Proportional Integral Derivative Control System Research Articles

Design and Experimental Study of an Embedded Controller for a Model-Based Controllable Pitch Propeller

The controllable pitch propeller hydraulic system has high constraints and nonlinearity. Due to these inherent deficiencies, the proportional–integral–derivative (PID) control algorithm cannot meet the control accuracy requirements of nonlinear systems. A control law based on a model predictive control (MPC) algorithm is designed in this paper. The gain parameters of the predictive control are optimized. The MPC and PID control systems are compared and simulated to verify the MPC controller’s effectiveness. Subsequently, the embedded controller of a controllable pitch propeller is developed. The support package for the embedded circuit board target containing an underlying driver for each interface is written by introducing the C-MEX S-Function and TLC programming language. A semi-physical simulation experiment is performed. The results show that the established controllable pitch propeller with an embedded controller displays reliable running performance, good anti-interference, and the capacity to fulfill the control function of the pitch propeller under various working conditions.

Development of an IoT-Based Egg Incubator with PID Control System and Web Application

The rapid development of technology significantly impacts various aspects of life, including the field of livestock farming. The advancement of technology is expected to enhance the rate and effectiveness of production, particularly in the hatching of chicken eggs or chick breeding. The existing technology relies on manual on/off systems and manual monitoring, hindering successful egg-hatching rates and percentages. Therefore, this research aims to explain the development of an automated egg incubator using a Proportional Integral Derivative (PID) control system with hypertuning parameters, as well as temperature and humidity monitoring, along with a protection system based on voltage sensors, all integrated with the Internet of Things (IoT). The PID control is employed to regulate the temperature of the egg incubator, ensuring stability according to the predetermined set point temperature. The IoT system in this study comprises an ESP32 node as a microcontroller connected to a sensor, using Firebase and User app for monitoring the egg incubator. The study employed PID control with parameter values Kp=10, Ki=3, and Kd=8. The research yielded time-efficient egg incubation and prevention of turning delays. The DHT21 sensor achieved a 90% success rate in detecting room temperature (38°C) and humidity (77%-84%) within the incubator, while PID control effectively maintained the target temperature. The ACS712 sensor accurately detected current in the heater, power supply, and motor. The Kodular application can display sensor readings. The future implication is developing a more adaptive PID method toward changes and nonlinear dynamics.

BLDC Motor Stability Management Using Adaptive PID (MRAC-PID)

BLDC motors have become popular in various industries such as automotive, consumer, healthcare, industrial automation, and instrumentation due to their optimal performance. To keep the BLDC motor in optimal condition, a control engineering system is required that serves as a controller. A single Proportional Integral Derivative (PID) control system is only suitable for linear conditions, so it cannot produce satisfactory output when there is a change in set point. To overcome this obstacle, an adaptive PID control system known as MRAC-PID control system is applied, which is able to control the stability of the BLDC motor as desired. Testing of this system is done under 4 different conditions using MATLAB software. After testing, the parameter values for the MRAC control system were obtained, namely ???????? = 0.4; ???????? = 50.75; ???????? = 0.0000867. Based on the test, the MRAC control system produces a system success rate of 81.2% to 98.9%.

An Adaptive PID Control System for the Attitude and Altitude Control of a Quadcopter

Abstract In adaptive model-based control systems, determining the appropriate controller gain is a complex and time-consuming task due to noise and external disturbances. Changes in the controller parameters were assumed to be dependent on the quadcopter mass, which was the process variable. A nonlinear model of the plant was used to identify the mass, employing the weighted recursive least squares (WRLS) method for online identification. The identification and control processes involved filtration using differential filters, which provided appropriate derivatives of signals. Proportional integral derivative (PID) controller tuning was performed using the Gauss–Newton optimisation procedure on the plant. Differential filters played a crucial role in all the developed control systems by significantly reducing measurement noise. The results showed that the performance of classical PID controllers can be improved by using differential filters and gain scheduling. The control and identification algorithms were implemented in an National Instruments (NI) myRIO-1900 controller. The nonlinear model of the plant was built based on Newton’s equations.

