Fuzzy PID Control Algorithm Research Articles

Design of a Closed Piggery Environmental Monitoring and Control System Based on a Track Inspection Robot

To improve environmental quality in enclosed piggeries, a monitoring and control system was designed based on a track inspection robot. The system includes a track mobile monitoring platform, an environmental control system, and a monitor terminal. The track mobile monitoring platform consists of three main components: a single-track motion device, a main box containing electronic components, and an environmental sampling device. It is capable of detecting various environmental parameters such as temperature, humidity, NH3 concentration, CO2 concentration, light intensity, H2S concentration, dust concentration, and wind speed at different heights below the track. Additionally, it can control on-site environmental control equipment such as lighting systems, ventilation systems, temperature control systems, and manure cleaning systems. The networked terminal devices enable real-time monitoring of field equipment operating status. An adaptive fuzzy PID control algorithm is embedded in the system to regulate the temperature of the piggery. Field tests conducted on a closed nursery piggery revealed that the system effectively controlled the maximum temperature range within 2 °C. The concentrations of CO2, NH3, and PM2.5 were maintained at a maximum of 1092 mg∙m−3, 16.8 mg∙m−3, and 35 μg∙m−3, respectively. The light intensity ranged from 51 to 57 Lux, while the wind speed remained stable at approximately 0.35 m∙s−1. The H2S concentration was significantly lower than the standard value, and the lowest relative humidity recorded was 18% RH at high temperatures. Regular humidification is required in closed piggeries and other breeding places when the system does not trigger the wet curtain humidification and cooling function, as the relative humidity is lower than the standard value. By controlling the temperature, the system combined with a humidification device can meet environmental requirements. The control method is simple and effective, with a wide range of applications, and holds great potential in the field of agricultural environmental control.

Research on speed control of high-speed trains based on hybrid modeling

With the continuous improvement of train speed, the automatic driving of trains instead of driver driving has become the development direction of rail transit in order to realize traffic automation. The application of single modeling methods for speed control in the automatic operation of high-speed trains lacks exploration of the com-bination of train operation data information and physical model, resulting in low system modeling accuracy, which impacts the effectiveness of speed control and the operation of high-speed trains. To further increase the dynamic modeling accuracy of high-speed train operation and the high-speed train's speed control effect, a high-speed train speed control method based on hybrid modeling of mechanism and data drive is put forward. Firstly, a model of the high-speed train's mechanism was created by analyzing the train's dynamics. Secondly, the improved kernel-principal component regression algorithm was used to create a data-driven model using the actual opera-tion data of the CRH3 (China Railway High-speed 3) high-speed train from Huashan North Railway Station to Xi'an North Railway Station of "Zhengxi High-speed Railway," completing the mechanism model compensation and the error correction of the speed of the actual operation process of the high-speed train, and realizing the hybrid modeling of mechanism and data-driven. Finally, the prediction Fuzzy PID control algorithm was devel-oped based on the natural line and train characteristics to complete the train speed control simulation under the hybrid model and the mechanism model, respectively. In addition, analysis and comparison analysis were conduct-ed. The results indicate that, compared to the high-speed train speed control based on the mechanism model, the high-speed train speed control based on hybrid modeling is more accurate, with an average speed control error reduced by 69.42%. This can effectively reduce the speed control error, improve the speed control effect and oper-ation efficiency, and demonstrate the efficacy of the hybrid modeling and algorithm. The research results can provide a new ideal of multi-model fusion modeling for the dynamic modeling of high-speed train operation, further improve control objectives such as safety, comfort, and efficiency of high-speed train operation, and pro-vide a reference for automatic driving and intelligent driving of high-speed trains.

An improved particle swarm fuzzy PID for adaptive control of temperature in CFRP induction heating

An Improved Particle Swarm Optimization-Based Fuzzy PID Control Algorithm(IPSO-PID) is used to address the problem of poor CFRP molding quality caused by the inadequate adaptive capability and slow response time of the induction heating temperature control system during the curing process of carbon fiber reinforced polymer (CFRP) by electromagnetic induction heating. The three PID parameters are optimized and self-adjusted using the improved algorithm. This algorithm combines the advantages of PSO and Fuzzy-PID algorithms. It guarantees the control accuracy and search performance of the improved algorithm during the iterative process and prevents the improved algorithm from settling for the local optimal solution. The outcomes of simulation and experimentation demonstrate that the algorithm significantly improves the controller’s capacity for adaptation and shortens the adjustment period. In contrast to the conventional PID algorithm, this algorithm is better suited for controlling the temperature of electromagnetic induction heating CFRP because it reduces overshoot and steady-state error more than it does.

