Fuzzy Proportion Integration Differentiation Research Articles

Research on the Autonomous Control Technology Used in the Slurry Mixing System of Cementing Units

Cementing is a critical link in oil and gas exploitation, in which slurry density control is particularly important. In this study, we examined a slurry mixing control system in order to solve the problem of time delays in the mixing system. The model of a slurry mixing system was built in accordance with the system’s structure. A Smith fuzzy PID (proportion integration differentiation) composite control solution is proposed herein, and the simulation results show that the adjustment time and overshoot are lower than those of the conventional PID control and Smith predictive compensation control. A genetic algorithm is utilized to optimize the quantization factor and scale factor of the Smith fuzzy PID controller. Following optimization, the rise time of the controller was found to be 0.45 s, which represents a decrease of 35.9%, the overshoot was reduced by 0.4%, and the stabilization time was reduced by 36.6%. Afterward, we built a cementing slurry mixing simulation experimental platform, and experiments were used to verify the feasibility and superiority of the Smith fuzzy PID controller optimized by the genetic algorithm in comparison with the conventional controllers. The study results thus provide a scientific basis for the engineering application of the autonomous control technology of the slurry mixing system in cementing units.

Temperature control of beer fermentation based on variable domain fuzzy PID and neural network technology and its application analysis

AbstractTemperature control in the beer manufacturing process is crucial for product quality. Given the gap between China's automation in beer production and the international level, improving the technology in this area has gradually become a core issue in optimizing domestic beer production. This study combines a proportional integral derivative controller with a fuzzy modeling strategy and incorporates a variable‐domain structure to propose a variable‐domain fuzzy proportional integral derivative controller control method. To cope with the challenges of production interaction, the study also introduces neural network technology. The experimental data indicated that the variable‐domain fuzzy proportional integral derivative controller outperforms the conventional proportional integral derivative controller and the fuzzy proportional integral differential controller in terms of overshooting, with a maximum overshoot of only 1.0, compared with 0.50 and 0.70, respectively. The variable‐domain fuzzy proportional integral differential controller exhibited a minimal overshoot of only 0.01 when the model parameter is increased by 20%. In comparison, the other methods reach overshoot values of 0.92 and 1.0. The proposed method maintained superior stability even under the influence of impulse disturbance, step disturbance, and modeling variations. These results demonstrated that the research method is significantly more stable than both the proportion integration differentiation (PID) controller and fuzzy PID controller in complex dynamic parameter environments. The proposed method involved 60 rounds of neural network control, which was successfully implemented. The temperature readings T1 and T2 remained stable within the range of 1.0%–1.02% throughout the experiments. The study demonstrates that the proposed methods have higher accuracy and less fluctuation in actual application, making them more available. Taken together, the above results show that the combination of variable‐domain fuzzy PID controller and neural network technology in beer production has achieved excellent control results. This study not only provides a strong technical support for the progress of beer production technology in China, but also has important industrial application value and wide promotion prospects.

On the use of PID control to improve the stability of the quad-rotor UAV

The recent development of drones has aroused great concern at home and abroad. For civil industry and agricultural plant protection industry, it can provide safer and more stable flight and improve production efficiency. For the exploration industry, the application of UAV (Unmanned Aerial Vehicle) will reduce unnecessary casualties. In places where damage is serious, UAV will bring accurate on-site information and data more quickly. It can make drones to replace people in high-risk operations, scientific and technological agricultural development and survey more stable and safe; For the military industry, it can be applied to different combat environments to increase its stability. In recent years, although UAV combat and reconnaissance are not the mainstream combat methods, UAV has become or an indispensable part. Improving the stability of UAV will also increase the military combat capability, so UAV has good development prospects and economic benefits. Nowadays, PID (Proportion Integration Differentiation) control plays an indispensable role in quad-rotor UAV. How to use different types of PID control combined with existing intelligent technology to improve the stability of quad-rotor UAV is a hot issue in recent years. This paper summarizes the previous research results in this field, briefly describes the motion mechanism of four-rotor UAV, wind disturbance resistance mechanism, the action principle and contract between traditional PID control and fuzzy PID control, as well as the effect of intelligent algorithm on PID control addition, and puts forward suggestions for PID control algorithm of the future UAV.

