plant with electric
governor. Performance evaluation is carried out by using intelligent
controller like ANFIS, ANN and Fuzzy controllers and conventional
PI and PID control approaches. To enhance the performance of
intelligent and conventional controller sliding surface is included.
The performances of the controllers are simulated using
MATLAB/SIMULINK package. A comparison of ANFIS, ANN,
Fuzzy, PI and PID based approaches shows the superiority of
proposed ANFIS over ANN & fuzzy, PI and PID controller for 1%
step load variation.
Abstract: This research paper designs a unique motion planner
of multiple platoons of nonholonomic car-like robots as a feasible
solution to the lane changing/merging maneuvers. The decentralized
planner with a leaderless approach and a path-guidance principle
derived from the Lyapunov-based control scheme generates collision
free avoidance and safe merging maneuvers from multiple lanes to a
single lane by deploying a split/merge strategy. The fixed obstacles
are the markings and boundaries of the road lanes, while the moving
obstacles are the robots themselves. Real and virtual road lane
markings and the boundaries of road lanes are incorporated into a
workspace to achieve the desired formation and configuration of the
robots. Convergence of the robots to goal configurations and the
repulsion of the robots from specified obstacles are achieved by
suitable attractive and repulsive potential field functions,
respectively. The results can be viewed as a significant contribution
to the avoidance algorithm of the intelligent vehicle systems (IVS).
Computer simulations highlight the effectiveness of the split/merge
strategy and the acceleration-based controllers.
Abstract: This paper proposes a direct power control for
doubly-fed induction machine for variable speed wind power
generation. It provides decoupled regulation of the primary side
active and reactive power and it is suitable for both electric energy
generation and drive applications. In order to control the power
flowing between the stator of the DFIG and the network, a decoupled
control of active and reactive power is synthesized using PI
controllers.The obtained simulation results show the feasibility
and the effectiveness of the suggested method
Abstract: An integrated vehicle dynamics control system is developed in this paper by a combination of active front steering (AFS) and direct yaw-moment control (DYC) based on fuzzy logic control. The control system has a hierarchical structure consisting of two layers. A fuzzy logic controller is used in the upper layer (yaw rate controller) to keep the yaw rate in its desired value. The yaw rate error and its rate of change are applied to the upper controlling layer as inputs, where the direct yaw moment control signal and the steering angle correction of the front wheels are the outputs. In the lower layer (fuzzy integrator), a fuzzy logic controller is designed based on the working region of the lateral tire forces. Depending on the directions of the lateral forces at the front wheels, a switching function is activated to adjust the scaling factor of the fuzzy logic controller. Using a nonlinear seven degrees of freedom vehicle model, the simulation results illustrate considerable improvements which are achieved in vehicle handling through the integrated AFS/DYC control system in comparison with the individual AFS or DYC controllers.
Abstract: This paper introduces a new method called ARPDC (Advanced Robust Parallel Distributed Compensation) for automatic control of nonlinear systems. This method improves a quality of robust control by interpolating of robust and optimal controller. The weight of each controller is determined by an original criteria function for model validity and disturbance appreciation. ARPDC method is based on nonlinear Takagi-Sugeno (T-S) fuzzy systems and Parallel Distributed Compensation (PDC) control scheme. The relaxed stability conditions of ARPDC control of nominal system have been derived. The advantages of presented method are demonstrated on the inverse pendulum benchmark problem. From comparison between three different controllers (robust, optimal and ARPDC) follows, that ARPDC control is almost optimal with the robustness close to the robust controller. The results indicate that ARPDC algorithm can be a good alternative not only for a robust control, but in some cases also to an adaptive control of nonlinear systems.
Abstract: This paper presents a systematic approach for designing Unified Power Flow Controller (UPFC) based supplementary damping controllers for damping low frequency oscillations in a single-machine infinite-bus power system. Detailed investigations have been carried out considering the four alternatives UPFC based damping controller namely modulating index of series inverter (mB), modulating index of shunt inverter (mE), phase angle of series inverter (δB ) and phase angle of the shunt inverter (δE ). The design problem of the proposed controllers is formulated as an optimization problem and Real- Coded Genetic Algorithm (RCGA) is employed to optimize damping controller parameters. Simulation results are presented and compared with a conventional method of tuning the damping controller parameters to show the effectiveness and robustness of the proposed design approach.
Abstract: This paper presents an approach for the design of
fuzzy logic power system stabilizers using genetic algorithms. In the
proposed fuzzy expert system, speed deviation and its derivative
have been selected as fuzzy inputs. In this approach the parameters of
the fuzzy logic controllers have been tuned using genetic algorithm.
Incorporation of GA in the design of fuzzy logic power system
stabilizer will add an intelligent dimension to the stabilizer and
significantly reduces computational time in the design process. It is
shown in this paper that the system dynamic performance can be
improved significantly by incorporating a genetic-based searching
mechanism. To demonstrate the robustness of the genetic based
fuzzy logic power system stabilizer (GFLPSS), simulation studies on
multimachine system subjected to small perturbation and three-phase
fault have been carried out. Simulation results show the superiority
and robustness of GA based power system stabilizer as compare to
conventionally tuned controller to enhance system dynamic
performance over a wide range of operating conditions.
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: 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: 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: There are various kinds of medical equipment which
requires relatively accurate positional adjustments for successful
treatment. However, patients tend to move without notice during a
certain span of operations. Therefore, it is common practice that
accompanying operators adjust the focus of the equipment. In this
paper, tracking controllers for medical equipment are suggested to
replace the operators. The tracking controllers use AHRS sensor
information to recognize the movements of patients. Sensor fusion is
applied to reducing the error magnitudes through linear Kalman filters.
The image processing of optical markers is included to adjust the
accumulation errors of gyroscope sensor data especially for yaw
angles.
The tracking controller reduces the positional errors between the
current focus of a device and the target position on the body of a
patient. Since the sensing frequencies of AHRS sensors are very high
compared to the physical movements, the control performance is
satisfactory. The typical applications are, for example, ESWT or
rTMS, which have the error ranges of a few centimeters.