International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 04 | Apr 2024
p-ISSN: 2395-0072
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DESIGN OF LEVEL CONTROLLER FOR A NON-LINEAR SYSTEM USING MACHINE LEARNING Athappan V1, Ikshvathith K S2, Rahul B3, Thirunilavan T4 1Faculty 2,3,4Student, Department of Electronics and Instrumentation Engineering, Kumaraguru College of
Technology, Coimbatore, Tamil Nadu, India. ---------------------------------------------------------------------***--------------------------------------------------------------------Abstract – This study explores machine learning-based engineering uses, including as machine learning, signal
processing, and control system design. The development, simulation, and analysis of algorithms pertaining to the design and implementation of a control system for a nonlinear spherical tank were carried out in this study using MATLAB. The study process was made more productive and efficient using MATLAB, which allowed for quick prototyping and iterative control algorithm improvement.
control for nonlinear spherical tank level management, addressing challenges in traditional methods. Leveraging simulated annealing, known for global optimization and robustness in noisy environments, PI controller parameters are optimized for enhanced accuracy and stability. The research aims to fill the gap in applying simulated annealing with linear regression machine learning model to this domain, providing a more effective and accurate solution for industrial processes. Implemented in MATLAB, simulated annealing demonstrates its superiority by globally optimizing PI parameters while offering simplicity in implementation. This research underscores the significance of leveraging simulated annealing for fine-tuning PI parameters with machine learning, offering promising avenues for efficient and reliable level control strategies.
DAQ: An ATmega2560 microcontroller-based Data Acquisition (DAQ) system was incorporated into the experimental configuration to facilitate communication with tangible sensors and actuators. The ATmega2560, when used in conjunction with MATLAB, enabled real-time data acquisition and control activities. It was equipped with several analog-to-digital converter (ADC) channels, digital I/O ports, and communication interfaces like UART and I2C. The DAQ system made it possible to collect sensor data from the actual system, which was then used as input for MATLAB's control algorithms and to actuate the actuators in the system using control signals. Validation and optimisation of control techniques for the nonlinear spherical tank system were made easier by the smooth transition from simulation to real-world experimentation made possible by the integration of MATLAB with the DAQ system contained with the ATmega2560 microcontroller.
Key Words: Nonlinear spherical tank, PI, Global optimization, MATLAB, annealing, linear regression.
1. INTRODUCTION For industrial processes to be efficient and safe, liquid levels must be controlled, particularly in spherical tanks. Conventional techniques have difficulty with the changing conditions and the intrinsic nonlinear dynamics. There is potential for increased control accuracy using machine learning.
DPT (level transmitter) :This study's differential pressure transmitter is an Allen Bradley precision instrument made especially for process control and monitoring applications. With a 24 V DC input voltage, this type can be used with conventional industrial power sources and control systems. The transmitter output provides a linear signal in relation to the observed pressure differential across its input ports and is arranged in the industry-standard 4-20 mA current loop format. Strong, noise-resistant transmission of measurement data over extended distances is made possible by the 4–20 mA signal range, which makes it ideal for the harsh conditions frequently found in industrial applications. Modern sensor technology is used into the design of this transmitter to provide high differential pressure measuring accuracy, stability, and repeatability.
To close this gap, this study incorporates simulated annealing—a technique that allows one to explore the whole solution space—with machine learning into a machine learning-based nonlinear tank level control controller. By comparing test data with trained data, it seeks to offer an industrial process solution that is more effective.
2. MATERIALS AND METHODS 2.1 MATERIAL DESCRIPTION MATLAB: This study makes considerable use of MATLAB, a proprietary programming environment created by MathWorks. A whole range of tools for numerical computing, developing algorithms, analysing data, and visualising it are provided by MATLAB. Because of its adaptability, it is ideal for a variety of scientific and
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CONTROL VALVE: The control valve we describe in our study has an input current range of 4–20 mA and is
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