Micro-vibration control of spaceborne payload with smart parallel struts

Currently, the observation resolution of Earth observation satellites and space telescopes is progressively increasing. However, vibrations induced by solar panel vibrations, control torque gyroscopic reaction forces, and internal satellite noise significantly impede the observation accuracy of space-borne precision optical payloads. To mitigate the impact of vibrations on precision payload, it is necessary to incorporate isolation devices into the transmission path between the payload and spacecraft bus. This paper aims to design a six-degree-of-freedom parallel struts for active control of vibration in sensitive payloads. An optical payload isolation system is constructed using a Gough-Stewart parallel configuration with flexible hinges. The dynamic model of the system is established by the Newton-Euler method. The dynamic characteristics and response of the isolation system are analyzed. Then the active controller is designed based on the Fuzzy Auto-Disturbance Rejection Control strategy, and the effectiveness of the controller is simulated and compared with the fuzzy Proportion Integration Differentiation (PID) control algorithm. The results show that the active parallel struts exhibit excellent vibration isolation performance and possess strong robustness, which provides a theoretical reference for further space applications.

Fuzzy adaptive PID control for path tracking of field intelligent weeding machine

In order to reduce the influence of external interference on the intelligent weeder, improve the stability of its path tracking system, enhance its weeding effect, and reduce the rate of seedling injury, the motion steering control of the four-wheeled weeder is studied. The control strategy of fuzzy adaptive proportional integral differential (PID) algorithm is determined by establishing the path tracking mathematical model of the field intelligent weeder; the fuzzy adaptive PID controller is designed with the input average steering angle deviation of the front wheels and the rate of change of the deviation so as to realize the automatic adjustment and optimization of the parameters. We use Simulink to build the control system model and to compare and analyze with the PID controller. The results show that under the action of the step signal, the rise time of the fuzzy adaptive PID and PID control system responses is 2.596 and 4.209 s, respectively; under the action of the impulse, the fuzzy adaptive PID has a significant advantage over the traditional PID control system in terms of the rise time and response time. In addition, this control system has a fast response speed, high adaptability, high anti-interference ability, and superior path tracking ability, which are necessary for improving the accuracy of the field intelligent weeder and reducing the rate of seedling injury.

Laser power stabilization system based on variable universe fuzzy proportional‐integral‐differential

AbstractA variable universe fuzzy proportional‐integral‐differential (PID) algorithm was designed and first application for a laser power stabilization system. To validate the algorithm's effectiveness, it is applied to the simulation model of Simulink and the experimental system based on an acousto‐optic modulator. The results demonstrate that the variable universe fuzzy PID presents higher control precision, better robustness, and can suppress long‐term drift phenomena compared with the traditional feedback control algorithm. The algorithm design method in this paper has significant reference and guidance value for laser power stabilization system.

Fuzzy proportional integral differential control of magnetorheological clutch torque based on bee colony algorithm optimization

As an intelligent transmission device, the magnetorheological (MR) clutch cannot realize its accurate torque control because it is difficult to establish its accurate torque control mathematical model. This paper designs a squeeze shear MR clutch. In order to achieve accurate torque control of the MR clutch, this paper proposes a fuzzy proportional integral differential (PID) control strategy based on the optimization of the bee colony algorithm. Based on the mechanical transmission experiment platform of the MR clutch, the performance of the torque control of the MR clutch is experimentally studied by using the real-time operating system Phar Lap ETS built by LabVIEW real-time module, it is found that the fuzzy PID control strategy based on bee colony algorithm can not only improve the torque control accuracy of the MR clutch by 70% compared to traditional PID control strategies but also increase the response speed by more than 50%.

APPLICATION OF FUZZY LOGIC IN THE CONTROL SYSTEM OF ELECTROMECHANICAL EQUIPMENT

The article considers the option of solving the problem of automatic selection of settings of proportional-integral-differential (PID) regulators to control the thermal process with chaotically changing dynamic properties. Based on analytical and experimental data, it illustrates the influence of various factors on changes in the dynamic properties of electromechanical equipment (EME) when heated during operation. The work of classical and fuzzy PID controllers was investigated using simulation models in Matlab and Simulink environments. The article illustrates the expediency of changing the configuration charts of the PID regulator when controlling the process, which can be carried out with the help of a phasing unit that implements variants of the rules for controlling the signals of the charts. The Control System ToolboxTM application was used to determine the application rules for adjusting electrical values. The attractive nature of the changes in the transition processes reflects the graphical and tabular data. Transient processes in a nonlinear control algorithm (NCA) are characterised by improved parametric characteristics. To increase the accuracy of the chart settings, it is advisable to use data from actual thermal events that determine perturbing variables. Realisation of the above-mentioned intelligent device for automatic setting of the real-time regulator, providing the formation of a more accurate output of the control signal, determines the advantages of the proposed engineering solution and the feasibility of its use at similar technological facilities. In the article, the practical implementation of fuzzy logic with the help of the Arduino family microcontroller is recommended. In addition, the stand-alone NCA unit can be used with an additional actuator for smoother thermal control during the operation of EM components, while maintaining the same basic automation system with a classic PID controller. Keywords: electromechanics, automation, controller, microcontroller, modelling, electronic model, fuzzy logic, algorithm, controller settings.

