Controller For System Research Articles

Development of Progressive Fuzzy Logic and ANFIS Control for Energy Management of Plug-In Hybrid Electric Vehicle

Hybrid electric vehicles are an effective alternative to the conventional fuel engine vehicles and thus efficient and intelligent energy management is the key for establishing a significant market for the hybrid electric vehicles globally. Recent developments in the field of intelligent techniques and demand to make the energy systems intelligent have become a means to develop energy efficient hybrid electric vehicles. The energy management issue becomes vital in order to enhance the autonomy of hybrid electric vehicles and to reduce the costs. Therefore, a novel approach with intelligent techniques, to control the plug-in hybrid electric vehicles, in front of different customer profiles has been presented. This paper presents the battery performance improvement of a plug-in hybrid electric vehicle using fuzzy logic controller and neural fuzzy logic controller with battery state of charge as a deciding parameter and consequently comparing the performance of both cases. The battery state of charge and engine speed as input has been selected and based on their values the advanced controller decides the accurate torque required to be converted to energy which could be used to charge the battery and this can be achieved by controlling the forward gain value. For this, an advanced fuzzy controller and advanced adaptive nuero fuzzy inference system controller are used to decide the value of forward gain. Simulink environment is used to simulate the performance of the proposed system. This could be helpful in deciding which type of intelligent system is to be used for the power efficient operation of the hybrid electric vehicle. The results of both the control techniques are compared and the better controller is recommended for energy management of a plug-in electric vehicles. The results indicate that advanced control techniques provide the good performance and improving the fuel budget of hybrid electric vehicles.

Collision Avoidance Modelling in Airline Traffic Based on the Change of Airplane Movements and Dynamic Clouds

An Air Traffic Controller (ATC) system aims to manage airline traffic to prevent collision of the airplane, called the Collision Avoidance (CA). The study on CA, called Conflict Detection and Resolution (CDR), becomes more critical as the airline traffic has grown each year significantly. Previous studies used optimization algorithms for CDR and did not involve the presence of cumulonimbus clouds. Many such clouds can be found in tropical regions like in Indonesia. Therefore, involving such clouds in the CDR optimization algorithms will be significant in Indonesia. We developed a CDR-based CA modelling that involves the Cumulonimbus (CB) clouds by considering three airplane maneuvers, i.e., Velocity, angle Turn and Altitude level Change (VTAC). Our optimization algorithm is developed based on a Mixed-Integer Programming (MIP) solver due to its efficiency. This proposed algorithm requires two input data, namely the initial airplane and cloud states input and the flight parameter such as velocity, angle and altitude levels. The outputs of our VTAC optimization algorithm are the optimum speed, altitude and angle turn of an airplane that is determined based on the currently calculated variables. Extensive experiments have been conducted to validate the proposed approach and the experiment results show that collisions between airplanes and clouds can be avoided with minimum change of the initial airplane velocity, angle and altitude levels. The VTAC algorithm produced longer distance to avoid collision between airplanes by at least 1 Nautical Mile (NM) compared to the VAC algorithm. The addition of angle in the VTAC algorithm has improved the result significantly.

On Second-Order Cone Positive Systems

Internal positivity offers a computationally cheap certificate for external (input-output) positivity of a linear time-invariant system. However, the drawback with this certificate lies in its realization dependency. Firstly, computing such a realization requires to find a polyhedral cone with a potentially high number of extremal generators that lifts the dimension of the state-space representation, significantly. Secondly, not all externally positive systems posses an internally positive realization. Thirdly, in many typical applications such as controller design, system identification and model order reduction, internal positivity is not preserved. To overcome these drawbacks, we present a tractable sufficient certificate of external positivity based on second-order cones. This certificate does not require any special state-space realization: if it succeeds with a possibly non-minimal realization, then it will do so with any minimal realization. While there exist systems where this certificate is also necessary, we also demonstrate how to construct systems, where both second-order and polyhedral cones as well as other certificates fail. Nonetheless, in contrast to other realization independent certificates, the present one appears to be favourable in terms of applicability and conservatism. Three applications are representatively discussed to underline its potential. We show how the certificate can be used to find externally positive approximations of nearly externally positive systems and demonstrated that this may help to reduce system identification errors. The same algorithm is used then to design state-feedback controllers that provide closed-loop external positivity, a common approach to avoid over- and undershooting of the step response. Lastly, we present modifications to generalized balanced truncation such that external positivity is preserved where our certificate applies.