Fuzzy PID control theory in multi-objective optimal longitudinal following of intelligent trolley

Abstract As a kind of mobile robot, the intelligent trolley is also called a wheeled robot, and this field is the fastest growing in the current technological development. Smart trolleys have good automatic guidance functions, often used in modern logistics conveying systems. A fuzzy control algorithm has a good control effect for nonlinear systems and has a strong anti-interference ability. In order to optimize the multi-objective longitudinal following system of smart carts, therefore, this paper develops the study of fuzzy PID control algorithm for multi-objective longitudinal following of smart carts. Based on the incremental PID control, an improved PID algorithm is constructed by introducing incomplete differentiation, differential prior, and “optimal curvature” algorithm. Based on the actual control experience, the fuzzy control table is designed, and the adaptive control algorithm is obtained. Finally, the simulation results of the fuzzy PID control algorithm are tested by MATLAB/Simulink, and the most suitable fuzzy PID control algorithm for multi-objective optimal longitudinal following of intelligent carts is derived.

Boundary Guidance Strategy and Method for Urban Traffic Congestion Region Management in Internet of Vehicles Environment

Accelerated urbanization has increased regional traffic congestion. To alleviate traffic congestion in a homogeneous road network, combined with the advantage of real-time traffic information obtained by the Internet of Vehicles (IoVs), a boundary guidance strategy for traffic congestion region is proposed. The strategy considers the optimal operation state of traffic congestion region and is divided into two categories according to different destinations of traffic demands. Meanwhile, a method for the boundary guidance strategy is presented in which the macroscopic fundamental diagram (MFD) is used to determine the optimal accumulation, a traffic flow equilibrium model is established to calculate the real-time accumulation, and a fuzzy adaptive PID control algorithm is designed to calculate the optimal traffic inflow of the traffic congestion region. Furthermore, an example is selected for simulation. The results show that the boundary guidance strategy can effectively improve the operational state of the road network and alleviate traffic congestion. Finally, the influence of connected vehicle penetration rate on the strategy is discussed. The simulation results show that the strategy can improve the operation state of the road network under mixed traffic flow, and the higher the penetration rate, the more significant its effect on alleviating traffic congestion.

Design of an Adaptive Algorithm for Feeding Volume–Traveling Speed Coupling Systems of Rice Harvesters in Southern China

We developed an adaptive algorithm to reduce rice loss in harvesting, promote threshing and improve the quality and efficiency of small- and medium-sized rice harvesters operating in southern China’s hilly and mountainous areas. Using a fuzzy PID control algorithm, the harvester adapts to the rice harvesting conditions in southern China, and monitors rice feed volume changes and instantly adjust the traveling speed to optimize feed volume levels and threshing quality. We compared and analyzed the algorithm and the traditional PID control regulation effect in the simulation experiment. The algorithm had a quicker response speed and stable accuracy. In the field trial, the average error rate was 3.4%, and the maximum error rate was 5.1%, with most data points centered around the ideal feeding rate of 3.2 kg/s. Our results showed that the algorithm’s stability, accuracy, and real-time performance met the threshing loss reduction requirements of southern China’s rice harvesting operations.

Fast and Precise Temperature Control for Axon Stretch Growth Bioreactor Based on Fuzzy PID Control.

A suitable environment is essential for successful long-term cell culturing in vitro. Too high or too low temperature will affect the growth of cells, so we need to maintain the constant temperature of the cell culture environment. Usually, cells are cultured in a cell incubator, and the constant temperature is provided by the cell incubator. Recently, we have developed a multi-channel axon stretch growth bioreactor for rapid acquisition of autologous nerve tissue. Since the motor and controller are placed in the incubator for a long time, the service life of the equipment will be shortened or even damaged due to high humidity and weak acid environment. In order to enable the axon stretch growth bioreactor to culture cells independently, we designed a constant temperature control system for the device. Firstly, the simulation results show that the fuzzy PID control reduces the overshoot and improves the traditional PID control with large overshoot and low control precision. Then, the two control algorithms were applied to the multi-channel axon stretch growth bioreactor by STM32F4 microcontroller. The experimental data show that the fuzzy PID control algorithm has good control effect and can meet the requirement of constant temperature of cell growth. Finally, nerve cells derived from human pluripotent stem cells were successfully cultured in a cell culture amplification chamber under a constant temperature environment provided by a fuzzy PID controller, and well-developed axons could be seen. In the future, we may transplant stretch growth axons into living organisms to repair nerve damage.

