Development Of Control System Research Articles

Advances in flight control systems for modern commercial aircraft

Abstract. In consequence of the accelerated evolution of aviation technology, the flight control systems of commercial aircraft have undergone substantial technological advancement and system optimization. This paper presents a comprehensive analysis of the recent advancements and future trends in flight control systems for commercial aircraft. The fundamental concepts and historical evolution of flight control systems are delineated, with a particular emphasis on the evolution of autopilot systems and the technological advancements of flight control systems (FCS). Furthermore, it examines innovations in flight control algorithms, including adaptive control and intelligent algorithms, and investigates the impact of fault-tolerant design on flight safety. In terms of system integration, the integration of flight control systems with navigation and communication systems, as well as performance enhancements resulting from system optimization, is explored. In addition, it examines the potential impact of intelligence and networking on future flight control systems, with a particular focus on the prospects for the application of artificial intelligence, quantum computing, and new material technologies. The synthesis of these areas provides a systematic understanding of the technological evolution and future development of flight control systems for commercial aircraft.

Rapid Optimization of Active Disturbance Rejection Controller Parameters for Quadrotor UAVs Using Kriging Surrogate Modeling

The Active Disturbance Rejection Controller (ADRC), celebrated for its superior resistance to interference, presents itself as an exemplary solution for the development of control systems that are designed to accommodate substantial changes in payload weight for quadrotor Unmanned Aerial Vehicles (UAVs) and to endure robust side winds along with other challenging operational scenarios. Despite the inherent complexity due to the numerous parameters required for the configuration of the ADRC, an innovative method utilizing the Kriging surrogate optimization algorithm has been introduced to automate and expedite the generation of these controller parameters. The development of the ADRC begins with the dynamics model of the quadrotor UAV, followed by the identification of key design parameters. These parameters are then rapidly optimized through the Kriging surrogate optimization algorithm. The controller’s effectiveness is confirmed by implementing the ADRC on Pixhawk flight control hardware, with a comparative analysis of the attitude response under various operating conditions, thereby validating the ADRC’s superior anti-disturbance performance.

Research on Autonomous Navigation of Unmanned Aerial Vehicles Based on Correlation-Based Image Comparison Methods

Relevance. Autonomous navigation of unmanned aerial vehicles (UAVs) is one of the key challenges in the modern aerospace industry. Specifically, for small UAVs, the task of autonomous navigation becomes even more complex due to limitations in computational resources and sensor capabilities. Optimizing navigation methods under such conditions is a pressing issue that requires solutions capable of providing high performance with minimal resource consumption.Objective. Improving the efficiency of autonomous navigation for small UAVs by using correlation methods for image comparison. Achieving this objective is linked to the development and evaluation of algorithms that provide high-speed and accurate navigation with limited computational resources. The work uses methods such as the autocorrelation function, Pearson correlation, the structural similarity index, and a simple neural network for image comparison.Solution. The research showed that the autocorrelation-based approach demonstrates the best performance under low computational resources. It ensures high-speed processing of the entire image and shows optimal detection accuracy. Compared to other methods presented in the study, the autocorrelation approach is capable of working not only with noise in the "reference" map but also of using alternative areas with altered patterns as detected regions.The scientific novelty of the study is determined by the systematic comparison of various methods applied to the task of image comparison for small UAVs with limited computational resources. Unlike well-known works in the field of correlation-extreme systems, this research focuses on the use of a "reference map" and a search area, representing two different images of the same terrain taken from different sources. This is a key difference, as most methods are not highly efficient in processing such images where patterns may differ significantly.The practical significance of the developed algorithm lies in the fact that the proposed autocorrelation-based method can be used by developers of autonomous small UAV control systems to reduce computational load and increase data processing speed.

Digital Revolution and Management Control Practices: The Impact of Digitalization in Moroccan Private Companies

This paper aims to investigate how digitalization activity influences the management control practices of Moroccan private enterprises. With technologies developing daily, organizations are grappling with how best to utilize these technologies in their MCs, thus the need for development. The main research question of this paper is therefore to: Examine the impact that digital transformation brings to the decision making, performance measurement and resource allocation in private firms in Morocco. To this end, the current study used both survey and interview questionnaires with managers drawn from different sectors and industries. These results therefore indicate that digitalization indeed increases the level of transparency and flow of daily monitoring information required in the management practices to increase its operational efficiency and overall strategic flexibility. That being said, the study also outlines some of the problematic areas like, resistance to change, training employees to learn how to deal with new tools. In conclusion, thus, despite the opportunities that the new digital reality opens to the approach and development of the management control systems in Moroccan private companies, this new reality poses a number of challenges which have to be met to improve the opportunities fully. The study adds to the current body of knowledge regarding digitalization of operations and the impact it has on managerial processes and provides recommendations to those practitioners seeking to enhance their effectiveness within the context of digitally enabled business environment.