Improvement of predictive control algorithm based on fuzzy fractional order PID

Abstract The existing predictive control strategy has comprehensive prior knowledge of the controlled process, requires weak continuity of the search space for parameter optimization, and its application is limited to some extent. Therefore, improved research on the fuzzy fractional proportional integral differential (PID) predictive control algorithm is proposed. First, the control principle of PID predictive control equipment is proposed. According to this principle, the structure of the PID predictive control equipment adaptive fuzzy PID energy-saving controller is constructed. Through the PID energy-saving control parameter setting principle and fuzzy control rules, the adaptive fuzzy PID energy-saving control of PID predictive control equipment is realized. Finally, the fractional order PID predictive transfer function model is constructed to improve the predictive control algorithm based on PID optimization technology. The experimental results show that the accuracy and efficiency of the designed algorithm can get the best performance index, and its stability, overshoot, time, and control accuracy are basically unchanged. In the small area temperature control, the disturbance interference is small, the anti-disturbance ability is good, and it has strong robustness.

Application of the fuzzy proportional integral differential (PID) temperature control algorithm in a liver function test system based on a centrifugal microfluidic device

Clinical laboratory examinations frequently include biochemical analysis of the liver. The presence of alanine aminotransferase (ALT) and aspartate transaminase (AST) in serum can be used to identify liver damage. In this study, a centrifugal microfluidic-based clinical biochemical detection system was developed for the detection of liver function markers. Using the centrifugal microfluidic chip and centrifugal force on the chip, separation of blood cells and serum was performed. The extraction and mixing of quantitative serum and diluent were completed under the chip design of microchannels and microchambers. The lyophilized reagent beads in the chip interacted with the combined solution. The Fuzzy PID algorithm regulates the power of the heating film to deliver the ideal reaction temperature. In accordance with Beer–Lambert, the rate of change in the absorbance of the reaction solution at 340 nm of the light source was measured and a standard curve for the relationship between concentration and rate of change in absorbance was constructed. The system is portable, quick, and simple to use because it uses a centrifugal microfluidic chip instead of the conventional detection and analysis approach. In the future, it is anticipated that the system will have several applications in the detection of highly integrated on-chip point-of-care devices.

Fuzzy PID synchronous leveling system coupled with sync-drive for hydraulic legs of CBP: co-simulation and experimental verification

The hydraulic leveling system plays an important role in the crane load-bearing platform (Referred to as CBP). In recent years, a lot of work has been done to improve the performance of the leveling system. However, all these efforts are focused on control techniques and methods, while ignoring other comprehensive improvement options. This article takes the hydraulic legs subjected to the synchronous drive system of CBP (Referred to as CBP) as the research object, a set of hydraulic synchronous leveling system used for the valve-controlled cylinder was designed, and the mathematical model of transfer function for the valve-controlled cylinder and the theoretical solutions of main parameters in hydraulic oil circuit are obtained, then establish a mechanical model suitable for the attitude position of CBP. After that, a composite leveling strategy applied to the fuzzy PID (Proportional Integral Differential) synchronous leveling system with hydraulic legs driven synchronously was proposed, and a fuzzy PID controller was mapped out independently. Meanwhile, the above-designed sync-drive system coupling synchronous leveling process suitable for the CBP legs was carried out by AMESim and MATLAB/Simulink software. In addition, the correctness and robustness of the simulation model were verified by the leveling experiment, and the validity and reliability of the previously designed oil circuit scheme was proved. The results proposed in this paper improve the hydraulic leveling accuracy and response speed to optimize the performance of the hydraulic leveling system.

Joint Simulation Study of Glass Greenhouse Roof Cleaning Machine Based on Adams and Matlab

The glass greenhouse roof cleaning machine is driven by dual motors, and there are problems with machine deflection and inconsistent speed of the two wheels during operation, to solve these problems, a joint simulation method based on Adams and Matlab is used in this project. Firstly, build detailed 3D models using SolidWorks software and Adams software, and set the necessary constraints and material properties for the models; then, the control system is modeled with Fuzzy-Proportion Integration Differentiation (PID) control algorithm in Matlab/Simulink module; finally, a joint simulation was performed using Adams software and Matlab/Simulink software. Through the analysis of the results of the joint simulation, the problem of inconsistent deflection and the two-wheel speed of the machine is optimized after the introduction of the Fuzzy-PID controller.

