Nonlinear System Identification Using Functional Link Multilayer Perceptron Artificial Neural Networks

Analytical FOPID controller design based on Bode’s ideal transfer function for desired closed loop response

In recent years, the fractional calculus has been studied for its wide range of applications due to its inherent stability region extension property. This article presents a tuning method of fractional order PID controllers (FOPID) for a class of feedback control system. The overall transfer function is equivalent to a desired FOPID model. The desired FOPID model is equivalent to Bode’s ideal function. Due to the robustness and iso-damping property of Bode’s ideal function, it is extensively used in the area of fractional order control. Analytically, the estimation of the five parameters are carried out through step response of stable process. The tuning techniques formulations is derived and the efficacy is tested by taking suitable examples.

Design and Analysis of Electric Vehicle with Battery System

In this work, our focus on the Electrical Vehicle with Battery System. With the development of technology, there is a huge change in the automobile industry. The automation of the vehicle and the safety is our key issue to be addressed. Apart from this the automobile industry also looking for alternative fuel-driven vehicles than petrol and diesel. The electric vehicle is a better solution in this direction. The electric vehicle is a better solution for alternative fuel and its non-pollution nature. So, more research is going on, this green technology. The pollution is a key factor, as the pollution contribution from road and transport is stood second than the industrialization. So the electrical vehicle is a good solution regarding this. Here our work is to model an electrical vehicle and its battery system i.e., the power distribution to all battery-driven components of an Electrical Vehicle.    

Robust design of FACTS controller using AI technique

Recent development of power electronics introduces the use of Flexible AC Transmission
System (FACTS) controllers in power systems. FACTS controllers are capable of controlling the
network condition in a very fast manner and this unique feature of FACTS can be exploited to
improve the stability of a power system. Static Synchronous Series Compensator (SSSC) is one
of the important members of FACTS family that is increasingly applied by the utilities in modern
power systems with long transmission lines. It can be considered as a synchronous voltage
source as it can inject an almost sinusoidal voltage of variable and controllable amplitude and
phase angle, in series with a transmission line. The present dissertation contributes in the area of
power system stability improvement using a SSSC-based controller.
A conventional lead-lag controller structure is preferred by the power system utilities because
of the ease of on-line tuning and also lack of assurance of the stability by some adaptive or
variable structure techniques. The problem of SSSC controller parameter tuning is a complex
exercise. A number of conventional techniques have been reported in the literature pertaining to
design problems of conventional power system stabilizers namely: the eigenvalue assignment, mathematical programming, gradient procedure for optimization and also the modern control
theory. Unfortunately, the conventional techniques are time consuming as they are iterative and
require heavy computation burden and slow convergence. In addition, the search process is
susceptible to be trapped in local minima and the solution obtained may not be optimal.
In recent years, one of the most promising research field has been “Heuristics from
Nature”, an area utilizing analogies with nature or social systems. Among these heuristic
techniques, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Differential
Evolution (DE) techniques appeared as promising algorithms for handling the optimization
problems. Optimization techniques such as GA, PSO and DE are inspired by nature, and have
proved themselves to be effective solutions to optimization problems. Among the modern
heuristic optimization techniques, the GA algorithm is quite popular among the researchers due
to its easy methods of implementation andnegligible parameter tuning made. GA optimization
technique has been applied for designing a Static Synchronous Series Compensator (SSSC)-
based controller. The design objective is to enhance the power system stability. Phillips-Herffron
model of a Single-Machine Infinite-Bus (SMIB) power system equipped with SSSC controller is
used to model the system in these studies. The design problem is formulated as an optimization
problem and GA optimization technique is employed to search for optimal controller parameters.
To show the superiority of the proposed approach, the performance of GA optimization
technique is compared with a recently published paper in which Phase Compensation
Technique(PCT) has been applied for the SSSC-based controller design Simulation results are
presented for various operating condition and parameter variation under various disturbances to
show the effectiveness and superiority of the GA optimization technique over the recently
published PCT technique for a SSSC-based controller design.

Braking System in E-vehicle

Explained about the different braking system for EV.

Battery charging system for EV

Explained about the different battery charging system for EV.

