Optimization Of Control System Research Articles

Multi-objective optimization of active control system using population guidance and modified reference-point-based NSGA-II

The optimization of the active control system is pivotal in achieving an acceptable performance level while minimizing control costs. The optimal location of actuators, efficient control force, and adequate vibration reduction are critical objectives in control system optimization. This study introduces a novel optimization algorithm, the Population-Guided and Modified Reference-Point Based Non-Dominated Sorting Genetic Algorithm-II (PMR-NSGA-II), tailored for actuator placement and tuning that incorporates input and output user preference. The analysis focuses on optimizing the vibration control of a 20-story steel frame building. The PMR-NSGA-II could reduce the computational expenses of three objectives optimization by guiding the initial population using prospective population prediction and orienting the search towards a reference point. The pre-calculated input energy distribution guides the determination of the prospective initial population. The reference point, along with the allowable drift and actuator capacity constraints, focuses the search process to efficiently obtain the Pareto fronts with less effort wasted to explore the less preferred areas in the search space. The non-dominated sorting and elitist operators are also employed to fasten the convergence. The structural analysis is conducted via non-linear time history analysis with seven ground motions considered. The PMR-NSGA-II exhibits significant computational efficiency in the active control system optimization. Results demonstrate that PMR-NSGA-II could provide the near-optimal solution closest to the reference point with a smaller population size and a faster convergence rate than the NSGA-II. The computational time per generation of the proposed PMR-NSGA-II is 1.36 to 2.43 times faster than NSGA-II for the seven ground motions analyzed. By applying PMR-NSGA-II, the number of individuals that need to be assessed to reach convergence is reduced by 50 % to 88 % compared to NSGA-II. Ultimately, the near-optimal configurations could significantly reduce the building responses and increase the performance level of the building based on FEMA 356 and ASCE 41 acceptance criteria.

Analysis and optimization of smart home control system stability analysis and optimization

Analysis and optimization of smart home control system stability analysis and optimization

Research and Evaluation of Coal Mine Ventilation Management

Abstract The main scientific and practical results of the study and assessment of ventilation control in coal mines using the decomposition method based on data from the analysis of aerodynamic parameters of air distribution are presented. This will lead to increased safety and reduced accident rates in mining operations at coal mines. Methods of decomposition, statistical dynamics, set theory, fundamental laws of mining aerodynamics, and discrete mathematics are described. The studies were carried out based on the experimental data of the authors using various mathematical and statistical methods. A universal technique is proposed that can be used in mine workings for analysis, assessment of ventilation processes and associated aerodynamic factors, as well as optimization of the air distribution control system under various operating modes. A methodology and application software developed on its basis have been compiled. Automated geographic information systems such as GIS K-MINE® (Krivoy Rog, Ukraine) and VENTSIM are recommended. This system has been approved by regulatory authorities in Ukraine and is utilized by specialists from industry institutes, State Mining Supervision authorities, mines, and others. They are adapted for coal mines, ensuring increased safety and efficiency of work. This will ensure increased safety, labor protection, and reduced accident rates in coal mining operations.

Control system optimisation of biodiesel-based gas turbine for ship propulsion

<p>Reducing a gas emission of shipping transportations become a main goal of international maritime organization to achieve a clean energy. One of best scenarios to achieve this goal is to shift a fossil fuel to a renewable energy-based fuel of a ship propulsion. This paper studies an optimization of a control system of the renewable-based small gas turbine engine for the ship propulsion. Proposed control system consists of a proportional-integral with engine performance limiters to avoid an engine damage. Proportional-integral gains are tuned by a whale optimization algorithm. A gain scheduling analysis of a step response is performed to obtain a searching area of tuning parameters and values of constant gains. In this step, the gains are modeled as function of plant variables. After the searching area is obtained, the proportional-integral gains are optimized using the whale optimization algorithm while the additional gains are set as constant values. Using this scenario, stable and optimal gains have been successfully achieved. Results show that the proposed method has better performance than that of the previous methods, i.e. gain scheduling and gain scheduling optimized by the whale optimization algorithm. The proposed method has lowest fitness value and does not have an overshoot problem.</p>

Artificial intelligence based smoke flow mechanism analysis and prediction patterns of fire for large space building