Optimal Sensor Placement for Observer Design

This paper is motivated by sensor location problems for infinite dimensional linear distributed parameter systems (DPS) defined by partial differential equations (PDEs). In this setting the term “sensor location” usually refers to a spatial location. However, the problem is also meaningful in finite dimensional models and results in a parameterization of the system output matrix. The problem of “optimal” sensor (and actuator) location has a long history and plays an important role in controller design, system identification and state estimation and, depending on the application, can be formulated in many ways. Any optimal location problem implies that there is a cost function to be minimized and the choice of this cost function places specific requirements on the computational methods needed to solve the problem. In addition, as shown in the papers Yang and Morris [2014, 2015] choices such as “optimizing observability” can lead to ill-posed problems and result in non convergence of suboptimal placements. On the other hand, minimum error variance methods (Kalman filtering) can be shown be well-posed. Efficient computational methods for solving the corresponding Riccati partial differential equation have been developed and are readily available. In the classic paper by Luenberger [1964], he introduced the term “observer” for deterministic state estimation to distinguish it from the Kalman filter. In this paper we focus on state estimators based on the Luenberger observer and suggest some natural cost functions for sensor location. The framework is fairly general and applies to both finite and infinite dimensional systems. Examples are given to illustrate the ideas.

Maximum Power Point Tracking for Stand-Alone Solar Photovoltaic System Connected to Battery Energy Storage Using Intelligent Control

This paper introduced modeling and simulation results of an isolated solar photovoltaic system with lead-acid battery. The system is operated at maximum power point tracking (MPPT) using MATLAB/Simulink environment. The proposed controllers used in this paper are proportional – integral (PI) controller, fuzzy logic controller (FLC) and Adaptive Neuro Fuzzy Inference System (ANFIS) controllers. The proposed controllers are used for controlling dc-dc converter. All simulation results are recorded and compared with each other using the conventional and intelligent controllers.

Cryo-CMOS Electronics For Quantum Computing: Bringing Classical Electronics Closer To Qubits In Space And Temperature

The core of a quantum computer is a quantum processor, which is composed of quantum bits (qubits). Qubits can be implemented in many flavors, but they are fragile, and their state has a tendency to decay in time. To be usable for computation, qubits must be corrected in real time by a classical controller. The controller must also see that desired computations are executed. Today, the control of qubits is performed at room temperature (RT) by racks of instruments, while the qubits operate at several tens of milli-Kelvin. To ensure compactness and, eventually, scalability, we have proposed and implemented CMOS circuits designed to operate at a few degrees Kelvin. This makes complex thermalization circuits unnecessary, potentially enabling superconductive interconnects, which would enable virtually zero resistance and low thermal conductivity. Cryogenic CMOS, or cryo-CMOS, technology was chosen to achieve classical control due to its scalable nature and overall miniaturization opportunities. Cryo-CMOS circuits and systems need to be carefully designed to ensure strict power budgets, while achieving quite advanced specifications in terms of noise and bandwidth. We review the requirements of a classical controller and cryo-CMOS circuits and systems developed for spin qubits in our lab. Results and perspectives are presented with a discussion about a road map for the future.