Human–Machine Redundant Braking System for Aftermarket Low-Speed Electric Vehicle: Design and Validation

This paper presents the design and experimental validation of a novel human–machine redundant braking system (HMRBS) for aftermarket low-speed electric vehicles (LSEVs) to realise the backup redundancy ability and improve active safety. First, the HMRBS is designed by connecting the electro-hydraulic braking (EHB) unit oil pipelines in parallel with the manual braking (MB) unit through two three-way shuttle valves. Then, the mathematical model of the EHB subsystem is built, and a master cylinder pressure controller with adaptive fuzzy proportion integration differentiation (PID) and a servo motor speed controller with double-closed-loop proportion integration (PI) are proposed to improve the system response performance. Following this, the co-simulation model of the proposed closed-loop system is established based on AMESim and MATLAB/Simulink to validate the feasibility of the proposed control strategy. Finally, the effectiveness of the HMRBS is also validated by test rig and vehicle experiments. The results imply that the modified LSEV with the HMRBS meets the requirements of vehicle active braking ability and manual braking redundancy. Furthermore, the proposed controller can significantly enhance pressure control accuracy compared to the classical PID controller. The deceleration fluctuation and braking distance in the active braking mode are smaller than those in the manual braking mode, indicating that the proposed system makes the braking effect more stable and safer.

Research and Implementation of Intelligent Control System for Grassland Grazing Robotic Vehicle

In response to the complex animal husbandry environment, wide livestock range, and labor shortage in Inner Mongolia grasslands, this study designed an intelligent control system for robotic vehicles used in natural grazing grassland environments. The control system consists of software and hardware components and motion control algorithms. Based on the application characteristics of different mechanisms of the robotic vehicle, the motion control of the robotic vehicle is decomposed into longitudinal speed control and lateral steering control. The longitudinal speed control adopts the traditional Proportional-Integral-Differential (PID) control method. Since the hardware composition and internal angle calculation method of the steering system are complex and prone to deviations caused by external terrain, a lateral adaptive fuzzy PID controller was constructed with lateral deviation and deviation change rate as input variables and parameters Kp, Ki, and Kd as outputs. The field test results show that when the longitudinal speed was set to 0.5 m/s, the adjustment time was 1.95 s, and the steady-state error was about 0. When the longitudinal speed was the same, the lateral adaptive fuzzy PID controller reduced the rise time by 0.77 s compared to traditional control methods, reduced overshoot and steady-state error by 2%, and quickly recovered to a steady state after being disturbed. The following test results show that the designed intelligent control system can achieve real-time tracking of targets and that the motion controllers can effectively control speed and steering angle. Therefore, the intelligent control system designed in this paper can effectively achieve stable and real-time control of the robotic vehicle.

Design of Plc-based Pipe Pile Residual Slurry Recovery Equipment Control System

To solve the cost loss caused by the low recovery efficiency of pipe pile residual slurry, as well as the pollution of the environment during discharge, a control system of pipe pile residual slurry recovery equipment is designed. The equipment is controlled by PLC (Programmable Logic Controller), aiming at the problem that the traditional PID (Proportion Integration Differentiation) cannot modify the parameters in real time to meet the accuracy requirements of the residual slurry density. The fuzzy PID controller algorithm is proposed, and the simulation analysis is carried out by MATLAB at the same time. Through PLC selection, PLC I/O allocation, electronic control system design, program writing, and HMI (Human Machine Interface) establishment, the information interaction is achieved between equipment and people while monitoring the production process. Through on-site production tests, the equipment is proved to be reliable and efficient, which can make the residual slurry recovery utilization rate reach more than 90%, and reduce the production cost of pipe piles.

Research on the electro-hydraulic servo system of picking manipulator

To meet the demand for high-altitude fruit picking, a luffing mechanism system is designed and used to control the hydraulic cylinder. Combined with the performance index of the servo system, the fuzzy proportional–integral–differential (PID) is adaptive and strong, and the robustness of the picking robot is simulated. Using the MATLAB/Simulink module, the fuzzy PID controller model of the electro-hydraulic servo system for picking the robot arm is established. The simulation results show that the fuzzy PID controller model can effectively reduce the error and meet the requirements. Through the simulation of the picking robot arm, the results show that the fuzzy PID control method can not only improve the performance of the picking robot arm but also provide a reference value for future optimization control.