The deposition and dynamics of smoke layers in large-space building fires are governed by a complex interplay of factors, making the prediction of such dynamics using traditional mathematical models challenging. In response, this study introduces a Back Propagation (BP) neural network model, devised from field simulation data, to efficiently forecast the temporal progression of smoke layers. It was observed that the model exhibits minimal training errors and high processing speeds, thereby fulfilling the stringent accuracy demands of fire engineering. Specifically, the model achieved a minimum relative error of 0.0005 and a maximum of 0.0845 across various prediction points, underscoring its reliability and precision. The ability of this BP neural network model to predict smoke layer changes significantly enhances the design optimization of smoke control systems swiftly and accurately in large buildings and supports rapid, informed decision-making during fire emergencies. Moreover, the model facilitates the development of engineering calculation models tailored for the quick prediction of fire smoke dynamics, which are essential for both theoretical research and practical applications. This approach not only conserves experimental resources but also advances the implementation of scientific, effective rescue operations in the event of large space building fires.

Precision Regulation and Forecasting of Greenhouse Tomato Growth Conditions Using an Improved GA-BP Model

Tomatoes are annual herbaceous plants of the family Solanaceae. They have very stringent requirements for their growing environment and climate conditions. To precisely control the greenhouse environment for tomato growth, this project designed and implemented a monitoring system utilizing programmable logic controllers and a data acquisition system for monitoring. Sensors are installed at key locations in the greenhouse, such as near the ground, in the plant canopy, and under the roof, to monitor environmental parameters such as temperature, humidity, light intensity, and carbon dioxide concentration in real time. A three-layer feedforward GA-BP neural network model was established using soil temperature, soil humidity, air humidity, and illumination as predictive parameters. This technology predicts optimal environmental parameters and performs real-time monitoring and intelligent control, overcoming the traditional BP neural network’s drawbacks of slow convergence, susceptibility to disturbance, and poor generalization ability, with the current model’s average prediction error being less than 5%. It provides a theoretical basis and decision support for precision control and related environmental forecasting in agricultural projects. Based on the growth characteristics, physiological and morphological features of tomatoes, and the comprehensive impact of planting conditions, the technology enhances tomato yield and quality while reducing production costs and labor inputs. It mainly includes the establishment of tomato growth models, measurement and control of environmental parameters, and the design and optimization of intelligent decision control systems. Research outcomes in this field are significant for improving agricultural production efficiency, ensuring food security, and promoting sustainable agricultural development.

Parameter optimization of vibration control system for adjacent building structures based on negative stiffness inerter damper

Building structures are subjected to strong earthquakes, which result in lateral collisions between them. Such collisions often cause severe structural damage and exacerbate the seismic hazard risk of building structures during earthquake events. This paper discusses the application of vibration control devices based on negative stiffness inerter damper in single-story adjacent building structures. The dynamic equations of the vibration control system containing different types of negative stiffness inerter damper under seismic excitation are established as a unified model. The H2 norm theory and Monte Carlo pattern search method are used to optimize the design parameters to improve the vibration control performance of the system, and the dynamic characteristics of the system are investigated. The results demonstrate that attaching negative stiffness inerter damper to adjacent building structures can effectively improve the overall seismic capacity reserve of the building and reduce the risk of collision of adjacent building structures; improve the robustness and stability of the system, and better reduce the displacement response of the building structure under seismic excitation. In addition, the potential of NSID-based vibration control devices to convert seismic energy into usable electricity has been investigated.

Optimization of dynamic control systems using water cycle algorithm

In this paper, path optimization in dynamic control systems was performed using water cycle algorithm. The core principles and assumptions that guide the suggested technique are drawn from nature and are based on observations of the water cycle process and how rivers and streams naturally flow into the sea. The Water Cycle Algorithm has been recognized as a reliable method for dealing with large-scale optimization challenges, largely because of its proficient exploitation and exploration capabilities. This Algorithm does not require the continuity of functions when specifying the issue, unlike many gradient-based approaches. This is achieved through the use of a simple mathematical model that represents the water cycle process. This model is used to simulate the movement of water droplets in a cloud, where each water droplet represents a potential solution to the optimization problem. Although water cycle optimization has been utilized successfully to tackle optimization issues, this is the first time dynamic control optimization has been used. The control problem is switched out for an optimum programming problem in order to find optimal pathways utilizing the Shifted Chebyshev polynomial. The water cycle method was then used to resolve the new issue. The numerical results demonstrate the excellent precision with which this search method can discover solutions.