Studying Impacts of Electric Vehicle Functionalities in Wind Energy-Powered Utility Grids With Energy Storage Device

Recently, electric vehicles (EVs) have proven remarkable impacts on reducing pollution and their suitability for utilizing renewable energy sources (RESs) instead of fossil fuels. The continuously increasing number of installed EVs has encouraged their employment for performing additional functionalities in utility grids. This paper provides study for the impacts of EV integration approaches on enhancing the operation of utility grids. The proposed study considers the targeted renewable energy powered utility grids with installed energy storage devices. Wind energy has been utilized as a case study for the RES with considering its stochastic characteristics and its continuous fluctuations with wind speed. Additionally, the superconducting magnetic energy storage system (SMES) device is chosen due to its promising long-life operation. The various aspects of point of common coupling (PCC) voltage fluctuations, reactive power support, leveling active power of the on-peak period, and power losses are considered in the proposed study. Moreover, fuzzy logic controller (FLC) systems have been proposed for controlling EVs and the SMES device. The energy management between the installed EVs, SMES, wind, and utility grid side is achieved through the coordination of the FLC systems. The results show that reduced peak and mean values of total power losses and line active power are obtained by the proposed coordination method. Moreover, improved profiles of the PCC voltage, reactive power support, active power leveling, and sustainable and reliable power supply are obtained by the proposed method for wide operating ranges and modes of operation.

WITHDRAWN: Monitoring and control of various process using distributed control system

Distributed Control System (DCS) is dined as a system of controllers connected by a data network which appears like a single system. The functions, the locations, the configuration and the specifications may be different but they all communicate using a single language and hence they are knitted together. DCS is preferred in applications where large number of input output modules are involved in the communication, such as chemical plant or oil refineries. They are multitasking systems able to handle large common databases and allows for various control loops, using graphical representation of function blocks. Monitoring and control of process from a remote station is the need of the hour. DCS is one such provision implemented in most of the process control industries. This proposal is based on controlling the existing process stations in the lab (level and flow process) by using DCS and implementing the concept of Supervisory Control and Data Acquisition (SCADA) in the stations. A user-friendly Front Panel is designed through which the process control panel can be monitored and controlled for the respective stations. Functional blocks are used for programming the Proportional Integral Derivative (PID) Controller for the level and flow process.

Moving object recognition system on a pedestrian crossing

The article discusses the development of a system for recognizing people at a pedestrian crossing. The recognition system includes a trained classifier and two sets of images taken from an open database containing images of city streets from outdoor cameras. Input information prepared using PictureCropper application. The classifier was tested based on the prepared sets of images. The result is shown in the figure, and after testing, the metrics were calculated, necessary to assess the effectiveness of the resulting classifier for each set. System is being developed for specialists, which will allow individual and centralized adjustment of the adjustment of pedestrian phases for each road situation, both for the local system of controllers, and in accordance with the existing algorithms of coordination systems with the expansion of such.

Density Based Traffic Control System Using Arduino

Traffic congestion is a severe problem in most of the cities across the world and it has become a nightmare for the citizens. It is caused by delay in signal, inappropriate timing of traffic signaling etc. The delay of traffic light is hard coded and it does not depend on traffic. Therefore, for optimizing traffic control, there is an increasing demand in systematic quick automatic system. This paper is designed to develop a Density based traffic controller system using ARDUINO. The signal timing changes automatically on sensing the traffic density at the junction. The microcontroller used in this project is ARDUINO. The system contains IR sensors (transmitter and receiver) which will be mounted on the either side of the road on poles. It gets activated and receives the signal as the vehicles passes close by it. In this system, we will use IR sensors to measure the traffic density. We have to arrange one IR sensor for each road; these sensors always sense the traffic on that particular road. All these sensors are interfaced to the microcontroller. Based on these sensors, controller detects the traffic and controls the traffic system.

DENSITY BASED TRAFFIC CONTROLLER SYSTEM USING WIRELESS SENSOR NETWORKS

This traffic controller system aims at designing a dynamic automated traffic control system where the signal and time limit will be automatically changed when it detects traffic congestion in any lane. Traffic congestion is a major problem in many cities around the world so it is urgent need to switch over from manual mode or timer mode to an automated traffic control system that has the power to make decisions on its own. The current traffic signal system is working on fixed time which may not work if one route gets more traffic than other lane. To overcome this problem, a smart traffic control system is proposed. When using this system high traffic congestion on a particular lane receives green for a longer period of time compared to the other lanes which has normal vehicle flow. Therefore, the proposed method of providing green and red light time is based on the amount of traffic congestion at the time with the help of an IR sensor and Sound sensors connected to ATmega 2560 Microcontroller.

