Selected Control Algorithm Research Articles

Optimizing Energy Efficiency in a Peltier-Module-Based Cooling Microunit through Selected Control Algorithms

Optimizing Energy Efficiency in a Peltier-Module-Based Cooling Microunit through Selected Control Algorithms

An AoTI-Driven Joint Sampling Frequency and Access Selection Optimization for Industrial Wireless Sensor Networks

The freshness of system information has attracted extensive attention and has become one of the most critical Quality of Service (QoS) indicators in industrial wireless sensor networks (IWSNs). For practical IWSNs composed of numerous components, the system freshness for a specific task is usually related to multiple and multitype sensing data. For example, in a chemical plant fire alarm system, the system controller (SC) cannot determine the fire notification until analyzing multiple types of ambient information, such as temperature, flame, carbon dioxide, and smoke. However, most existing research on freshness metrics, such as Age of Information (AoI) or Age of Processing (AoP), only considers a single-package setting with a single type of data. Therefore, to measure the system freshness accurately in IWSNs, we propose the Age of Task-oriented Information (AoTI) for industrial tasks. It measures the time elapsed of the latest analyzed results before arriving at the receiver since the generation of any type of sampling data belonging to an industrial task. Considering the time-varying and wireless environments, we aim to minimize the long-term AoTI for IWSNs applications by jointly optimizing access selections and sampling frequencies for all sensors. We first formulate this problem as a Mixed Integer Nonlinear Programming (MINLP) problem and then transform it into a Constrained Markov Decision Process (CMDP), which is further relaxed as an MDP using the Lagrangian method. Finally, we develop a Learning-based Access selection and Sampling frequency Control (LASC) algorithm and verify its superiority through extensive simulations.

Epsilon-Greedy-Based MQTT QoS Mode Selection and Power Control Algorithm for Power Distribution IoT

Employing message queuing telemetry transport (MQTT) in the power distribution internet of things (PD-IoT) can meet the demands of reliable data transmission while significantly reducing energy consumption through the dynamic and flexible selection of three different quality of service (QoS) modes and power control. However, there are still some challenges, including incomplete information, coupling of optimization variables, and dynamic tradeoff between packet-loss ratio and energy consumption. In this paper, the authors propose a joint optimization algorithm named EMMA for MQTT QoS mode selection and power control based on the epsilon-greedy algorithm. Firstly, the joint optimization problem of MQTT QoS mode selection and power control is modeled as a multi-armed bandit (MAB) problem. Secondly, the authors leverage the online learning capability of the epsilon-greedy algorithm to achieve joint optimization of MQTT QoS mode selection and power control. Finally, they verify the superior performance of the proposed algorithm through simulations.

Development of Discrete-Time Waterjet Control Systems Used in Surface Vehicle Thrust Vectoring

To create an autonomous surface vehicle, the microcontroller that will be responsible for the vessel’ s response will have access to both engine thrust controls and steering controls. In the case of a vehicle with waterjet propulsion, the term “controls” refers to engine RPM and nozzle angle. The latter allows the thrust vectoring. Having performed the mathematical modelling of the surface vessel, a method must be found such that the microcontroller actions on the controls create the desired outputs, based on predefined performance objectives. A commonly selected control algorithm considered an industry standard for control applications—at least from the perspective of classical control methods—is the PID control scheme. Many times, a PID control scheme is sufficient for achieving the required performance. There are also many methods that extend the functionality of the PID to robust control schemes, to accomplish more specific targets, such as noise rejection. The vast majority of all these implementations, will have to be implemented using a digital microcontroller or an FPGA. This paper assists theoretically in implementing PID controllers digitally, by elaborating on the discrete-time perspective of PID control. It also provides a test method for evaluating the algorithmic implementation.

Maximum power point tracking algorithms for wind power generation system: Review, comparison and analysis

AbstractWind energy is one of the most important clean energies and the variable speed constant frequency technology is widely used in wind energy conversion systems. Maximum power point tracking (MPPT) is essential for a variable speed constant frequency wind power generation system. Concerning the current research on the MPPT algorithm, this paper studies the principle, characteristics, and reported improvement strategies of principal algorithms. Through the comparison of simulation results for selected control algorithms, the improved optimal torque control algorithm has been found to be the best MPPT algorithm for wind power generation systems because of its simplicity and efficiency. On this basis, further corresponding simulation runs are carried out to analyze the effect of the wind speed fluctuation characteristics on the systematic dynamic performance and power generation efficiency. The results show that the average wind speed, wind fluctuation frequency, and wind fluctuation amplitude can affect the performance of system operation and the efficiency of wind energy capture in different degrees, which has a great practical significance for the research of MPPT control strategy.

