Evolution of Controllers for the Speed Control in Thyristor Fed Induction Motor Drive

Induction Motors (IMs) are now becoming the pillar of almost all the motoring applications related to the industry and household. The practical applications of IMs usually require constant motoring speed. As a result, different types of control systems for IM's speed controlling have been shaped. One of the important techniques is the utilization of thyristor fed drive. Although, the thyristor fed induction motor drive (TFIMD) offers stable speed performance, the practical speed control demand is much more precise. Hence, this drive system utilizes additional controllers to attain precise speed for practical applications. This paper offers a detailed review of the controllers utilized with the thyristor fed IM drive in the past few decades to achieve good speed control performance. The clear intent of the paper is to provide a comprehensible frame of the pros and cons of the existing controllers developed for the TFIMD speed control requirements. Keywords: Thyristor Fed Drives, Induction Motors, Speed Controller, Conventional Controllers, and Soft Computing Techniques

Role of optimization algorithms based fuzzy controller in achieving induction motor performance enhancement.

Three-phase induction motors (TIMs) are widely used for machines in industrial operations. As an accurate and robust controller, fuzzy logic controller (FLC) is crucial in designing TIMs control systems. The performance of FLC highly depends on the membership function (MF) variables, which are evaluated by heuristic approaches, leading to a high processing time. To address these issues, optimisation algorithms for TIMs have received increasing interest among researchers and industrialists. Here, we present an advanced and efficient quantum-inspired lightning search algorithm (QLSA) to avoid exhaustive conventional heuristic procedures when obtaining MFs. The accuracy of the QLSA based FLC (QLSAF) speed control is superior to other controllers in terms of transient response, damping capability and minimisation of statistical errors under diverse speeds and loads. The performance of the proposed QLSAF speed controller is validated through experiments. Test results under different conditions show consistent speed responses and stator currents with the simulation results

An application of modified adaptive bats sonar algorithm (MABSA) on fuzzy logic controller for dc motor accuracy

Controllers are mostly used to improve the control system performance. The works related to controllers attract researchers since the controller can be applied to solve many industrial problems involving speed and position. Fuzzy logic controller (FLC) gains popularity since it is widely used in industrial application. However, the FLC structure is still lacking in terms of the accuracy and time response. Although there are optimization technique used to obtain both accuracy and time response, it is still lacking. Therefore, this research presents works on the FLC system which is the fuzzy inference system that will be optimized by the modified adaptive bats sonar algorithm (MABSA) for the DC servo motor position control. The MABSA will be optimized with the range of the membership input in the FLC. The research aims are to achieve accuracy while minimizing the time response of the DC servo motor. This is done by designing the FLC using the Matlab toolbox. After the FLC is designed completely, the Simulink block diagram for the DC servo motor and FLC are built to see the performance of the controller. The range of the membership function for inputs and outputs will be optimized by the MABSA to get the best positional values. The performance of the developed FLC with the optimized MABSA is verified through the simulation and robustness tests with the system that did not use the FLC and also the system without MABSA. It was demonstrated from the study that the proposed FLC with optimization of MABSA algorithm was able to yield an improvement of 3.8% with respect to the rise time in comparison to other control schemes evaluated. When compared with PSO algorithm, proposed FLC optimized by MABSA showed improvement by 12.5% in rise time and 10% in settling time. PSO-FLC also give 0.6% steady state error compared to the MABSA-FLC. In conclusion, the results validate the better performance in terms of rise time and settling time of the developed FLC that has been optimized by the MABSA

Controllability and Observability Analysis of DC Motor System and a Design of FLC-Based Speed Control Algorithm

DC motor is an electrical motor widely used for industrial applications, mostly to support production processes. It is known for its flexibility and operational-friendly characteristics. However, the speed of the DC motor needs to be controlled to have desired speed performance or transient response, especially when it is loaded. This paper aims to design a DC motor model and its speed controller. First, the state space representation of a DC motor was modeled. Then, the controllability and observability were analyzed. The transfer function was made based on the model after the model was ensured to be fully controllable and observable. Therefore, a fuzzy logic controller is employed as its speed controller. Fuzzy logic controller provides the best system performance among other algorithms; the overshoot was successfully eliminated, rise time was improved, and the steady-state error was minimized. The proposed control algorithm showed that the speed controller of the DC motor, which was designed based on the fuzzy logic controller, could quickly control the speed of the DC motor. The detail of resulted system performance was 2.427 seconds of rising time, 11 seconds of settling time, and only required 12 seconds to reach the steady state. These results were proved faster and better than the system performance of PI and PID controllers

Cooperative beamsteering in wireless sensor network based on backtracking search algorithm