Integrated Room Monitoring and Air Conditioning Efficiency Optimization Using ESP-12E Based Sensors and PID Control Automation: A Comprehensive Approach

This study addresses the critical need for efficient room monitoring and air conditioning systems, particularly in educational settings like the STMIK STIKOM Indonesia campus. The paper introduces a novel approach that combines ESP-12E based sensors with Proportional-Integral-Derivative (PID) control automation to optimize air conditioning efficiency. Utilizing an ESP-12E microcontroller, the study designed and implemented a room monitoring tool equipped with DHT22 and BH1750 sensors for accurate measurement of temperature, humidity, and light intensity. We also explores the integration of a PID control system into an existing air conditioning (AC) unit. The PID controller was fine-tuned to maintain a stable indoor temperature of 25oCelsius, even when subjected to external heat loads, such as ten LED lamps. The effectiveness of this system was quantified through real-time monitoring of temperature, humidity, and energy consumption, both pre- and post-implementation. Results indicated a rapid and stable response from the PID controller, achieving an amplitude of 1 within 0.08 seconds, thereby confirming its successful tuning and adaptability. We found that this study has broader implications for enhancing energy efficiency and creating conducive learning environments. However, it is worth noting that the research was conducted under specific conditions, and further studies could explore its applicability in different settings.

A flight control system for quadrotor plant protection UAV based on RBF-PID algorithm

As a powerful tool for modern agricultural protection, unmanned aerial vehicle (UAV) has the advantages of good adaptability and high efficiency in spraying and other operations compared with traditional manual operations The attitude and control of plant protection UAV are greatly affected by external factors during operation. However, the traditional proportional-integral-derivative (PID) controller commonly used at present cannot adapt to the complex environment because of the fixed parametersTherefore, a more sensitive method is needed to achieve adaptive balance of the aircraft attitude. In this paper, we establish the dynamic mathematical model of quadrotor UAV, and the influence of air resistance and rotational torque is considered in the process. Adopting the combined control method of Radial Basis Function (RBF) neural network and PID, the dynamic tuning of system control parameters is achieved through the self-learning of neural network and the feature of nonlinear mapping. The control system not only has the advantages of simple PID control structure and clear physical meaning, but also has the ability of neural network self-learning and adaptation. Matlab/Simulink was used as the experimental platform to respectively simulate the RBF-PID control system and the traditional PID control system. The simulation results show that the control system based on RBF-PID algorithm has a shorter response time and stronger robustness, which enhances the systems self-adaptability

Desain Mobile Robot dengan Differential Steering untuk Penyemprot Nutrisi Tanaman Melon di Greenhouse

Maintenance of melon cultivation is still carried out conventionally even though melon plants require different nutrients in each phase of vegetative and generative development. For this reason, an automation system is needed that can help distribute nutrients and pH in each planter bag. In this study, the automation system is realized in the form of a line follower robot using a PID (Proportional Integral Derivative) control system. The PID control system is used to correct errors from measuring input variables so that the system output matches the set point value to produce the smallest possible error. Based on the results of the experiments conducted, the best PID control parameter results were obtained with a value of Kp = 10, Ki = 0.5, and Kd = 1, where these values can respond to the position of the mobile robot line follower so that it always detects the set point value and has the fastest time to reach the finish with a time of 30,450 ms.

FusionCPP: Cooperative fusion of infrared and visible light images based on PCNN and PID control systems

The imaging results of infrared and visible light sensors are highly complementary. Thus, combining the two images can compensate for the shortcomings of a single sensor. However, previous fusion methods have not fully utilized the collaborative guidance of infrared and visible images in the fusion process, leading to a poor correlation between extracted features and cannot guarantee feature fusion accuracy. In order to address these challenges, this paper proposes a collaborative fusion method for infrared and visible images based on pulse-coupled neural networks (PCNN) and proportional-integral-derivative (PID) control systems, called FusionCPP. First, a coupled neural network with a dual pulse structure is designed for the feature extraction section. Unlike traditional PCNN, this network includes two pulse generators, one of which is utilized to extract the salient features of the image, while the other is employed to extract the common pulse layer of the source image and multiply the pulse layer by the coupling iteration value to obtain the base layer. Second, a detail layer is obtained by subtracting the base layer from the source image. This method accurately extracts salient and detailed features based on the coupling between source images. Finally, a closed-loop PID control system is utilized as the fusion strategy in the image fusion task, which judges the difference between the fusion result and the source image in real-time through the feedback effect of the system. The controller adaptively adjusts the fusion weight based on the difference to fuse image features into the new image accurately. Experimental results indicate that the proposed FusionCPP can maintain significant contrast and rich textures. Besides, the proposed FusionCPP has extended to multi-focus image fusion and target detection tasks to verify its effectiveness.