Simulation research on phase selection control algorithm of a four-phase interleaved parallel Boost converter

In order to increase the rated power of the DC/DC converter, the interleaved parallel connection is widely used. Although the interleaved and parallel Boost converters can increase the operating power of the system, at the same time, it has the advantages of smaller system output current ripple and can reduce the stress of switching devices, but when the system is running in low power mode, due to the use of multi-phase Boost, the increase in loss makes the conversion efficiency of the system lower. A phase selection control algorithm is proposed in this paper, by which the conversion efficiency of multi-phase interleaved parallel Boost converters can be effectively improved. Finally, the feasibility of the phase selection control algorithm designed is verified through simulation in this paper.

THE INFLUENCE OF THE INPUT INDUCTANCE ON THE QUALITATIVE INDICATORS OF ACTIVE CONTROLLED RECTIFIER

The main part of electricity is used by industrial plants in a converted form and the use of semiconductor converters is growing rapidly. The requirements for the quality of semiconductor converters are increased. And their impact on the power supply, load and related consumers is increased too. One of the most popular converters in the segment of low and medium power drives is a frequency converter made on the basis of a three-phase stand-alone voltage inverter. A significant disadvantage of such converters is the use of a diode rectifier at the input of the circuit, which has disadvantages as the inability to recover electricity to the mains in the mode of dynamic braking of an induction motor and significant distortions of the mains current. These disadvantages can be eliminated by using an active rectifier instead of a diode rectifier, which provides a sinusoidal shape of the mains current in phase with the mains voltage and the possibility of bidirectional energy exchange with the load. The efficiency of the active rectifier-voltage source is determined by the selected control algorithm of the circuit switches and the correct task of the inductance of the input choke. A mathematical model of a three-phase active rectifier-voltage source, working with a fixed modulation frequency with a vector algorithm for building a control system and analysis of the influence of the input inductance on the quality of its work was created. From the simulation results it became obvious that the proposed structure of the control system provides stable operation of the converter and acceptable standards for electromagnetic compatibility with the mains at a fixed modulation frequency; the proposed algorithm for calculating the value of the input inductances allows you to correctly select their allowable value, and obtained in the simulation of the dependence allows you to most accurately determine the value of the input inductances on the allowable level of distortion of mains current and supply voltage.

Multi Selection Control Algorithm in Examination Invigilation Management System(ExIMS) for Exchanging Invigilation Duties Among Invigilators at UiTM Pulau Pinang Branch

Invigilation of the examination is one of the important roles and tasks of lecturers appointed as invigilators or proctors. Based on the past semi-annually reports of examination post-mortems meeting, less than 10% of the invigilators were unhappy with the examination duties that have been assigned or appointed to them. The examination committee members who are responsible in the process of assigning the invigilation duties to each invigilator had given a lot of effort and spent a few weeks to ensure that everybody is happy with the invigilation duties assigned to them. Nevertheless, despite the excellent planning of invigilation duties assignment, it cannot make everybody satisfied. Thus, a web-based application system named Examination Invigilation Management Systems (ExIMS) has been developed using the System Development Life Cycle (SDLC) waterfall model methodology to meet the need of invigilation duties exchanged among invigilators or applicants. This system has been developed to control and cross check the request of invigilation duties exchanged using the algorithm named multi selection control. The algorithm could avoid any conflicts or anomalies that could happen after the exchange of duty has been made. The process of duty exchange is examined and verified automatically by the system. The system provides a platform for users or invigilators to give their views or opinions on the invigilation duties and system features or performance through the online survey. The survey uses the TAM method concentrating on the perceived-ease-of-use (PEOU) component. Based on the survey, 95% respondents reported that they were satisfied with the platform for duty exchange among invigilators. The users can easily and flexibly select the new duties, which will be automatically and mutually swapped among invigilators. ExIMS has solved the problem of unsatisfaction of invigilation duties and increased the happiness index among invigilators during the examination period.

