Дата публикации: 13.10.2024
SYNTHESIS AND OPTIMIZATION OF THE DIGITAL CONTROL SYSTEM FOR THE ELECTRIC DRIVE OF THE OZONATOR DEVICE
Abydakayrov A.A., Professor, Satbayev University (KazNITU named after K.I. Satpayev);
Abatov M.K., Master's Student, AUES named after Daukeyev;
Abdullayev М.А., Senior Lecturer, Satbayev University (KazNITU named after K.I. Satpayev);
Ermanova D.R., Doctoral Student, Satbayev University (KazNITU named after K.I. Satpayev);
Akylzhan P. Doctoral Student, Satbayev University (KazNITU named after K.I. Satpayev).
Abstract: This article addresses the issues of synthesizing and optimizing the digital control system for the electric drive of the ozonator device. During the research, digital control algorithms based on a PID controller were applied, which improved the dynamic characteristics of the system. Optimization methods were introduced to accelerate the transient process and enhance energy efficiency. As a result, the control accuracy and stability of the ozonator device were improved. This approach contributes to increasing the effectiveness of ozonator device management and their widespread application in industrial processes.
Keywords: digital control, optimization, PID controller, electric drive, ozonator device.
1. Introduction
In modern industrial processes, environmental protection and energy efficiency enhancement are among the key challenges [1,2]. In this context, ozonator devices are widely used for effective disinfection of water, air, and various surfaces. The high oxidative capacity of ozone makes it an indispensable tool in eliminating bacteria and viruses, highlighting it as an environmentally friendly solution [3,4,5].
However, to improve the efficiency of ozonator devices, there is a need to optimize and synthesize their electric drive control systems [6]. Utilizing the capabilities of digital control systems is crucial in this regard. Digital control systems not only enhance accuracy and reliability but also allow for automatic adjustment of the device's operating modes, thereby reducing energy consumption and increasing the efficiency of ozone production.
This research focuses on the synthesis and optimization of the digital control system for the electric drive of the ozonator device. Specifically, the structure of the system, control algorithms, and methods for their implementation are analyzed. The results of this study provide concrete recommendations aimed at improving the efficiency of ozonator operations.
2. Materials and Methods
This study focuses on the synthesis and optimization of the digital control system for the electric drive of the ozonator device. The following materials and methods were utilized in the research.
2.1 Design and Optimization of the Digital Control System for the Electric Drive of the Ozonator Device
Research on the synthesis and optimization of the digital control system for the electric drive of the ozonator device has gained significant importance in recent years [7]. The main objectives of these studies are to effectively control the operation of the ozonator device, reduce energy consumption, and improve the quality of the ozone production process. Initially, the principles of ozonator operation and the characteristics of the electric drive system were analyzed. Since the electric drive system is a key component of ozone generation, its stability and efficiency directly impact the overall productivity of ozone production. Therefore, a mathematical model of the system was developed to ensure its efficient operation.
To enhance the efficiency of the control system, digital control algorithms were investigated. The MATLAB/Simulink software environment was used to synthesize the algorithms and evaluate their effectiveness. The researchers improved the stability of the system by implementing PID controllers and adaptive control methods. These algorithms were tested in various operating modes, and their effectiveness was demonstrated.
One of the crucial stages of the research involved experimental tests of the ozonator device. These tests allowed for the evaluation of the algorithms' effectiveness under real operating conditions. The researchers achieved positive results in improving ozone production efficiency and reducing energy consumption. The experimental data were compared with the simulation results, and the system's performance and reliability were assessed. Additionally, optimization issues related to the system were addressed during the study. During the optimization process, the operating parameters of the electric drive were adjusted to their most efficient levels, enhancing the quality of ozone production. Various sensors, including current, voltage, and temperature sensors, were used to manage the system's operation. These sensors enabled real-time measurement of the system's operating parameters.
The research results highlighted the advantages of the digital control system for the ozonator device. The system demonstrated high accuracy and reliability, proving its applicability in real industrial conditions. Future plans include further enhancement of the system through the implementation of intelligent control methods and the introduction of new algorithms to further improve the efficiency of ozone production.
2.2 Research Methods. The following methods are effective for synthesizing and optimizing the digital control system for the electric drive of the ozonator device (Table 1):
Table 1. Scientific Justification of Methods for Synthesizing and Optimizing the Digital Control System for the Electric Drive
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№
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Methods for Synthesizing and Optimizing the Digital Control System for the Electric Drive
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Scientific Justification of the Method
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1
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Mathematical Modeling
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By creating a mathematical model of the electric drive system, it is possible to gain a deeper understanding of its operating principles and dynamics. This model can be used as a foundation for synthesizing and optimizing the control system.
|
|
2
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Digital Control Algorithms
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PID controllers (proportional-integral-derivative regulators) and adaptive control algorithms enable precise control of the ozonator device's operation. These methods help monitor and optimize the system's static and dynamic parameters.