基于STC单片机的小型直流电机变频调速控制器系统设计

基于STC单片机的小型直流电机变频调速控制器系统设计

Establishing a Remote Controller System to Elucidate the olfactory navigation of Insects

Establishing a Remote Controller System to Elucidate the olfactory navigation of Insects

Exhaust Fan Speed Controller Using Fuzzy Logic Controller

Fuzzy logic is a reasoning method for solving linguistic problems that cannot be solved mathematically. The application of fuzzy logic from our development program regarding the development of exhaust fans with fuzzy logic controllers to regulate air flow velocity in accordance with air conditions in a room. Using a gas sensor and a temperature sensor will create a number of conditions to then be treated in a fuzzy logic system using the Mamdani method. The process that occurs in the Mamdani fuzzy includes four stages including the process of calculating the fuzzy set (fuzzification), the application of the implication function, the composition of the rules to the last stage, the defuzzification process. The inter elements of the mamdani fuzzy process are interconnected in the sense that the order determined by fuzzy mamdani cannot be removed or replaced by another. Development that has been done related to the exhaust fan uses two sensors namely the temperature sensor and gas sensor. Used as a detector for the amount of harmful gas in a room so that it activates the exhaust fan to eliminate it at a speed consistent with the gas content in the room. Also used to measure the temperature of the room and then activate the exhaust fan so that the room can be inhabited comfortably. Fuzzy logic controller system is used to adjust the rotational speed of the exhaust fan.

Study on Tool Deterioration in Machining with Minimum Quantity Lubrication (MQL) Condition

This study aims to observe the tool deterioration following application of minimum quantity lubrication method (MQL). The designed MQL system is completed with an Arduino controller system which was able to be either manually-controlled or automatically-controlled. The tool used in this study is DCMT 11 insert type. The Taguchi method using the Orthogonal Array L9 design was used to compile the design of experiments with variations in depth of cut, coolant composition, and cooling fluid application methods. Each variable consists of three levels. Tool deterioration evaluated by observing it under an optic microscope from three sides of the tool. There are four levels of tool deterioration. The value of tool deterioration of each tool is the accumulation of each side view. ANOVA analysis found that depth of cut, cutting tool composition and method of applying coolant influence tool deterioration in percentage by 32.69%, 17.30 % and 12.82% respectively. Moreover, the minimum tool deterioration would be achieved when using the parameter combination of depth of cut of 1.6 mm; mixture composition of 3:7; and using the temperature-controlled MQL.

Machine learning and price-based load scheduling for an optimal IoT control in the smart and frugal home

We pose and study a scheduling problem for an electric load to develop an Internet of Things (IoT) control system for power appliances, which takes advantage of real-time dynamic energy pricing. Using historical pricing data from a large U.S. power supplier, we study and compare several dynamic scheduling policies, which can be implemented in a smart home to activate a major appliance (dishwasher, washing machine, clothes dryer) at an optimal time of the day, to minimize electricity costs. We formulate our scheduling task as a supervised machine learning classification problem which activates the load during one of two preferred time bins. The features used in the machine learning problem are hourly market, spot and day-ahead prices along with delayed label of the prior day. We find that boosting tree-based algorithms outperform any other classification approach with measurable reduction of energy costs over certain types of naive and static policies. We observe that the delayed label has most predictive power across features, followed, on average, by spot, hourly market, and day-ahead energy prices. We further discuss implementation issues using a micro controller system coupled with cloud-based serverless computing and dynamic data storage. Our test system includes an interactive voice interface via an intelligent personal assistant.

Simulating the Speed control System of Wind Turbines Using MATLAB Software

In this paper, a mathematical method is proposed to control the output frequency of a self-excited induction generator using wind turbines and static loads. A dynamic model of the wind turbine is implemented to model the Connections and fittings of the wind turbine to convert the wing energy to electrical energy. Also a PID controller system is proposed to control the rotor speed of the wind turbine. The proposed mathematical model is developed in MATLAB-Simulink software. The simulation results showed that the developed controller can be used to control the wind turbine velocity.