EV Charging System: An Overview

Electric vehicles is a new technology has emerging in transportation sector to counter the drawbacks of conventional vehicle in terms of economic and environmental. Now a days, battery is the single source of energy, which plays the vital role in the transportation sector. The battery gets charged from an external source, like grid, solar etc. Mainly, the nature of source is an alternating current (AC), which needs to convert to DC with the help of rectifier and feed to DC-DC converter for fast charging. There are two types of charger provided by the manufacturer, i.e., On-board and Off-board charger. This paper presents the various charging infrastructure including battery charger, charging station and it focused on the technology behind the DC fast charging. A comparative study has been given based on electric range, charger power and charging time.

Comparative Analysis of Indirect Vector Control of Induction Motor Drive using PI and PID Controllers

The most common type of speed controller to be used the speed control of induction motor is conventional proportional
integral (PI) controller. But this paper presents a performance based on comparative study of conventional proportional integral (PI)
controller and proportional integral derivative (PID) controller. The induction motor well known as its robustness, relatively low
cost, good reliability and efficiency. But induction motor also characterized by complex, highly non-linear and time varying
dynamics. Hence their speed control is challenging problem in the industry. The approach of vector control techniques has solved
induction speed control problems. Simulation is carried out in MATLAB/Simulink environment and results are compared for speed
control of induction motor PI controller and with PID controller.

Comparative Analysis of scalar & vector control of Induction motor through Modeling & Simulation

The main objective is implementation of scalar & vector control of three phase induction motor drives.
Scalar control as the name indicates is due to magnitude variation of control variable only disregards any coupling
effect in the induction machine. The scalar control is very simple method for controlling the speed of induction motor
compared to the vector control which is more complex. Vector control is completely mathematical model on control of
torque and speed of a three-phase indirect vector controlled induction motor drive. In this paper, an implementation of
speed control of an induction motor (IM) using indirect vector control method has been developed and simulated. The
comparative study of VCIM and conventional v/f control of IM is done this work. The VCIM drive involves
decoupling of the stator current component which produces torque and flux of induction motor. It is seen that it
provides smooth speed control and compared to v/f control. Finally comprise the result of scalar and vector control
technique.

Speed Control of Induction Motor using Scalar Control Technique

This paper presents design and implementation of scalar
control of induction motor. This method leads to be able to
adjust the speed of the motor by control the frequency and
amplitude of the stator voltage of induction motor, the ratio of
stator voltage to frequency should be kept constant, which is
called as V/F or scalar control of induction motor drive. This
paper presents a comparative study of open loop and close
loop V/F control induction motor. The V/F control is based on
advent of stator voltage derivatives. Simulation is carried out
in MATLAB/SIMULINK environment and results are
compared for speed control of induction motor.

Identification of Real-Time Maglev Plant using Long-Short Term Memory Network based Deep Learning Technique

Deep neural network has emerged as one of the most effective networks for modeling of highly non-linear complex
real-time systems. The long-short term memory network (LSTM) which is a one of the variants of recurrent neural network
(RNN) has been proposed for the identification of a highly nonlinear Maglev plant. The comparative analysis of its
performance is carried out with the functional link artificial neural network- least mean square (FLANN-LMS),
FLANN-particle swarm optimization (FLANN-PSO), FLANN-teaching learning based optimization (FLANN-TLBO) and
FLANN-black widow optimization (FLANN-BWO) algorithm. The proposed LSTM model is a feed forward neural
network trained by a simple iterative method called the ADAM algorithm. The obtained results indicate that the proposed
network has better performance than the other competitive networks in terms of the MSE, CPU time and convergence rate.
To validate the dominance of the proposed network, a statistical tests, i.e. the Friedman test, is also applied.

Design of Lagrangian-Based FOPID Controller for Desired Closed Loop System

In this paper, Lagrangian-based method has been proposed for tuning the parameters of fractional order PI?D?PI?D? controller. In this method, the five parameters (KpKp, KiKi, KdKd, ?? and ??) of fractional order PI?D?PI?D? controller (FOPID) are suitably optimized, and successfully applied to a benchmark stable second-order feedback system. To prove the performance of the proposed method, several state-of-the-art approaches were compared. The computational complexity, robustness and stability analysis has been performed to investigate the performance of all these algorithms. Moreover, the precision and flexibility analysis among all these approaches has also been carried out in this paper. The closed loop response of the second-order bench mark stable plant in Simulink has also been depicted in this paper.