Development of a control algorithm for a boost converter with the possibility of dynamic correction of control system parameters

RELEVANCE of the study lies in the development of an algorithm for controlling a DC voltage converter capable of providing a high-quality transient process with a wide change in the input parameters of the control object, affecting its nonlinear properties. purpose. THE PURPOSE. To consider methods for the development of continuous and discrete control systems for a step-up DC voltage converter and to obtain an analytical dependence of accounting for the nonlinear properties of the converter that affect the quality of regulation of the output voltage of the voltage stabilizer METHODS. The method of the average geometric root was chosen as a method for calculating the coefficients of the DC voltage converter control system, and the current circuit is adjusted using the technical optimum method, and the voltage circuit using the symmetric optimum method. The obtained analytical solutions of mathematical models and simulation models were compared in the SimInTech software environment. results. RESULTS. The simulation results show that the analytical solution of the mathematical model of the voltage stabilization system has a shorter transition time than the simulation results. This is due to the fact that in the mathematical model according to which the continuous control algorithm was synthesized, the presence of a power input filter, as well as the degree of their precharge, is not taken into account. Because of this, the transition time on simulation models is slower, applied 4 times than in the mathematical model. conclusion. CONCLUSION. When comparing the algorithms with each other, it can be seen that the algorithm obtained by converting a continuous algorithm by the Tusten method has the shortest transition time than all other algorithms. The continuous algorithm and the algorithm obtained by the inverse Euler transformation method have a speed close to each other, and the algorithm obtained by the direct Euler transformation turns out to be the slowest.

Research on Tilt-Rotor Type Water-Air Multicopter

In Japan today, aging infrastructures such as dams and bridges are becoming an issue. We considered that the use of a hydrofoil multicopter would be an effective method for inspecting and surveying such facilities. When using a hydrofoil multicopter, it is possible to take off from the shore, land on the water, and dive without using a ship. The aircraft developed in this study has the capability to communicate with the land. The aircraft developed in this study is equipped with a communication buoy, and after landing on the water, a communication cable is extended from the buoy to enable underwater maneuvering via wired communication. We report on the development of a precise control system for flight and diving of this aircraft.

Automated yeast cultivation control using a biosensor and flow cytometry.

Effective microbial bioprocessing relies on maintaining ideal cultivation conditions, highlighting the necessity for tools that monitor and regulate cellular performance and robustness. This study evaluates a fed-batch cultivation control system based on at-line flow cytometry monitoring of intact yeast cells having a fluorescent transcription factor-based redox biosensor. Specifically, the biosensor assesses the response of an industrial xylose-fermenting Saccharomyces cerevisiae strain carrying the TRX2p-yEGFP biosensor for NADPH/NADP+ ratio imbalance when exposed to furfural. The developed control system successfully detected biosensor output and automatically adjusted furfural feed rate, ensuring physiological fitness at high furfural levels. Moreover, the single-cell measurements enabled the monitoring of subpopulation dynamics, enhancing control precision over traditional methods. The presented automated control system highlights the potential of combining biosensors and flow cytometry for robust microbial cultivations by leveraging intracellular properties as control inputs.

Design and Development of a Prototype Water Level Control System for Early Flood Detection Based on The Internet of Things: a Case Study of Kambaniru Dam

Flooding can occur due to overflowing water during the rainy season, therefore early detection of the water level in dams as water runoff processing structures is needed. When heavy rain occurs it can result in flood disasters that are detrimental to communities, dam infrastructure and agricultural land. This research aims to monitor water levels online as initial information about impending flood disasters. Monitoring using Internet of Things (IoT) based technology is intended to obtain water level information in real time. In this device the HC-SR04 ultrasonic sensor is used as a water level reader, a waterflow sensor as a water discharge reader and a servo motor as a sluice gate controller. The results of this research are a prototype of a water level detection device that can provide water level information and can control sluice gates. automatically and can be controlled manually or remotely via the latest notifications in the Blynk application using the MQTT protocol. This research was carried out by taking information based on the results of direct observations and interviews with officers at Kambaniru Dam, Lambanapu Village, Kambera District. In this way, the prototype of this detection tool will be easy to use as initial information about the possibility of flooding.