Power Control Optimization for Large-Scale Multi-Antenna Systems

Large-scale multi-antenna systems can effectively improve data transmission reliability and throughput for smart grid. However, the massive number of antennas and radio frequency (RF) chains also result in high complexity and energy cost. In this paper, we develop a new performance benchmark named energy economic efficiency for measuring the time-average throughput per energy cost. Then, we investigate how to maximize long-term energy economic efficiency via the joint optimization of communication and energy resource allocation. The formulated joint optimization problem is NP-hard because it not only involves long-term nonlinear optimization objective and constraints, but also involves both integer and continuous optimization variables. Next, we propose an online joint antenna selection and power control algorithm by combining nonlinear fractional programming, Lyapunov optimization, and bisection method. The proposed algorithm can achieve bounded performance deviation from the optimum performance without requiring the prior knowledge of future channel state information (CSI), energy arrival, and electricity price. Finally, a comprehensive theoretical analysis is provided, and the proposed algorithm is verified through simulations under various system configurations.

Multiple Harmonic Current Injection System for Audible Noise Analysis of AC Filter Capacitors in Converter Stations

The audible noise of AC filter capacitors is one of the major noise sources in High Voltage Direct Current (HVDC) converter stations. In order to avoid the sharp noise of AC filter capacitors, it is necessary to design a Multiple Harmonic Current Injection System (MHCIS) for power capacitors to analyze their audible noise characteristics. Simulating effectively the noise of AC filter capacitors under their actual working conditions in converter stations is the primary target of this proposed MHCIS. To ensure the accuracy of the harmonic currents injected into the power capacitors, each harmonic current is detected and controlled separately. On the basis of the instantaneous reactive power theory, a selective harmonic current detection algorithm is proposed in this paper for the single-phase MHCIS. The amplitude of the selected harmonic current can be tracked timely and exactly in d-q synchronous rotational coordinate system, such that a Proportional Integral (PI) controller can be directly operated to eliminate the steady-state errors. The experimental parameters are usually invariable in this system, the phase deviation of the selected harmonic current is also fixed. Thus the phase angle offset of the corresponding harmonic current can be detected and calculated to compensate the system inherent delay. Owing to the accurate detection and control of multiple harmonic currents, the actual working conditions of AC filter capacitors can be accurately simulated, and the reliable noise analysis results of power capacitors can be obtained. Finally, the proposed selective harmonic current detection and control algorithm is verified by the theoretical analysis, simulation and experimental results.

Feedforward Selective Fixed-filter Active Noise Control: Algorithm and Implementation

Conventional real-time active noise control (ANC) usually employs the adaptive filtered-x least mean square (FxLMS) algorithm to approach optimum coefficients for the control filter. However, lengthy training is usually required, and the perceived noise reduction is not immediately realized. Motivated by the practical implementation, we propose a selective fixed-filter active noise control (SFANC) algorithm, which selects a pretrained control filter to attenuate the detected primary noise rapidly. On top of improved robustness, the complexity analysis reveals that SFANC appears to be more efficient. The SFANC algorithm chooses the most suitable control filter based on the frequency-band-match approach implemented in a partitioned frequency-domain filter. Through simulations, SFANC is shown to exhibit a satisfactory response time and steady-state noise reduction performance, even for time-varying noise and real non-stationary disturbance.

Slip at Vehicle Transmission System with Hydrokinetic Converter in the Urban Test Cycle

Abstract The study concentrated on slip phenomenon occurring at hydrokinetic converter in passenger car transmission system. Optimization of modern automatic transmission systems aims, on the one hand, to further increase the comfort of use, and on the other hand to improve the efficiency and reliability operation of individual components, in such a way as to ultimately result in lowering fuel consumption and also harmful emissions. One of the important factors affecting the mentioned transmissions properties as a whole system is the slip phenomenon occurring between the pump and the hydrokinetic converter turbine. The study presents the results of research on ZF 4HP20 and Fuji Hyper M6 gearboxes operating in vehicle transmission systems. The tests were carried out using the MAHA MSR 500 chassis dynamometer during driving at chosen test cycles. The research covered the transmission systems operating according to selected control algorithms affecting not only the gear shifting strategy but also the operation of the lock up clutch causing the transmissions slip values. As shown by the conducted research, the algorithm controlling the operation of the torque converter can have a significant influence on their operating indexes. The conducted analyses indicate the possibilities of improving the transmission operating indexes, especially in urban driving cycles.