Thermal Management of Fuel Cells Based on Diploid Genetic Algorithm and Fuzzy PID

The operation of a proton exchange membrane fuel cell (PEMFC) is greatly affected by temperature. Reliable thermal management of fuel cells can improve the life, efficiency, and power output of fuel cells. The model established in this paper is based on the inner layer of the fuel cell, and through the analysis of the heat change and material flow between layers, the simulink model can reflect the temperature change of the end plate, the bipolar plate, and the membrane electrode assembly (MEA) plate. In terms of the thermal management control strategy, the deviation and deviation rate between the MEA plate’s temperature and the target temperature are taken as input, and the fuzzy PID (proportional integral differential) controller is used to control the cooling water flow, to achieve a cooling effect. Due to the low efficiency and instability of a haploid genetic algorithm (GA) in solving dynamic optimization problems, a diploid genetic algorithm to optimize the membership function of the controller, and improve the adaptability of the control system, was designed. The simulation results show that compared with the haploid genetic algorithm, the optimal results of 100 iterations of the fuzzy PID control strategy reduce by 27.9%. Compared with the haploid genetic algorithm and fuzzy PID control, the MEA layer temperature, under the control of a diploid genetic algorithm, is reduced by 18% and 28%, respectively, and the minimum temperature difference of the reactor is 2.28 K.

Cascade Control Method of Sliding Mode and PID for PEMFC Air Supply System

Proton exchange membrane fuel cells (PEMFC) are vulnerable to oxygen starvation when working under variable load. To address these issues, a cascade control strategy of sliding mode control (SMC) and Proportion Integration Differentiation (PID) control is proposed in this study. The goal of the control strategy is to enhance the PEMFC’s net power by adjusting the oxygen excess ratio (OER) to the reference value in the occurrence of a load change. In order to estimate the cathode pressure and reconstruct the OER, an expansion state observer (ESO) is developed. The study found that there is a maximum error of about 2200Pa between the estimated cathode pressure and the actual pressure. Then the tracking of the actual OER to the reference OER is realized by the SMC and PID cascade control. The simulation study, which compared the control performance of several methods—including PID controller, adaptive fuzzy PID controller and the proposed controller, i.e., the SMC and PID cascade controller—was carried out under various load-changing scenarios. The outcomes demonstrate that the proposed SMC and PID cascade controller method really does have a faster response time. The overshoot is reduced by approximately 3.4% compared to PID control and by about 0.09% compared to fuzzy adaptive PID. SMC and PID cascade control reference OER performs more effectively in terms of tracking compared to PID control and adaptive fuzzy PID control.

A New Control Strategy for Greenhouse Environment Control System Based on Inverse Model

The greenhouse environment control system is a type of non-linear system since the temperature and humidity of the system are highly coupled. Besides, the time lag of the temperature and humidity control process is large, so it’s quite difficult to linearize and decouple the temperature and humidity of the system. To cope with this issue, this paper proposed a novel control strategy for greenhouse environment control system based on Back Propagation Neural Network (BPNN) and inverse model, the proposed method can perform inverse identification on the temperature and humidity control system to attain higher accuracy. Then, the inverse model and the original system were connected in series to form a pseudo linear system to realize the decoupled control of temperature and humidity. After that, aiming at the impact of some non-linear factors on the greenhouse environment system, this paper adopted the adaptive fuzzy Proportion Integration Differentiation (PID) controller to enhance the adaptability of the system, thereby reducing control error and the interference caused by non-linear factors of the temperature and humidity control system. At last, the experimental results showed that, the temperature error of the system could be controlled within 1.2℃ and the error of relative humidity was less than 2.5%. The proposed method can improve the control effect of the greenhouse environment to a certain extent, and it provides a novel approach of greenhouse control.

Design and experiment of magnetic navigation control system based on fuzzy PID strategy

Abstract. In view of the difficulties in the navigation of facility agricultural equipment in a greenhouse environment, which are greatly affected by environmental factors, being difficult to navigate, and low accuracy, a magnetic navigation controller suitable for greenhouse environments is designed based on fuzzy PID (proportion integration differentiation) control and combined with the principle of magnetic navigation control in this paper. The magnetic navigation in a greenhouse environment is realised, and the installation test is carried out on the existing agricultural machinery platform. The results show that when driving in a straight line, the straightness error is controlled at ±2.5 cm m−1, and when driving on a bend, the driving deviation is controlled at ±4.5 cm m−1. Therefore, it can be considered that the magnetic navigation control method based on fuzzy PID control designed in the greenhouse environment can effectively improve the accuracy of navigation and promote the application of facility agricultural equipment to a certain extent.