Hydrodynamics, heat transfer and kinetics reaction of CFD modeling of a batch stirred reactor under hydrothermal carbonization conditions

Computational Fluid Dynamics simulation was used to study hydrothermal carbonization of avocado stone (AS) in a batch stirred reactor, using an open-loop controller system. The corresponding simulations were carried out in COMSOL Multiphysics 5.2. The different biomass-to-water ratio was investigated. The hydrodynamic study shows that the ideal stirring speed to obtain a homogeneous mixture inside the reactor is 550 rpm, due to the high density of AS particles (1547.64 ± 27.33 kg/m3). However, a stagnant zone was observed just below the impeller. To validate the CFD simulations temperature profiles with experimental data, the heat transfer coefficient of the insulator was determined (11.66 W/m2.K), this value was used to set the heat loss in the CFD simulation. According to the model, the difference between the thermal properties of biomass and water under hydrothermal carbonization conditions is negligible. However, experimentally, an increase in temperature was observed with increasing biomass to-water-ratio; this is due to the global hydrothermal carbonization of AS is exothermic reaction. The heat released during 8 h, including heat-up time, was −7.25 ± 0.32 MJ/kg of feedstock. Finally, a kinetic model was proposed taking into account the influence of temperature, heat-up time, reactor volume, and biomass concentration.

A Study of Deep Neural Network Controller-Based Power Quality Improvement of Hybrid PV/Wind Systems by Using Smart Inverter

Presently, climate change and global warming are the most uncontrolled global challenges due to the extensive fossil fuel usage for power generation and transportation. Nowadays, most of the developed countries are concentrating on developing alternative resources; consequently, they did huge investments in research and development. In general, alternative energy resources including hydropower, solar power, and wind energy are not harmful to nature. Today, solar power and wind power are very popular alternative energy sources due to their enormous availability in nature. In this paper, the photovoltaic cell and wind energy systems are investigated under various weather conditions. Based on the findings, we developed an advanced intelligent controller system that tracks the maximum power point. The MPPT controller is a must for the renewable energy sources due to unpredictable weather conditions. The main objective of this paper is to propose a new algorithm that is based on deep neural network (DNN) and maximum power point tracking (MPPT), which was simulated in a MATLAB environment for photovoltaic (PV) and wind-based power generation systems. The development of an advanced DNN controller that improves the power quality and reduces THD value for the microgrid integration of hybrid PV/wind energy system was performed. The MATLAB simulation tool has been used to develop the proposed system and tested its performance in different operating situations. Finally, we analyzed the simulation results applying the IEEE 1547 standard.

Atmega Microcontroller 2560 Based Safety System of Monitor Panel and Controller on a Small Excavator

This study aimed to design and build an Atmega Microcontroller 2560 based safety system on a monitor panel and a controller on a small excavator. Atmega 2560 Microcontroller-based alarm technology is the right choice as a solution to prevent theft of monitor panels and controllers on small excavator units, using Passive Infra-Red (PIR) sensor number 1 in the cabin room, PIR sensor number 2 on the battery, and door switch that is between the cabin and the door, if there is theft in the cabin room, then the alarm will be active, if a theft occurs in the battery then the alarm will be active. The alarm indicator system uses a buzzer (horn) unit, and there is additional lighting in the cabin room with a 24V Light Emitting Diode (LED) lamp if the alarm is active. Using an alarm, the risk of losing the monitor panel and controller due to theft on the unit can be prevented. The study results showed that the tool could work appropriately according to the standard testing tool, PIR sensors number 1 and 2 could detect movement or radiation so that the alarm would be active for 15 seconds. The door switch was tested by looking at the alarm condition, if the cabin door were closed when it was in the locked position, the alarm would not be active, but if the door was locked and forcibly opened, then the alarm would be active continuously without stopping until the door was closed again.