Design and implementation of an optimal fuzzy logic controller using genetic algorithm

All control systems suffer from problems related to undesirable overshoot, longer settling times and vibrations while going form one state to another state. Most of relevant techniques had been in the form of suggesting modification and improvement in the instrumentation or interfacing part of the control system and the results reported, remain suffering from shortcomings related to hardware parameter dependence and maintenance and operational complexities. Present study was based on a software approach which was focusing on an algorithmic approach for programming a PIC16F877A microcontroller, for eliminating altogether the parametric dependence issues while adding the benefits of easier modification to suit a given control system to changing operational conditions. Said approach was first simulated using MATLAB/SIMULINK using the techniques of Proportional Derivative Fuzzy Logic Controller (PD-FLC) whose membership function, fuzzy logic rules and scaling gains were optimized by the genetic algorithm technique. Simulated results were verified by programming the PIC16F877A microcontroller with the algorithm and using it on a temperature control system where a fan was regulated in response to variations in the ambient system temperature. Resulting tabulated performance indices showed a considerable improvement in rising and settling time besides reducing overshoot and steady state error

A new approach to optimize the energy efficiency of CO2 transcritical refrigeration plants

This paper proposes a model-free real-time optimization and control strategy for CO2 transcritical refrigeration plants that assures covering the cooling demand and continuous tracking of conditions for maximum efficiency. Our approach obtains the feedback with only three measurements, and controls the opening degree of a backpressure valve and the speed of the compressor. The strategy minimizes the power consumption of the compressor instead of maximizing the coefficient of performance, which avoids several sensors, and we demonstrate mathematically that both approaches are equivalent. We implemented the strategy with an algorithm that includes two independent auto tuned controllers, one devoted to regulate the high-pressure and another to regulate the outlet temperature of the secondary fluid of the evaporator. It also incorporates a real time perturb and observe procedure to locate online the optimum high-pressure that minimizes the compressor power consumption. The paper presents the experimental evaluation of the control strategy, verifying the stable operation of the algorithm and the energy optimization of the plant

Implementing PID Control on Arduino Uno for Air Temperature Optimization

This research investigates the precise regulation of liquid filling in tanks, specifically focusing on water storage systems. It employs the Proportional-Integral-Derivative (PID) control method in conjunction with an HC-SR04 ultrasonic sensor and an Arduino Uno microcontroller. Given the paramount importance of water as a resource, accurate management of its storage is of utmost significance. The PID control method, known for its rapid responsiveness, minimal overshoot, and robust stability, effectively facilitates this task. Integrating the ultrasonic sensor and microcontroller further augments the precision of water level regulation. The article expounds upon the foundational principles of the PID control method and elucidates its application in the context of liquid tank filling. It offers a comprehensive insight into the hardware configuration, encompassing pivotal components such as the Arduino Uno microcontroller, HC-SR04 ultrasonic sensor, and the L298 driver responsible for water pump control. The experimental approach is meticulous, presenting results from tests involving the Proportional Controller, Proportional Integral (PI) Controller, and Proportional Integral Derivative (PID) Controller. These tests rigorously analyze the impact of varying Proportional Gain (Kp), Integral Gain (Ki), and Derivative Gain (Kd) parameters on crucial performance metrics such as response time, overshoot, and steady-state error. The findings underscore the critical importance of an optimal parameter configuration, emphasizing the delicate equilibrium between response speed, precision, and error minimization. This research significantly advances PID control implementation in liquid tank filling, offering insights that pave the way for developing more efficient liquid management systems across various sectors. The identified optimal parameter configuration is Kp = 5.0, Ki = 0.3, and Kd = 0.2

Control of DC Motor Using Proportional Integral Derivative (PID): Arduino Hardware Implementation

The research proposes controlling DC motor angular speed using the Proportional Integral Derivative (PID) controller and hardware implementation using a microcontroller. The microcontroller device is Arduino Uno as data processing, the encoder sensor is to calculate the angular speed, and the motor driver is L298. Based on the hardware implementation, the proportional controller affects the rise time, overshoot, and steady-state error. The integral controller affects overshoot and undershoot. The derivative controller affects overshoot insignificantly. The best parameter PID is Kp=1, Ki=0.3, and Kd=0.1 with system response characteristic without overshoot and undershoot. Using various set point values, the controller can make the DC motor reach the reference signal. Thus, the PID controller can control, handle, and stabilize the DC motor system

“Ball and beam” virtual laboratory: a teaching aid in automatic control courses

This paper describes a humanoid robot simulator with realistic dynamics. As simulation is a powerful tool for speeding up the control software development, the suggested accurate simulator allows to accomplish this goal. The simulator, based on the Open Dynamics Engine and GLScene graphics library, provides instant visual feedback and allows the user to test any control strategy without damaging the real robot in the early stages of the development. The proposed simulator also captures some characteristics of the environment that are important and allows to test controllers without access to the real hardware. Experimental results are shown that validate this approach

Adaptive P Control and Adaptive Fuzzy Logic Controller with Expert System Implementation for Robotic Manipulator Application

This study aims to develop an expert system implementation of P controller and fuzzy logic controller to address issues related to improper control input estimation, which can arise from incorrect gain values or unsuitable rule-based designs. The research focuses on improving the control input adaptation by using an expert system to resolve the adjustment issues of the P controller and fuzzy logic controller. The methodology involves designing an expert system that captures error signals within the system and adjusts the gain to enhance the control input estimation from the main controller. In this study, the P controller and fuzzy logic controller were regulated, and the system was tested using step input signals with small values and larger than the saturation limit defined in the design. The PID controller used CHR tuning to least overshoot, determining the system's gain. The tests were conducted using different step input values and saturation limits, providing a comprehensive analysis of the controller's performance. The results demonstrated that the adaptive fuzzy logic controller performed well in terms of %OS and settling time values in system control, followed by the fuzzy logic controller, adaptive P controller, and P controller. The adaptive P controller showed similar control capabilities during input saturation, as long as it did not exceed 100% of the designed rule base. The study emphasizes the importance of incorporating expert systems into control input estimation in the main controller to enhance the system efficiency compared to the original system, and further improvements can be achieved if the main processing system already possesses adequate control ability. This research contributes to the development of more intelligent control systems by integrating expert systems with P controllers and fuzzy logic controllers, addressing the limitations of traditional control systems and improving their overall performance

Remote laboratory to support control theory

The Control Systems plays a vital role in the industry, which is the most essential application of the Electrical Engineering. The control concepts are present in most of the automation systems. The Control Systems theory is the key concept to achieve the automation and makes world faster. But, in reality the study of control engineering is decreased in the recent years, because of the difficulty in learning the concepts of the control theory. Most of the students feel difficult to understand theoretical concepts of control systems. The traditional teaching methodology is one way of teaching control systems concepts. Even though books are proper way of teaching control systems in a systematic way, we need additional tool to create interaction between the subject and the students. The teaching platform is worth to analyse the possibility to add or complement the way of standing with means able to add Real evidences. In another way, it is important that the provided lab experiment should be affordable. The teaching platform to support control theory has been introduced with set of experiments to create Real evidences, and manuals to carry out those experiments, slides to have a guidance and Graphical User Interface (GUI) to have an interaction with the control system is provided