Optimization of Unmanned Aerial Vehicle Flight Control Sensor Control System based on Deep Learning Model

Based on data modelling strategies have created reliable classifier designs for various classes and other neural network applications. The fact that modelling complexity rises with the total number of groups in the system does is one of the approach's major shortcomings. No matter how well it performs, it could make the classifier's design ugly. This article discusses the development of a novel, logic-based Optimum Bayesian Gaussian process (OBGP) classifier to reduce the number of separate empirical models required to accurately detect various fault types in industrial processes. The precision of the OBGP classifier's defining faults also contrasts with the results of other approaches documented in the literature.

Energy Optimization of the Multi-objective Control System for Pure Electric Vehicles based on Deep Learning

Advancements in information technology have revolutionized multiple sectors such as healthcare, industrial control, and environmental monitoring. With the advent of smaller, more sophisticated and wireless sensors, their applications have expanded across various industries. These sensors offer numerous advantages like cost-effectiveness, easy setup, reliable transmission, and high capacity for data processing. However, despite their benefits, there are certain limitations to consider. The primary constraint that affects their lifespan is energy availability, as replacing or recharging power sources for nodes can be challenging or infeasible. The reliance on batteries hampers data analysis by network nodes, hindering the exchange of information. Hence, prolonging the network's overall lifespan is crucial for optimizing its performance. The existing approaches, with their tried-and-tested practices and heterogeneity, require enhancements to address specific characteristics. In every application, two critical aspects are the duration of network operation and energy consumption for data routing. Through comparative analysis, it is evident that various algorithms and techniques can reduce energy usage to different extents. Based on these findings, a recommended strategy is to achieve a significant 70% reduction in energy consumption.

Research on Intelligent Vehicle Trajectory Tracking Control Based on Improved Adaptive MPC.

Intelligent vehicle trajectory tracking exhibits problems such as low adaptability, low tracking accuracy, and poor robustness in complex driving environments with uncertain road conditions. Therefore, an improved method of adaptive model predictive control (AMPC) for trajectory tracking was designed in this study to increase the corresponding tracking accuracy and driving stability of intelligent vehicles under uncertain and complex working conditions. First, based on the unscented Kalman filter, longitudinal speed, yaw speed, and lateral acceleration were considered as the observed variables of the measurement equation to estimate the lateral force of the front and rear tires accurately in real time. Subsequently, an adaptive correction estimation strategy for tire cornering stiffness was designed, an AMPC method was established, and a dynamic prediction time-domain adaptive model was constructed for optimization according to vehicle speed and road adhesion conditions. The improved AMPC method for trajectory tracking was then realized. Finally, the control effectiveness and trajectory tracking accuracy of the proposed AMPC technique were verified via co-simulation using CarSim and MATLAB/Simulink. From the results, a low lateral position error and heading angle error in trajectory tracking were obtained under different vehicle driving conditions and road adhesion conditions, producing high trajectory-tracking control accuracy. Thus, this work provides an important reference for improving the adaptability, robustness, and optimization of intelligent vehicle tracking control systems.

Method of reliability control of thermoelectric systems to ensure thermal regimes

The paper presents the results of research of controllability of the thermoelectric system for ensuring thermal modes of electronic equipment, including a regulator, a cooler, and a component of excess heat removal to the environment. It is shown that for the use of methods of optimization of automatic control systems it is necessary to study the transfer and dynamic characteristics of the object - thermoelectric cooling device with one input and one output. The mathematical model of the thermoelectric cooler of the system of providing thermal modes of a given design is presented, which takes into account the influence of the conditions of interaction of the heat sink with the medium on the main significant parameters, reliability indicators, dynamic and energy characteristics of the single stage cooler. The model is created for the operating range of cooling, level of thermal load, geometry of thermocouple branches, different temperature of the medium, for the characteristic thermal regime of maximum cooling capacity. The results of calculations of the main significant parameters, reliability indicators, dynamic and energy characteristics of the cooler for different medium temperature in the operating temperature range and variation of conditions of heat exchange of the heat sink with the medium are given. It is shown that as the intensity of heat exchange of the heat sink with the medium increases, the temperature difference between the heat sink and the medium decreases. This makes it possible to significantly reduce the relative failure rate, increase the probability of failure-free operation of the thermoelectric cooler and control the reliability indicators of the device of a given design during operation.