BEM Turbine Model and PID Control System of a Floating Hybrid Wind and Current Turbines Integrated Generator System

This is a new installment in the series of publications that describe the mathematical modeling of the Floating Hybrid Generator Systems Simulator (FHYGSYS) tool. This work presents an improved mathematical model of the turbines of the floating hybrid system—consisting of an “OC3-Hywind” wind turbine and two marine current turbines—presented by the authors in previous publications. In this third installment, the modeling of the three turbines of the floating hybrid system is described using the Blade Element Momentum (BEM) theory. This modeling allows one to replace the one based on the One-Dimensional theory used in previous installments. For the operation of modeling with BEM, it has been considered necessary to implement a continuous feedback control system. In this case, two PID (proportional–integral–derivative) controllers have been implemented in each of the turbines. The first controls the torque on the turbine generator and the second controls the collective pitch angle of the blades. The results obtained are presented and validated through a code-to-code comparison with simulations carried out with FASTv8 under the same conditions and with the operating results of marine current turbines that exist in the literature. This improvement in the mathematical model offers the possibility of implementing other types of controllers that allow for the testing of different strategies of the floating hybrid control system, with the aim of maximizing energy production while ensuring the structural stability of the floating hybrid system.

Fuzzy PID control system optimization and verification for oxygen-supplying management in live fish waterless transportation

Live fish waterless transportation could be recognized as an essential supplement for water-based transportation due to its low oxygen consumption and less waste water pollution. The critical problem to maintaining the fish survival quality under such a unique transport strategy is accurately controlling the oxygen concentration in the container to be constantly at stable and high levels. This paper aims to propose an improved fuzzy PID control system based on the grey model with residual rectification by improved particle swarm optimized Gated Recurrent Unit (GM-IPSO-GRU) to realize advanced oxygen level control. In addition, it is also reinforced by adopting the improved grey wolf optimization (IGWO) for the majorization of control parameters (quantization factors, scale factors) with full consideration of fish size features. In this study, Turbot (Scophthalmus maximus) is taken as the test subject to verify the integrated control performance of the optimized fuzzy PID controller through simulated waterless live transportation under low-temperature conditions. The proposed control system is validated as more efficient than the traditional proportional integral derivative (PID) and fuzzy PID algorithms for handling its nonlinear, time-varying, and time lag problems well. In summary, the control group experiment shows that the newly-designed control system has the advantages of shorter stabilization time, minor overshoot, and strong anti-interference ability for oxygen level adjustment. Finally, applying this novel control technology can effectively improve oxygen adjustment efficiency and provide feasible quality control support for the deep optimization of the live fish circulation industry.

Fuzzy proportional–integral–derivative control system of electric drive downhole cutting tool based on genetic algorithm

An electric drive downhole tubing cutting tool is the key equipment for tubing cutting in pressurized gas well workover operation. Cutting force control in radial feed movement of the cutting blade is the key to ensuring the successful cutting of tubing. In this article, the relationship model between cutting blade feed speed and cutting force is established by Matlab. Based on the mathematical model of cutting head rotation and blade feed movement, combined with the relationship between blade feed and cutting force obtained from cutting experiments. To keep the cutting force of the blade constant in the cutting process. Genetic algorithm was used to optimize the fuzzy proportional–integral–derivative control, and the automatic control scheme of the electric drive downhole tubing cutting tool was constructed. Based on Matlab/Simulink, the cutting force control simulation of the tubing cutting process is carried out. The performance of fuzzy proportional–integral–derivative control optimized by the genetic algorithm is compared with that of other standard controllers. The results show that the response speed and stability of the fuzzy proportional–integral–derivative control system are better than other algorithms. The steady-state error of the controller in the steady-cutting stage is < 5% based on the ground-cutting experiment of tubing, which meets the actual engineering needs and verifies the reliability and accuracy of the control system.

DC Motor Speed Control with Proportional Integral Derivative (PID) Control on the Prototype of a Mini-Submarine

DC (Direct Current) motors are widely used as controllers in the industrial and robotics fields. In the rotation of the DC motor, there is still an unstable rotational speed so that a controller is needed to be able to stabilize the speed at the rotation of the DC motor. The control used in this study used the PID (Proportional Integral Derivative) control method. The PID control system works by processing calculations based on control variables Kp, Ki, and Kd to achieve conditions according to the expected setpoints. To achieve the expected conditions, the trial and error method is used. PID control in this study was implemented on a DC motor with a brushed type using an Arduino Mega microcontroller. The speed of the DC motor is read by the encoder sensor and entered in the PID equation. The output of the PID value will produce data in the form of PWM (Pulse Width Modulation) which will be the input of the L298N driver via Arduino Mega. The DC motor will produce a rotational speed in the form of RPM (Revolution Per Minute) data up to the specified set point. The implementation of PID was produced by giving parameter values to Kp, Ki, and Kd. The best PID parameter usage in this study was in the form of Kp = 0.6; Ki=0.3; and Kd=0.01. The application of the PID parameter obtains a stable system response curve at a predetermined set point. The resulting Kp, Ki, and Kd parameter data is used as graph data in MATLAB software.