Vehicle Rollover Warning and Control Based on Attitude Detection and Fuzzy PID

Car rollovers are a class of serious traffic accidents that can easily cause heavy casualties and property damage, particularly for special operation vehicles. To enhance the driving stability of vehicles on forest roads, we developed a control strategy for wire-controlled auxiliary braking based on body-attitude detection and the overall design of the system. Moreover, the control system was further investigated and developed. A three-degrees-of-freedom (3-DOF) vehicle dynamics model with longitudinal, lateral, and lateral tilt was developed based on actual-vehicle test data. The lateral load-transfer rate (LTR) of the vehicle was selected as the early warning algorithm for vehicle rollover; the differential braking of the vehicle was realized by adjusting the pressure of the wheel cylinders; and automatic speed reduction was achieved according to the rollover attitude of the vehicle by combining the fuzzy-PID control algorithm. Finally, a vehicle dynamics model was developed, and the results verified the effectiveness of the anti-rollover control strategy under extreme operating conditions.

An Improved Differential Evolution Adaptive Fuzzy PID Control Method for Gravity Measurement Stable Platform.

In the platform gravimeter, the stabilization accuracy of the gravimetric stabilization platform is crucial to improve the accuracy of gravity value measurements due to its uncertainties, such as mechanical friction, inter-device coupling interference, and nonlinear disturbances. These cause fluctuations in the gravimetric stabilization platform system parameters and present nonlinear characteristics. To resolve the impact of the above problems on the control performance of the stabilization platform, an improved differential evolutionary adaptive fuzzy PID control (IDEAFC) algorithm is proposed. The proposed enhanced differential evolution algorithm is used to optimize the initial control parameters of the system adaptive fuzzy PID control algorithm to achieve accurate online adjustments of the gravimetric stabilization platform's control parameters when it is subject to external disturbances or state changes and attain a high level of stabilization accuracy. The results of simulation tests, static stability experiments, and swaying experiments on the platform under laboratory conditions, as well as on-board experiments and shipboard experiments, all show that the improved differential evolution adaptive fuzzy PID control algorithm has a higher stability accuracy compared with the conventional control PID algorithm and traditional fuzzy control algorithm, proving the superiority, availability, and effectiveness of the algorithm.

Design and Test of Self-Leveling System for Cleaning Screen of Grain Combine Harvester

As one of the core working parts of a combine harvester, the cleaning device directly affects the operation performance of the whole machine. This is especially the case on hilly and gently sloping terrain, which, due to the uneven ground, causes the combine harvester body to incline and material to accumulates on one side, resulting in a high cleaning loss rate. To solve this problem, a self-leveling cleaning screen device and a control system based on a fuzzy PID control algorithm are developed for a caterpillar harvester, enabling it to operate on gentle slopes of 10°. To verify the performance of the fuzzy PID algorithm applied to this system, simulation tests, response tests, comparison tests, and field tests were carried out. The indoor test results show that the system has a good tracking effect when the inclination amplitude does not exceed 10°. The maximum leveling error is −0.62°, the maximum leveling time is 1.85 s, and the maximum overshoot is 1.5°. The field test results show that when the tilt angle of the harvester body is within 10°, the system can stabilize the real-time leveling of the cleaning screen. Even with an increase in the tilt angle of the harvester, the cleaning loss of the harvester installed with the automatic leveling system can still be maintained at a low level. The cleaning loss rate of the harvester is 1.2% higher after leveling than during flat operation, which meets the accuracy requirements of the system design. Therefore, this system can be applied to grain combine harvesters and effectively reduce the cleaning loss caused by their operation.

Research on Stellar Spectrum Simulation Method Based on Genetic Algorithm and Fuzzy PID Composite Control

For the current spectral simulation based on Digital Micromirror, the spectral simulation unit has different bias properties and nonlinear modulation, which leads to the lack of a spectral simulation method for multiple color temperature modulation. In this paper, a stellar spectrum simulation method based on Genetic Algorithm and fuzzy PID compound control was proposed. It analyzed the composition and working principle of the stellar spectrum simulation system, designed the corresponding matrix algorithm, studied the compound control spectral simulation algorithm of Genetic Algorithm and fuzzy PID, constructed a dual-input, three-output fuzzy PID controller architecture, and designed the Genetic Algorithm dual-optimized fuzzy PID control algorithm. In this research, an experimental platform was built to verify the generalization and simulation accuracy of the Genetic Algorithm and fuzzy PID compound control stellar spectrum simulation method based on the 2500K-10000K color temperature spectral curve as the target. The results indicated that the error of 2500K-10000K spectral simulation was between -2.91% and 2.94%, and the error of spectral curve area simulation was between -0.18% and 0.22%.As the smoothing of the star spectrum curves fails to verify the simulation ability of detailed characteristics such as peaks and troughs, the AM1.5 solar spectrum is taken as the simulation object, which verifies the performance of the star spectrum simulation method based on a genetic algorithm and a fuzzy PID integration.On this basis, the AM1.5 solar spectrum was taken as the simulation target to verify the ability to simulate detailed features such as wave peaks and troughs based on Genetic Algorithm with fuzzy PID compound control stellar spectrum simulation method. The results revealed that the accuracy of the single-point spectral simulation of AM1.5 solar spectrum is -5.76%, and the spectral curve area simulation error is 0.07%. The proposed method has the ability to simulate a wide range of color temperature targets with high accuracy. It has the ability to simulate detailed features such as wave peaks and troughs. It can provide a theoretical and technical basis for the ground calibration of the high-precision star sensor development.