Total Energy-Based Control Design and Optimization for Lift-Plus-Cruise Aircraft

This paper presents a methodology for optimizing a Total Energy–based control system architecture for a lift-plus-cruise vertical takeoff and landing urban air mobility concept. The Total Energy Control System algorithm, which was originally developed for fixed-wing applications, is extended to also be applicable to hovering and transitioning flight. Control system parameters are optimized using a genetic algorithm optimization scheme, subject to constraints on dynamic stability and control response characteristics. Control system optimization and flight simulations are performed using the Modular Aircraft Dynamics and Control Algorithm Simulation Platform. Results presented include simulations of the optimized flight control system operation for maneuvering scenarios representative of hover, transition, and forward flight conditions as well as during transitions between vertical and forward flight modes. The presented results demonstrate the effectiveness of the proposed control system architecture and the demonstrated optimization methodology.

Adaptive MPC-Based Lateral Path-Tracking Control for Automatic Vehicles

Adaptive MPC-Based Lateral Path-Tracking Control for Automatic Vehicles

APRTC: advanced precise rapid thermal cycling in blow molding by applying fuzzy controller on thermoelectric devices for cooling and heating applications

This study presents an innovative approach to enhance thermal control in the blow molding production process, specifically focusing on achieving rapid and precise temperature regulation. To address the significant time consumption during mold closure, strategically deploying thermoelectric modules (TEMs) arranged according to the mold’s geometry enables meticulous temperature monitoring through integrated thermocouples. The configuration forms a matrix of interconnected TEMs, thermocouples, and controllers, orchestrating precise temperature adjustments across the mold surface. The research integrates fuzzy algorithms to facilitate seamless communication between thermoelectric devices and thermocouples for precise mold temperature control. Simulation of thermoelectric devices using COMSOL Multiphysics and implementation of fuzzy controllers in Matlab, connected through a live-link, enables real-time. I have adjustments and precise control. This integrated approach allows for a comprehensive analysis and optimization of the thermal control system, ensuring effective and adaptive temperature regulation. TEMs, operating as solid-state heat pumps, offer size versatility, adaptability to diverse mold shapes, and maintenance-free operation. Leveraging individual control capabilities significantly enhances heat transfer resolution, enabling precise manipulation of heat flux. Emphasizing the reduction of cooling time and achieving high-gloss surface quality, the study introduces the Advanced Precise Rapid Thermal Cycling in Blow Molding (APRTC) method. APRTC strategically employs TEMs to expedite cooling and elevate temperature determination accuracy, guaranteeing enhanced temperature resolution on the core mold surface. Furthermore, the study optimizes energy utilization through a closed-loop fluid circulation system, harnessing waste energy from other stages. Comparative analysis highlights APRTC's superiority, revealing a remarkable 91%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$91 \\%$$\\end{document} reduction in temperature differentials during the cooling cycle and an 89%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$89 \\%$$\\end{document} decrease in the heating stage. These findings underscore APRTC's efficacy in maintaining minimal temperature variations throughout the entire molding process.

Developing and Simulating a Model using Proportional Integral Derivative (PID) on Matlab/Simulink for Speed Control of DC Motor

This study uses MATLAB/SIMULINK to perform a thorough inquiry of the use of Proportional Integral Derivative (PID) control for accurate speed control of a DC motor. To increase steady-state error, decrease settling time, and improve transient response, the suggested model uses sophisticated PID tuning techniques. The efficacy of the PID controller in preserving the intended speed setpoints is demonstrated by means of comprehensive simulation experiments that assess its performance in a range of operational scenarios. This work creates a useful foundation for DC motor speed control system optimization in addition to offering insightful information on PID-based control techniques.