Development of smart control system for leakage warning in compact roofs

Smart sensor technologies to monitor temperature and moisture conditions in building components can be used to give automatic warnings for abnormal high levels of moisture. Flat compact roofs are of particular interest in this context, especially due to their vulnerability to rain leakages through the roofing membrane. Installing moisture sensors in such roofs measuring relative humidity (RH) and free water may give an early warning of rain leakages or condensation owing to air leakages from indoors before damage occurs. One challenge is, however, that normal moisture redistribution in the insulation layer over the season or a day may give very high levels of RH in the top or bottom part of the insulation. A sensor system must be able to distinguish between these normal RH levels and leakage events. Together with a sensor technology and control system producer, a semi-quantitative system that defines typical or normal RH levels in the insulation layer of the roof during the year has been developed. To investigate the applicability of such a system, measurements from the compact roofs of two pilot buildings located in Norway have been analysed. The objective has been to identify connections between measured data, occurring climate conditions and seasonal variations. Results may be used to further develop and improve the algorithms of the leakage warning system.

Development of a Quality Control System Using Modal Analysis to Evaluate a Multi-Point Projection Welding Process.

The article presents the results of the development and research regarding the application of modal analysis to the evaluation of a multi-point projection welding process. The quality control system is based on the information provided by modal analysis for the entire welding station after the previously completed welding process. The research is carried out due to the lack of an effective method for assessing the course of the multi-spot projection welding process in the case of a single current circuit passing through many points at the same time. A study of the applicability of modal analysis in investigating the quality of a multipoint joint was conducted for four DIN 928 nuts welded to a steel profile. The aim of the study was to determine the influence of weld defects on the displacement of resonant frequencies. To realize the objective, the dynamic properties of the entire welding station, including the sample in the frequency domain, were investigated. In the first stage of the study, a finite element method was used to perform modal analysis and examine the form of vibrations for the individual natural frequencies of the welding fixture including the sample. Then, quality verification using the dynamic resistance method was performed, which was compared later with the modal approach. The last stage of the study was to conduct modal analysis in the frequency domain to verify the numerical studies.

Design and Development of Intelligent Access Control System Integrating Hi3861 and K210

Design and Development of Intelligent Access Control System Integrating Hi3861 and K210

A scalable Deep Q-Learning approach for hot stamping process under dynamic control environment

Despite the significant advancements in Artificial Intelligence and its applications, traditional control techniques still dominate industrial and manufacturing processes. These techniques often rely on static setups and default values, which make them resistant to environmental changes. However, in dynamic and uncertain contexts, real-time process adaptation offers significant advantages in decision-making and control. In this research, we present a novel approach utilising Deep Reinforcement Learning for real-time dynamic parameter adaptation and autonomous control of processes in unpredictable environments. The effectiveness of our proposal is illustrated through the development and training of a Deep Q-Learning control system tailored for optimising a hot stamping process. The control model aims to minimise defect rates and enhance efficiency by reducing forming times during batch productions. Comparative analysis against conventional industry practices, characterised by static and non-adaptive time settings, validates the efficacy of our solution, with improvements of over 20% in the times, depending on the scenario considered. Experimental evaluation in a semi-industrial pilot setting not only demonstrates the adaptability of our approach but also highlights its potential scalability for diverse industrial applications.

Structural organization of a fuzzy system for monitoring the condition of equipment and control of a gas distribution station

Objective. The objective of the study is to create an intelligent system for monitoring the condition of equipment and managing the operating modes of a gas distribution station (GDS) based on a logical-linguistic (fuzzy) model for making assessment and management decisions. Method. The theory of fuzzy sets and algorithms of L. Zade was used, which allows the use of qualitative concepts and allows the creation of logical-linguistic models of the controlled process on their basis. Result. The article provides a rationale for using an intelligent system for assessing the state of equipment and managing a gas distribution station (GDS) based on a logical-linguistic (fuzzy) model for representing the knowledge base for making assessment and management decisions. This approach provides developers of control systems for complex processes and/or objects with a clear and effective way to describe their dynamics. Conclusion. An intelligent system for assessing equipment conditions and managing the operating modes of the gas distribution station, in which the knowledge base is presented in the form of a logicallinguistic (fuzzy) model, allows for solutions to the specified problem based on the knowledge of an experienced expert (dispatcher), which will improve the quality of service and reduce the operating costs of the gas distribution station

Development of optimization control system for chiller plant based on a predictive model with Multi Stack LSTM and Deep Learning Neural Network Multi Output

Net-zero emissions become necessary for every country, implementing energy efficiency is the way to achieve it. Released report by the International Energy Agency in September 2022 stated that energy efficiency and electrification are the main priorities for Indonesia in achieving NZE. Currently, more than 50 % of energy consumption in buildings is intended for cooling systems (chillers plants). Thus, energy efficiency in buildings cooling systems has great potential to help achieve NZE and support SDGs. This research is intended to find an optimization control system for chiller plant in order to reduce energy consumption of cooling system in building. The development of this optimization control system is based on predictive model with Multi Stack LSTM and Deep Learning Neural Network. This research developed four different optimization control system frameworks. Every developed framework then will be tested by carrying out simulation with Chiller Plant model. The important parameter was found by using correlation matrix. Based on the correlation matrix, it was found that the most influence parameter that affect chiller plant performance are Tcws and Twb. It leads developed Frameworks 1 become the best compared with others developed frameworks in this research, because it used Twb as a variable input in parallel with performance prediction. This developed framework has deficient error MAE, MSE, RMSE respectively 0.8079, 0.6527, 0.8079 and able to reduce energy consumption by 10.72 %.