The progressive development of Wireless Sensor Network (WSNs) contributes to many applications such as in the intelligent transport system (ITS), safety monitoring, military and in natural disasters prevention. In parallel to WSNs, the idea of internet of things (IoT) is developed where IoT can be defined as an interconnection between identifiable devices within the internet connection in sensing and monitoring processes. With recent growth in both size and power efficient computing, the concept of the ubiquitous WSN has aggressively emerged as an acknowledged research topic. As the capabilities of individual nodes in WSNs increase, so does the opportunity to perform more complicated tasks, such as cooperative beamsteering (CB). This CB manages to improve the range of communications and save precious battery power during the transmission. Therefore, this research proposes a meta-heuristic algorithm to organize node location in array arrangement. It is expected to effectively improve radiation beampattern fluctuations, exhibits lower complexity and less energy. From the simulation that has been done, it's observed that the proposed algorithm helps to reduce the side lobe level, thus better radiation beampattern is achieved

Design of Multivariate PID Controller for Power Networks Using GEA and PSO

The issue of proper modeling and control for industrial systems is one of the challenging issues in the industry. In addition, in recent years, PID controller design for linear systems has been widely considered. The topic discussed in some of the articles is mostly speed control in the field of electric machines, where various algorithms have been used to optimize the considered controller, and always one of the most important challenges in this field is designing a controller with a high degree of freedom. In these researches, the focus is more on searching for an algorithm with more optimal results than others in order to estimate the parameters in a more appropriate way. There are many techniques for designing a PID controller. Among these methods, meta-innovative methods have been widely studied. In addition, the effectiveness of these methods in controlling systems has been proven. In this paper, a new method for grid control is discussed. In this method, the PID controller is used to control the power systems, which can be controlled more effectively, so that this controller has four parameters, and to determine these parameters, the optimization method and evolutionary algorithms of genetics (EGA) and PSO are used.  One of the most important advantages of these algorithms is their high speed and accuracy. In this article, these algorithms have been tested on a single-machine system, so that the single-machine system model is presented first, then the PID controller components will be examined. In the following, according to the transformation function matrix and the relative gain matrix, suitable inputs for each of the outputs are determined. At the end, an algorithm for designing PID controller for multivariable MIMO systems is presented. To show the effectiveness of the proposed controller, a simulation was performed in the MATLAB environment and the results of the simulations show the effectiveness of the proposed controller

Implementation and tuning of an extended expert control system for helicopter autorotation and development of a nonlinear model of electric drives to be used in the optimization of torque performance

This thesis covers two separate investigations under the topic of control. The first is the design and tuning of a fuzzy logic controller for Human-in-the-Loop (HITL) helicopter autorotation. The second is the exploration of an optimized pulse pattern for the control of an electric drive with focus on the development of the mathematical model of the drive. Part One of this thesis discusses the autorotation controller. Helicopter autorotation is the operation a pilot performs when power is no longer supplied to the main rotor and an emergency landing is required. A controller was developed that allowed an autonomously controlled helicopter to perform an autorotation, an ‘expert skill’ more easily learned by human pilots. This controller is used in this thesis to create a tool that brings the computer and human together. The tuning process of the autorotation controller is described in detail. The controller used has five stages of operation; the transitions between these stages occur through a fuzzy logic determination. The results of the tuning bring about a successful autorotation in a simulated environment. The specific model of the controller developed in this thesis can be used in a different system to supply commands to a human pilot, aiding in the decisions during an autorotation. Part Two of this thesis covers the development of the mathematical model of an electric drive and an optimization scheme to find a ‘better’ switching sequence for control. The goal of the model is to use it to find a better switching sequence, where better means fewer switching events as well as hitting targets of other key performance indicators (KPIs). The idea explored in this thesis is controlling the drive based on direct manipulation of the switches instead of indirectly through voltage or current. The mathematical model focusing on the switches is important to develop to facilitate the exploration of this control. Two different methods for developing this model are described. The first is a manually switched model based on examining every possible state of the drive. The second method is a non-smooth differential algebraic equation (DAE) approach, a more sophisticated mathematical approach that describes every state of the drive in one set of equations. An optimization scheme using model predictive control (MPC) is described. The focus of the optimization is the torque output of the motor and the number of switching events. The optimization would use the model developed in the thesis.M.S

The 1st International Conference on Computational Engineering and Intelligent Systems

Computational engineering, artificial intelligence and smart systems constitute a hot multidisciplinary topic contrasting computer science, engineering and applied mathematics that created a variety of fascinating intelligent systems. Computational engineering encloses fundamental engineering and science blended with the advanced knowledge of mathematics, algorithms and computer languages. It is concerned with the modeling and simulation of complex systems and data processing methods. Computing and artificial intelligence lead to smart systems that are advanced machines designed to fulfill certain specifications. This proceedings book is a collection of papers presented at the first International Conference on Computational Engineering and Intelligent Systems (ICCEIS2021), held online in the period December 10-12, 2021. The collection offers a wide scope of engineering topics, including smart grids, intelligent control, artificial intelligence, optimization, microelectronics and telecommunication systems. The contributions included in this book are of high quality, present details concerning the topics in a succinct way, and can be used as excellent reference and support for readers regarding the field of computational engineering, artificial intelligence and smart system