Real-time Identification of Fuzzy-PID controlled Maglev System using TLBO based Functional Link Artificial Neural Network

In this paper, the teaching–learning-based optimization-based functional link artificial neural network (FLANN) has been proposed for the real-time identification of Maglev system. This proposed approach has been compared with some of the other state-of-the-art approaches, such as multilayer perceptron–backpropagation, FLANN least mean square, FLANN particle swarm optimization and FLANN black widow optimization. Further, the real-time Maglev system and the identified model are controlled by the Fuzzy PID controller in a closed loop system with proper choice of the controller parameters. The efficacy of the identified model is investigated by comparing the response of both the real-time and identified Fuzzy PID-controlled Maglev system. To validate the dominance of the proposed model, three nonparametric statistical tests, i.e., the sign test, Wilcoxon signed-rank test and Friedman test, are also performed.

Design of Health Monitoring System for Li-ion Battery

The battery in any system or device is the main component because it powers the entire system. Hence,
we need to monitor the voltage level of the battery. We all know that an improper system of charging and discharging
may lead to battery damage or system failure. Most of the electrical/electronic devices have a Battery Management
System (BMS). Actually, BMS monitors all the properties of the battery like the voltage, current, temperature & auto
cut-off system. To ensure the proper safety and handling of Lithium-Ion or Lithium Polymer batteries. Most of the
Lithium-Ion batteries come with a nominal voltage of 3.7V. The maximum voltage is 4.2±0.5 when the battery is fully
charged. You see the manufacturer datasheet, for your battery cut-off voltage because it varies depending on the type
of battery that you are using. The battery that I am using in this project has a discharge cut-off voltage of 2.8V which is
common. You can get batteries cut-off voltage of a lithium-ion battery between 2.5V-3V.

Identification of Real-time Maglev System using Cat Swarm Optimization based FLANN

In the recent past identification of nonlinear plant is a significant work has done by many researcher and
it is found to be an emerging area for further research due to its wide application. In this article, the
characteristics and behavior of a real time maglev plant has been identified using an efficient Artificial
Neural Network (ANN) based on functional expansion technique i.e. functional link artificial neural
network (FLANN). The weights of FLANN has been iteratively updated by a heuristic optimization
algorithm i.e. Cat Swarm Optimization (CSO). So that the error needs to minimized, which is considered
as a cost function. To demonstrate the robust identification performance of the Maglev plant Mean
square error (MSE) and CPU time is considered for analysis. The simulation results justify the proposed
model robustly identifies the characteristics and parameters of non-linear dynamic maglev plant.

Identification of Real-time Maglev System using GA based Low Complexity based ANN

In the recent past identification of nonlinear plant is a significant work has done by many researcher and
it is found to be an emerging area for further research due to its wide application. In this article, the
characteristics and behavior of a real time maglev plant has been identified using an efficient low
complexity based Artificial Neural Network (ANN) based on functional expansion technique i.e.
functional link artificial neural network (FLANN). The weights of FLANN has been iteratively updated
by a heuristic optimization algorithm i.e. a natural genetics. So that the error needs to minimized, which
is considered as a cost function. To demonstrate the robust identification performance of the Maglev
plant Mean square error (MSE) and CPU time is considered for analysis. The simulation results justify the
proposed model robustly identifies the characteristics and parameters of non-linear dynamic maglev
plant.

JAYA based Functional Link Artificial Neural Network for Identification of Real-time Maglev Plant

In the recent past identification of nonlinear plant is a significant work has done by many researcher and
it is found to be an emerging area for further research due to its wide application. In this article, the
characteristics and behavior of a real time maglev plant has been identified using an efficient Artificial
Neural Network (ANN) based on functional expansion technique i.e. functional link artificial neural
network (FLANN). The weights of FLANN has been iteratively updated by a heuristic optimization
algorithm i.e. JAYA. So that the error needs to minimized, which is considered as a cost function. To
demonstrate the robust identification performance of the Maglev plant Mean square error (MSE) and
CPU time is considered for analysis. The simulation results justify the proposed model robustly identifies
the characteristics and parameters of non-linear dynamic maglev plant.

Fundamentals of Electrical and Electronics Engg.