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|
3
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Modeling and Simulation
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Modeling and simulating the system's operation in MATLAB/Simulink or other software environments. This method allows for the preliminary evaluation of the effectiveness of control algorithms and their refinement before applying them in real-world scenarios.
|
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4
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Optimization Methods
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By applying optimization algorithms such as genetic algorithms, particle swarm optimization (PSO), or gradient descent, it is possible to improve the operating parameters of the system. These methods are aimed at enhancing system performance and reducing energy consumption.
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5
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Experimental Research
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To verify how the system operates under actual industrial conditions, it is necessary to conduct experimental trials. The data obtained during these trials will be used to further refine the control system.
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6
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Sensor Data Collection and Analysis
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To monitor the operating parameters of the ozonator in real-time, various sensors (such as current, voltage, temperature, etc.) are used. Analyzing the collected data helps identify and address any faults in the system's operation.
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7
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Robust Control Methods
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To ensure system stability and enhance its ability to adapt to external disturbances, robust control methods are employed. These methods increase the reliability of the ozonator device's operation.
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8
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Artificial Intelligence and Machine Learning
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Implementing artificial intelligence and machine learning methods to predict system performance and enhance efficiency. These methods enable intelligent control of the system, such as making forecasts or automatically adapting to changing conditions.
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9
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System Identification
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System identification methods are used to determine the dynamics and parameters of the system based on real data. This helps in the accurate synthesis and optimization of the control system.
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10
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Automated Control Systems
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By automating the system, its operation is optimized, leading to increased efficiency of the ozonator device and a reduction in human errors.
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The methods presented in Table 1 assist in effectively synthesizing and optimizing the digital control system for the electric drive of the ozonator device, thereby enabling enhanced performance and reduced energy consumption. However, throughout the research, special attention was given to mathematical modeling for synthesizing and optimizing the digital control system of the ozonator device's electric drive.
3. Results and Discussions
To create a mathematical model for synthesizing and optimizing the digital control system of the ozonator device's electric drive, the following key elements need to be considered. The input and output variables of the device are: u(t) - control signal (input), y(t) - output signal of the ozonator, e(t) = r(t) − y(t) - error signal, where r(t) is the desired (reference) value. The structural diagram of the ozonator device's control system is presented below in Figure 1.
Figure 1. Structural Diagram of the Ozonator Device's Control System
The necessary equations for creating the model are as follows:
Dynamics of the Electric Drive System:
A simple first-order inertial model can be used to describe the dynamics of the electric drive system (Figure 2):
(1)Where: Tm is the time constant of the electric drive, and Km is the amplification coefficient.
Figure 2. Dynamic Response of the Ozonator Drive System
This graph illustrates the dynamic response of the ozonator device's electric drive system. Initially, the system starts from zero and gradually increases its output value over time. The graph describes an exponential growth, meaning the system stabilizes and reaches a set value after a certain period. This dynamic reflects the inertial nature of the system, indicating that it does not respond immediately to input stimuli but adapts to them over time. The system is stable because it remains in a steady state as time progresses.
However, in some cases, the digital control system can be described in discrete time. If we use the sampling period Ts, the control system can be represented as follows:
(2)Where: u[n] is the control signal at the n - th discrete time; Kp, Ki, Kd are the proportional, integral, and derivative coefficients, respectively.
And when the complete system is given with feedback:
(3)Optimization Criteria for the Model:
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Transient Process: The system's temporal characteristics (e.g., rise time, settling time, overshoot).
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System Stability: Ensured through the analysis of amplitude-phase frequency characteristics.
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Energy Efficiency: Minimizing power consumption.
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Digital Simulation: The model's parameters can be analyzed and optimized using simulation software like Python, MATLAB, Simulink, or others. In this research, we conducted the study using Python.
Optimization Methods: 1) Gradient Descent - a method for minimizing or maximizing parameters; 2) Genetic Algorithms - used to find the best results.
Thus, by synthesizing the mathematical model of the ozonator device's digital control system for the electric drive, its parameters can be optimized. This model can be used for digital simulation and application in real physical systems.
Analysis of the Research Work: In the scientific research, the structural diagram of the ozonator device's control system was identified. The diagram shows the system's input signal u(t)u(t)u(t) and output signal y(t). Within the system, there is an ozone production process and a feedback mechanism that calculates the error signal e(t). This feedback system is used to detect the control signal error and adjust the system's response accordingly. The diagram visually explains how the overall control system works and how its components interact with each other.
Conclusion
This study was dedicated to the synthesis and optimization of the digital control system for the electric drive of the ozonator device. The analyses and simulation results demonstrated the importance of applying digital control algorithms to enhance the system's efficiency and stability. The digital control system, based on the PID controller, improved the dynamic characteristics of the ozonator device and increased control accuracy. Additionally, optimization methods allowed for the acceleration of the transient process and ensured energy efficiency. Thus, the research results contribute to the advancement of digital control systems for ozonator devices and their broader application in industrial processes. Future work should include further refinement of these methods and additional research to test their application under real-world conditions.
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