Predictive Location Aware Online Admission and Selection Control in Participatory Sensing

Participatory sensing is a crowdsourcing-based framework, where the platform executes the sensing requests with the help of many common peoples’ handheld devices (typically smartphones). In this paper, we mainly address the online sensing request admission and smartphone selection problem to maximize the profit of the platform, taking into account the queue backlog, and the location of sensing requests and smartphones. First, we formulate this problem as a discrete time model and design a location aware online admission and selection control algorithm (LAAS) based on the Lyapunov optimization technique. The LAAS algorithm only depends on the currently available information and makes all the control decisions independently and simultaneously. Next, we utilize the recent advancement of the accurate prediction of smartphones’ mobility and sensing request arrival information in the next few time slots and develop a predictive location aware admission and selection control algorithm (PLAAS). We further design a greedy predictive location aware admission and selection control algorithm (GPLAAS) to achieve the online implementation of PLAAS approximately and iteratively. Theoretical analysis shows that under any control parameter V > 0, both LAAS and PLAAS algorithm can achieve O (1/ V )-optimal average profit, while the sensing request backlog is bounded by O ( V ). Extensive numerical results based on both synthetic and real trace show that LAAS outperforms the Greedy algorithm and Random algorithm and GPLAAS improves the profit-backlog tradeoff over LAAS.

Joint channel and Power Allocation for Device-to-Device Communication on Licensed and Unlicensed Band

Device-to-Device communications (D2D) are being used to improve the spectral efficiency and to reduce the load of the base station (BS) by reutilizing the licensed spectrum. However, the primary cellular users may face high interference from D2D users. There is also a scarcity of licensed spectrum due to the dense deployment of smart devices. To alleviate this problem, in this paper, we are extending the D2D users to the unlicensed band. In this scenario, the D2D users are allowed to reuse the licensed spectrum or share the unlicensed spectrum with the incumbent WiFi users. However, cellular and WiFi users experience high interference if an efficient interference management scheme is not used. In this paper, we propose a joint mode selection, channel allocation, and power control algorithm using particle swarm optimization to manage the interference and improve the overall throughput of cellular and D2D users such that the minimum data rate requirement of WiFi users is guaranteed. Through numerical simulations, we show that the proposed algorithm can significantly mitigate the interference and improve the throughput of the system.

Energy Efficient Nano-Node Association and Resource Allocation for Hierarchical Nano-Communication Networks

Nano-communication networks operating in the Terahertz (THz) band are being developed quickly owing to the high bandwidth range. In this paper, we investigate energy efficient data transmission in hierarchical nano-communication networks with multiple inbody nano-nodes and micro-nodes. Considering the channel characteristic of the THz band, we formulate a weighted energy efficiency optimization problem for the joint design of nano-node association and resource allocation in such networks. By regarding nano-node association results as the network cluster configurations, the non-convex optimization problem is decomposed into a network clustering problem and a resource allocation problem. The network clustering problem is a combine optimization problem, whose optimal cluster configuration is derived using Markov approximation theory. Besides, the resource allocation problem, which is a mixed integer nonlinear programming problem, is solved by comprehensively using continuity relaxation theory and an improved dynamic programming method. An adaptive frequency selection and power control algorithm is given for nano-nodes to determine the optimal frequency and transmit power under a specific cluster configuration. To gain more insights into the obtained solutions, a joint network clustering and resource allocation algorithm is presented to accommodate hierarchical nano-communication networks to achieve the energy efficient data transmission. Simulation results validate the theoretical analysis of the proposed algorithms.

Energy-Efficient Vehicular Heterogeneous Networks for Green Cities

With the evolutionary development of automobile industry, modern transportation systems cause a series of critical problems, such as increased energy consumption and air pollution. To make green cities a reality, an ever expanding and evolving vehicular heterogeneous network infrastructure is required to enable fine-granularity data collection and reliable service delivery. In this paper, we investigate how to realize energy-efficient vehicular heterogeneous networks for green cities by exploring cooperative two-hop device-to-device-based vehicle-to-vehicle (D2D–V2V) transmission. We propose a two-stage energy-efficient resource allocation algorithm. In the first stage, an auction-matching-based joint relay selection, spectrum allocation, and power control algorithm is derived, which employs an English-auction approach for matching preference updating and conflict avoidance, and optimizes the energy efficiency of two-hop D2D–V2V and cellular links simultaneously in an iterative fashion. In the second stage, a nonlinear fractional programming based power control algorithm is developed to maximize the energy efficiency of the base station. Theoretical properties in terms of convergence, stability, and complexity are analyzed. Finally, the proposed algorithm is evaluated based on real-world road topology and realistic vehicular traffic. Numerical results demonstrate that the proposed algorithm achieves superior performance in terms of energy efficiency and network coverage compared to other heuristic algorithms.