The design and implementation of a smart greenhouse control system

Abstract Against the dual backdrop of continuously improving agricultural production levels and the global scarcity of water resources, countries worldwide are actively exploring the topic of water conservation. The development of an intelligent irrigation control system could make a significant contribution to water conservation efforts, while also helping to increase crop yields. This project adopts a modular design approach, utilizing sensors to collect real-time environmental data surrounding the crops. If the data falls outside the predefined optimal range for crop growth, the system automatically initiates irrigation, cooling, and supplemental lighting. The hardware design primarily consists of a data perception module and a control terminal module. After testing, the system runs stably and can accurately obtain information such as soil moisture, light intensity and ambient temperature, and basically realize the digital intelligent control of crop growing environment.

Spatio-temporal grid-based storage method for aerospace operation and control data

Abstract The current aerospace operation and control data system has problems such as difficult integration organization and difficult data retrieval in terms of mission planning efficiency, which limits the development of satellite operation and control systems. This paper is oriented to the organization and management of aerospace operation and control data, according to the nature of the current big data indexing model. Based on the principle of earth dissection, we aim to design a grid that can be indexed. First of all, the Earth is dissected to form a hierarchical and uniformly distributed global geographic grid, and then the formed grid is encoded to store satellite attribute information and orbit data for mission planning. Finally, through the data retrieval experiment, the retrieval time is within 100 milliseconds under ten million pieces of data, which proves that the spatial-temporal grid model has the characteristics of efficient and fast retrieval. It can provide the basic information for satellite mission planning to further improve the efficiency of satellite mission planning and management and the efficiency and quality of satellite mission planning.

Development of a Fuzzy Variable Rate Irrigation Control System Based on Remote Sensing Data to Fully Automate Center Pivots

Development of a Fuzzy Variable Rate Irrigation Control System Based on Remote Sensing Data to Fully Automate Center Pivots

Leveraging large language models for comprehensive locomotion control in humanoid robots design

This paper investigates the utilization of large language models (LLMs) for the comprehensive control of humanoid robot locomotion. Traditional reinforcement learning (RL) approaches for robot locomotion are resource-intensive and rely heavily on manually designed reward functions. To address these challenges, we propose a method that employs LLMs as the primary designer to handle key aspects of locomotion control, such as trajectory planning, inverse kinematics solving, and reward function design. By using user-provided prompts, LLMs generate and optimize code, reducing the need for manual intervention. Our approach was validated through simulations in Unity, demonstrating that LLMs can achieve human-level performance in humanoid robot control. The results indicate that LLMs can simplify and enhance the development of advanced locomotion control systems for humanoid robots.

Control system development for monitoring nutrition of curly mustard plants in horizontal NFT hydroponic based-IoT

Control system development for monitoring nutrition of curly mustard plants in horizontal NFT hydroponic based-IoT

Theoretical basics of the self-learning system of intelligent locomotive decision support systems

Analysis of works in the field of artificial intelligence allows to make an assumption that today there is a sufficiently developed theoretical basis for the development of intelligent control systems for locomotive control. This will minimize the risks associated with the human factor on the railways. The paper presents the theoretical rationale for the development of a knowledge base for intelligent locomotive control systems. The approach and structure of the self-learning system of intelligent DSS is proposed, the advantage of which is the presence of a fuzzy classifier that works according to the set criteria and determines a fuzzy image of the current train situation. Learning a fuzzy classifier consists in finding a vector K that minimizes the distance between the results of logical inference and experimental data from the sample. The knowledge base is implemented using linguistic variables formalized by methods of fuzzy logic. The use of linguistic values makes it possible to design the base using the usual language of communication, which greatly simplifies both the design process itself and the analysis of the system's performance. Also, the knowledge base has the possibility of constant self-improvement. This happens in two ways. The first is by adding new rules to the knowledge base in case the current situation does not match the existing ones in the base, in which case an additional rule is created and checked for adequacy. The second way is a mechanism for ranking rules in the knowledge base. If the control action of the locomotive driver coincided with the recommendation of DSS in the current situation, then the rating of this recommendation (rule) increases, and in the future the rule selection algorithm will choose one or another control action for the current situation that has the highest rating (that is, it has already been verified several times person). The experiment has shown that the use of intelligent DSS has positive results. On average, the DSS made the correct train control decisions faster than the locomotive driver.