Solar heat collection photoelectric tracking servo drive system based on cloud computing

In order to solve the problem of high cost and low utilization of solar power generation, the author proposed a solar heat collection photoelectric tracking servo drive system based on cloud computing. By establishing the MATLAB/SIMULINK simulation mathematical model of PID and adaptive fuzzy PID control in the core controller PLC, the authors compare and analyze the response characteristics of the tracking control system under different control strategies under the action of unit step signal and disturbance. The experimental results show that the overshoot of the adaptive fuzzy PID control algorithm is very small, almost zero, the response speed is the fastest, the transition time is the shortest, the system reaches stability at about 0.02 second. In conclusion the tracking control system of solar concentrator under adaptive fuzzy PID control not only has good tracking control accuracy, but also has the advantages of strong anti-interference ability and good stability, which greatly improves the comprehensive performance of the tracking control system of solar concentrator.

Research on Attitude Control System of Quadrotor Unmanned Aerial Vehicle based on Fuzzy PID Control Algorithm

The control of quadrotor aircraft mainly includes attitude control and position control. Because the change of position is caused by the change of attitude, it is most important to control the attitude of quadrotor aircraft. PID control principle is simple, easy to master, convenient to use, strong adaptability and good robustness, and its control quality is insensitive to the change of the controlled object, so it is most widely used. Quadrotor UAV is a strongly coupled underactuated system, and its stability is easily affected by system uncertainty and external interference during flight. Therefore, an improved fuzzy PID control method is proposed. In this article, the significance and ideas of constructing the attitude control system of quadrotor UAV based on fuzzy PID control algorithm are analyzed. This control strategy not only keeps the advantages of fuzzy control algorithm, which is independent of accurate model, and flexible and fast control, but also combines the advantages of PID control algorithm with small static error, and can achieve good control of nonlinear complex system.

Research on Trajectory Planning and Control of Operational Underwater Robots

For the complex system modeling of operating underwater robots, various modules are built in Simulink to simulate the interaction of various forces and torques inside the underwater vehicle. According to the characteristics of the ROV itself, the ROV dynamics model is reasonably designed. In view of the problems of the position tracking delay, underwater environment interference, and unstable operation of the ROV, the fuzzy fractional PID control algorithm is adopted in the controller in this paper, and the control effect of the fuzzy PID and PID algorithm is compared and analyzed.

Miniature Detection Buoy Based on Fuzzy Adaptive PID Algorithm

<p>The ocean buoy is an important technical means to obtain ocean environmental parameters as a newly developed detection technology. Traditional ocean buoys use hydraulic devices to change the volume of the oil bladder to achieve fixed-depth control, with slow response speed and long detection cycle. Therefore, it can only be applied to deep-sea environment and lacks the ability of rapid and fine detection in shallow waters (<200m). Considering the above problems, the paper designs a miniature detection buoy, which adopts an innovative inner and outer sleeves design. The miniature detection buoy uses a geared motor to drive the internal screw rotation to quickly stretch and shrink the volume of the shell to achieve the functions of floating, diving and fixed-depth. Moreover, the control performance research is carried out by establishing the dynamics model of the miniature detection buoy. According to the model, the fuzzy adaptive PID control algorithm is used to accurately control the speed and steering of the geared motor. The simulation results show that the buoy system with the fuzzy adaptive PID control algorithm has better fixed-depth accuracy and response speed than the classic PID control algorithm.</p> <p> </p>

Application of Fuzzy PID in Walking along the Wall Strategy of Mobile Robot

Aiming at the walk-along-the-wall strategy of the mobile machine, a guidance method based on two analogue laser ranging sensors and a fuzzy PID control algorithm is proposed in this paper, which solves the shortcoming that traditional PID control algorithm does not apply to the nonlinear movement of mobile machine. The fuzzy controller and fuzzy rules were first designed, and the control effect was simulated in MATLAB to verify the superiority of fuzzy PID control in the walking along the wall strategy, making the movement smoother and more stable.