Comparison of Machine Learning Algorithms for DC Motor PID Control with Genetic Algorithm

Proportional-Integral-Derivative (PID) is a closed loop control system based on dynamic system feedback commonly employed in industrial control systems that require continuous modulated control. The purpose of this project was to improve the efficacy of PID control, a branch of classical control theory, to allow for the application of PID controllers in more complex and non-linear environments. The research compares the results of different machine learning algorithms with genetic algorithms to optimize PID control parameters to achieve more precise control of 12V DC motors. Genetic Algorithm (GA) is a metaheuristic algorithm used to solve search and optimization problems in machine learning. GAs are developed based on the process of natural selection and genetics, relying on biologically inspired operators. After a series of selections and crossovers, the genetic algorithm selected the fittest generation of PID constants that would result in the optimal PID controller which minimizes system error value and linearizes the system behavior. Optimizing PID parameters to enhance the efficacy of classical control theory could increase machining precision in industrial production and improve the robustness of integrated PID controllers in complex environments such as the human body, enabling PID controllers to be an adaptive, simple, and viable option to be implemented in implantable artificial organs and motorized prosthetics. The results of the study suggest that GA optimized PID parameters are effective in regulating the system behavior of DC motors and the enhanced PID control system could be implemented in a wide range of rapidly changing and non-linear applications.

Research on robotic end positioning based on fuzzy PID control

The existing traditional proportional-integral-derivative (PID) control robot has low-end positioning accuracy, which cannot meet the needs of accurately placing the molding die in the current automatic coal pen production process. To address this problem, this paper proposes a fuzzy strategy PID control method based on fuzzy strategy, aiming to improve the accuracy and robustness of the system when the manipulator grasps the mounding mold. Firstly, a manipulator motor control model is established to derive the parameters of the manipulator control system; secondly, the fuzzy control strategy is applied to adjust the PID control parameters in real time to improve the dynamic stability of the system. Finally, the control system is simulated, and field experiments are conducted to verify the accuracy of robotic end positioning. The experimental results show that the fuzzy PID control system has a shorter response time, better anti-interference ability, and better stability than the traditional PID control and achieves the accuracy requirements of coal pen automation production.

Extra control coefficient additive ECCA-PID for control optimization of electrical and mechanic system

Proportional Integral Derivative (PID) controllers are frequently used control methods for mechanical and electrical systems. Controller values are chosen either by calculation or by experimentation to obtain a satisfactory response in the system and to optimise the response. Sometimes the controller values do not quite capture the desired system response due to incorrect calculations or approximate entered values. In this case, it is necessary to add features that can make a comparison with the existing traditional system and add decision-making features to optimise the response of the system. In this article, the decision-making unit created for these control systems to provide a better control response and the PID system that contributes an extra control coefficient called ECCA-PID is presented. First, the structure and design of the traditional PID control system and the ECCA-PID control system are presented. After that, ECCA-PID and traditional PID methods’ step response of a quadratic systemare examined. The results obtained show the effectiveness of the proposed control method.

LQI Control System Design with GA Approach for Flying-Type Firefighting Robot Using Waterpower and Weight-Shifting Mechanism

This study proposes a flying robot using waterpower and a novel weight-shifting mechanism, whose purpose is to be applied in firefighting tasks in water areas that are difficult to access and are suppressed by conventional firefighting methods. The sufficient amount of water in the area is used for propelling the robot, as well as fire suppression activity. A weight-shifting mechanism governs the weight distribution of the robot head in order to perform the robot motions. In this paper, the system’s dynamical characteristics are analyzed in detail through mathematical models. A linear-quadratic integrator (LQI) is designed for controlling the system motion. Additionally, the LQI is tuned with the genetic algorithm (GA) so that both system performance and robustness are optimally preserved. Simulation studies are carried out, in which the proposed system is compared with a cascade proportional-integral-derivative (PID) control system. The results validate the feasibility of the design and show the superiority of the proposed control system in motion performances. Moreover, the LQI-GA consumes 2.28% less water and uses 83.85% less kinetic energy of the actuator than the PID control system.