Optimization and evaluation of a neural network based policy for real-time control of construction factory processes

This paper focuses on the development, optimization, and evaluation of an intelligent real-time control system for the fabrication of precast reinforced concrete components. The study addresses the unique challenges associated with real-time control in the construction manufacturing industry, including high customization, uncertain work demand, and limited stockpiling opportunities. A production system model is built based on a real construction manufacturing factory to simulate real-world precast reinforced concrete component fabrication, and acts as the basis for the development and validation of the control system. A review of alternative decision-making techniques is presented to identify the most suitable for the control of construction manufacturing factories. Ultimately, an artificial neural network approach trained using a reinforcement learning strategy is selected as a promising technique for effective real-time control. The controller is developed and validated, and its performance is optimized using sensitivity analysis, which takes into account both the structure of the artificial neural network and the parameters of the reinforcement learning algorithm. The ANN-based control policy is applied to the sequencing of precast reinforced concrete component production, while a rule-of-thumb policy is used as a benchmark for comparison. The study demonstrates that the optimized ANN-based control policy significantly outperforms the standard rule-of-thumb policy. The paper concludes by providing suggestions for further advancement of the ANN-based approach and potential avenues to increase the control policy's scope of application in construction manufacturing.

Experimental Performance Evaluation and Optimization of a Weak Gel Deep-Profile Control System for Sandstone Reservoirs.

Globally, most oil fields use excessive water flooding to recover oil. By injection of water between wells, channels are created, which result in lower oil recovery. Water-plugging deep-profile control must be used to control the excessive water production from an oil reservoir. This laboratory study used sulfonated polyacrylamides with a molecular weight of 10.3-13.0 × 106 Da (FPAM) and polyethylenimine (PEI-600) to formulate a weak gel system to control excessive water production from deep formations. Using different FPAM and PEI-600 concentrations, the Sydansk bottle test approach was applied to evaluate the gelation time, strength, and stability of the weak gel. The weak gel concentration of 0.5 wt % FPAM and 0.4 wt % PEI-600 was confirmed for deep-profile control by this approach. The temperature and salt resistance of the selected weak gel system were evaluated using the same bottle test methodology. The gelation time depends on temperature: 5-7 days at <100 °C to 0.5 days >100 °C. Salinity >20,000 mg/L significantly affected the weak gel system's strength. By performing a viscometer test, the viscosity of the weak gel system at different times was evaluated, confirming the gelation time of the selected weak gel. Next, a microfluidic chip flooding test analyzed weak gel performance and plugging ability in porous media. This micromodel provided a visual analysis of the weak gel plug. Finally, a low- to medium-permeability sandstone core-flooding was conducted to determine the plugging rate of weak gel at the core scale, followed by an evaluation of the injection pressure, blocking effect, and oil recovery. According to the study, the selected weak gel has an extended gelation time with a significantly low viscosity, which affects its injectivity and can move from injection wells into deep formations. In the core-flooding test, the weak gel's blocking rate after 7 days of gelation time exceeded 90%.

Parameter Optimization of Steam Generator Water Level Control System Based on Piecewise ARX Modeling

Parameter Optimization of Steam Generator Water Level Control System Based on Piecewise ARX Modeling

Improvement and optimization of spacecraft environmental control and life support systems

Environmental control and life support systems (ECLSS) are essential for the triumph of human spaceflight missions, furnishing astronauts with crucial resources like breathable air, purified water, nourishment, and protection from radiation. The unique and challenging space environment, coupled with the critical nature of ECLSS components, necessitates a high degree of reliability to prevent catastrophic failures. This paper conducts a comprehensive examination of various ECLSS subsystems, including air revitalization, water processing, food storage, waste management, and radiation shielding. Gaining perspectives from historical missions like Apollo, Skylab, and the International Space Station (ISS), the research outlines strategic approaches to improve the fault tolerance of ECLSS. The implementation of advanced simulation modeling, strategic component redundancy, and improved subsystem interconnectivity are posited as pivotal measures to bolster the reliability of ECLSS. These improvements are vital to guarantee the safety and viability of prolonged space missions to the Moon, Mars, and beyond, thereby facilitating humanitys ongoing exploration of the universe.