Intelligent, Energy Saving Power Supply and Control System of Hoisting Mine Machine with Compact and Hybrid Drive System / Inteligentne, Energooszczędne Układy Zasilania I Sterowania Górniczych Maszyn Wyciągowych Z Napędem Zintegrowanym Lub Hybrydowym

Abstract In the paper present’s an analysis of suitableness an application of compact and hybrid drive system in hoisting machine. In the paper presented the review of constructional solutions of hoisting machines drive system, driving with AC and DC motor. In the paper presented conception of modern, energy sparing hoisting machine supply system, composed with compact motor, an supplied with transistor or thyristor converter supply system, and intelligent control system composed with multilevel microprocessor controller. In the paper present’s also analysis of suitableness application an selected method of artificial intelligent in hoisting machine control system, automation system, and modern diagnostic system. In the paper one limited to analysis of: fuzzy logic method, genetic algorithms method, and modern neural net II and III generation. That method enables realization of complex control algorithms of hosting machine with insurance of energy sparing exploitation conditions, monitoring of exploitation parameters, and prediction diagnostic of hoisting machine technical state, minimization a number of failure states. In the paper present’s a conception of control and diagnostic system of the hoisting machine based on fuzzy logic neural set control. In the chapter presented also a selected control algorithms and results of computer simulations realized for particular mathematical models of hoisting machine. Results of theoretical investigation were partly verified in laboratory and industrial experiments.

An experimental assessments on different control techniques of series hybrid active power filter

Summary For high power application of active power filters, series hybrid active power filter (SHAPF) configuration is popular because of its multi-functionality and small capacity inverter requirement. The SHAPF filtering characteristics and efficiency of harmonic compensation depend on the control algorithm applied to calculate the reference voltage. This paper compares the filtering performance of SHAPF using three different control algorithms applied for reference voltage generation. The first control algorithm derives reference voltage proportional to the source current harmonics, where the source current harmonics are separated using the p-q theory. The second control algorithm derives reference voltage from load voltage harmonics separated using the dual p-q theory. The third control algorithm derives reference voltage using vector algebra-based voltage decomposition method in three-phase coordinate system. The mathematical formulation of selected three control algorithms along with their applications to SHAPF is presented. To check the filtering performance of SHAPF with selected control algorithms, an experimental prototype of SHAPF controlled by the ARM Cortex M4 microcontroller STM32F407VGT6 is developed. Experiments are performed to check the performance of SHAPF in compensating current harmonic generating load and voltage harmonic generating load. The results of compensation, the complexity involved in extracting reference, and control algorithm computation time are compared. The comparison shows that the operation of the voltage decomposition method is comparatively better due to fast extraction of the reference voltage. Copyright © 2014 John Wiley & Sons, Ltd.

Pricing-based spectrum leasing in cognitive radio networks

According to the property rights model of cognitive radio, primary users who own the spectrum resource have the right to lease part of the spectrum to secondary users (SUs) in exchange for appropriate profit. In this study, the authors propose a pricing-based spectrum leasing framework between one primary user and multiple SUs. In this scenario, the primary user attempts to maximise its utility by setting the price of spectrum, the selected SUs have the right to decide their power levels to help the primary user's transmission, aiming to obtain corresponding spectrum access time. Then, the spectrum leasing problem can be cast into a Stackelberg game, which jointly maximises the utilities of primary user and SUs. Moreover, the authors design a joint relay selection and admission control algorithm, which can select the proper SUs and protect all the active SUs’ performance. Numerical results show that the proposed pricing-based spectrum leasing framework is effective, and the performance of the primary user and the SUs is both improved, compared with the traditional mechanism without cooperation.

Active suppression of nonlinear composite beam vibrations by selected control algorithms

This paper is focused on application of different control algorithms for a flexible, geometrically nonlinear beam-like structure with Macro Fiber Composite (MFC) actuator. Based on the mathematical model of a geometrically nonlinear beam, analytical solutions for Nonlinear Saturation Controller (NSC) are obtained using Multiple Scale Method. Effectiveness of different control strategies is evaluated by numerical simulations in Matlab–Simulink software. Then, the Digital Signal Processing (DSP) controller and selected control algorithms are implemented to the physical system to compare numerical and experimental results. Detailed analysis for the NSC system is carried out, especially for high level of amplitude and wide range of frequencies of excitation. Finally, the efficiency of the considered controllers is tested experimentally for a more complex autoparametric “ L-shape” beam system.