Using PCM for building energy management to postpone the electricity demand peak load and approving a new PID controller to activate alternative chiller

In this study, PCM was used to delay the maximum heat transfer and postpone it to a low load time in a building. Hence, a new proportional–integral–derivative (PID) controller system was introduced that could adjust the temperature using six setpoints. To evaluate PCM performance accurately, a 5 cm layer of PCM was added to the wall and instead, the thickness of Thermalite Turbo (which had the highest heat resistance) was reduced by 5 cm to keep the final wall thickness constant. The energy equation for indoor space was approved and by considering the PCM phase change along with radiation effects, the temperature distribution within the wall was obtained. The PCM inside the wall during January–June lessened electricity demand but raised it slightly in the remaining months. However, in the annual analysis, PCM managed to reduce electricity demand by 15.3%. To compensate for poor PCM performance during summer, a PID control system disconnects the main chiller and adds an alternative chiller to the circuit. Using new PID system led to an electricity demand reduction of 2.2%. According to the schedule, electricity consumption decreased by 2.3%.

Integrated Physical Modeling and Optimal Control Method of Limited-Angle Torque Motor in Fuel Metering Apparatus

Limited-angle torque motor (LATM) is a critical component to precisely drive the valve angle of an engine’s fuel metering apparatus and accurately measure the fuel flow, and research on it is of great significance. Thus, the LATM of a certain kind is regarded as the research object in this paper. Firstly, a Simscape-based LATM integrated physical modeling method is proposed, which can better demonstrate the real operational characteristics of a motor, compared with the current mathematical model. Secondly, a Proportional-Integral-Derivative (PID) parameter self-tuning method based on a constriction factor particle swarm optimization (CPSO) algorithm is broached since it is difficult to tune due to a large number of multi-loop cascade PID control parameters. Simulation and experimental results showed that the control performance increases by 40% in the triple closed-loop PID control system with a stronger disturbance rejection, simpler design, and quickly responds when compared with the previous empirical tuning method. The triple closed-loop PID control system comprises an angle loop + angle velocity loop + current loop and technically supports the engineering application design of motors.

Design of a Parallel Quadruped Robot Based on a Novel Intelligent Control System

In order to make a robot track a desired trajectory with high precision and steady gait, a novel intelligent controller was designed based on a new mechanical structure and optimized foot trajectory. Kinematics models in terms of the D-H method were established to analyze the relationship between the angle of the driving joint and the foot position. Inspired by a dog’s diagonal trot on a flat terrain, foot trajectory planning in the swing and support phases without impact were fulfilled based on the compound cycloid improved by the Bézier curve. Both the optimized cascade proportional–integral–derivative (PID) control system and improved fuzzy adaptive PID control system were applied to realize the stable operation of a quadruped robot, and their parameters were optimized by the sparrow search algorithm. The convergence speed and accuracy of the sparrow search algorithm were verified by comparing with the moth flame optimization algorithm and particle swarm optimization algorithm. Finally, a co-simulation with MATLAB and ADAMS was utilized to compare the effects of the two control systems. The results of both displacement and velocity exhibit that the movement of a quadruped bionic robot with fuzzy adaptive PID control systems optimized by the sparrow search algorithm possessed better accuracy and stability than cascade PID control systems. The motion process of the quadruped robot in the co-simulation process also demonstrates the effectiveness of the designed mechanical structure and control system.

2-D Actuator based Shape Memory Alloy using PID controller

Over the past years, researchers have been focusing on development the robotics and actuation due to increase demand for these applications like industrial engineering, oil industry, healthcare, aerospace … etc. This work involves the design, construction and control of the Shape Memory Alloy (SMA) actuator. The industrial actuator has many characteristics able to be measured, which have an impact on the efficiency and effectiveness of the actuator while the execution of its tasks. The most important measurable characteristics are repeatability and accuracy. The current system typically is using Nitinol (Nickle Titanium Naval Ordinance Lab), which is one of the Shape Memory Alloy that contract when applying specific heat on it, and it can be used as an actuator. This work presents SMA in the shape of a spring to operate and control the accurate position of the 2-D system which containing four SMA springs, two SMA springs for the x-axis and two SMA springs for the y-axis. The theoretical design and calculations for SMA springs have been presented to collect information about the SMA springs. In a practical manner, the SMA spring characteristic like force and displacement were collected by a test bed that was designed and constructs before making the final rig. The setting shape of the SMA spring was presented and done as per the theoretical calculations. In the rig, each axis works as a two-direction actuator, the actuator is not prone to precise position points due to hysteresis and temperature variation. The SMA spring exhibited hysteresis and imprecise pointing, for that employing PID (Proportional Integral Derivative) with tracking mode controller to compensate the hysteresis. PID control system is played a decisive role with tracking mode model that achieves the aim behind the construction of the experimental rig. Good results have been obtained presented in three cases of drawing different shapes.