Opening and Closing Positioning Control of the Seedling Picking-Up Mechanism Based on Fuzzy-PID Control Algorithm

The success rate of automatic transplanter seedling picking is affected by the harsh operating environment of the transplanter and the reduced output accuracy of the opening and closing positioning of the seedling-picking mechanism due to the interference of the external environment. To solve such problems, the paper proposes to use an adaptive fuzzy PID control algorithm as the opening and closing positioning-control system of the seedling-picking mechanism. Based on the theoretical analysis and calculation of the positioning accuracy of the seedling-picking mechanism, a mathematical model of stepper motor angular velocity control was established, and a stepper motor angular velocity control model based on fuzzy PID (conventional PID) control was established in MATLAB/Simulink. In order to further simulate the actual working condition of the seedling-picking mechanism, a simulation model of stepper motor control with disturbance was built, and the response results showed that the fuzzy PID control system could get rid of the disturbance faster under the action of external disturbance signals. The results of the opening and closing positioning test of the seedling-picking mechanism showed that the fuzzy PID control system has higher control accuracy and more stable control performance than the PID control system, which meets the design requirements.

EKF-AF PID-Based Attitude Control Algorithm for UAVs

Aiming at the problems of manual setting of control rules and low control accuracy of the fuzzy PID control method, an attitude control method of the unmanned aerial vehicle (UAV) based on the extended Kalman filter (EKF) and adaptive fuzzy PID (AF PID) is proposed. The dynamics equations and measurement equations of UAV are established, and the EKF is used to estimate and predict attitude changes; an adaptive fuzzy PID control algorithm is designed, and the adaptive adjustment method is adopted to revise fuzzy control rules and parameters online; a simulation platform of the attitude control system (ACS) of UAV is built to simulate and verify the control effect of the method. The simulation results show that the proposed algorithm can estimate the change of attitude angles accurately and improve the control accuracy; meanwhile, it also ensures the stability and rapidity of the attitude changes. The research results can resolve the contradiction between high precision and rapid stability of complex systems to a certain extent.

Development and Testing of Automatic Row Alignment System for Corn Harvesters

Corn harvester row alignment is a critical factor to improve harvesting quality and reduce cob drop loss. In this paper, a corn harvester automatic row alignment system is designed with a self-propelled corn combine harvester, incorporating a front-end touch row alignment mechanism of the cutting table and a harvester steering control system. Corn stalk lateral deviation from the reference row during harvesting is detected by a front-end touch-row alignment mechanism that serves as an input to the automatic alignment system. The harvester steering control system consists of Hirschmann PLC controller, electric steering wheel, steering wheel deflection angle detection device, display module and mode selection module, etc. The adaptive fuzzy PID control algorithm is used to determine the desired turning angle of the harvester steering wheel by combining with the harvester kinematic model, and the model is simulated and analyzed by Matlab/Simulink software. The automatic row alignment system was mounted on a 4LZ-8 self-propelled corn harvester for field tests, and the test results showed that the average percentage of deviation of corn stalks from the center of the row alignment cutting path within ±15 cm during the automatic row alignment process was 95.4% at harvester speeds ranging from 0 to 4.6 km/h, which could meet the requirements of the corn harvester for row alignment harvesting. The test results meet the requirements of the corn harvester for row alignment and serve as a benchmark for the research on automatic row alignment of corn harvesters.

Research on control strategy of asymmetric electro-hydraulic servo system based on improved PSO algorithm

In the electro-hydraulic servo control system, there are some problems, such as low position control accuracy, uncontrollable speed, speed impact, and asymmetric control due to the asymmetry of hydraulic cylinder. To solve the above problems, this paper proposes a Fuzzy PID control algorithm with load force compensation based on improved PSO optimization. This improved particle swarm optimization algorithm introduces crossover and mutation operations in genetic algorithm to improve the optimization performance of traditional particle swarm optimization algorithm. The mathematical model of valve controlled asymmetric cylinder is established, and the Amesim linearization analysis function is used to identify the system, establish the transfer function, and build the control system. The joint simulation and test results of Amesim and MATLAB show that the proposed control strategy can realize the simultaneous control of speed and position. Under different loads, different speeds, and different input follow signals, it does not need to adjust any parameters. Regardless of forward or reverse operation, the optimal dynamic performance and minimum steady-state error are obtained, which greatly improves the response speed of the system. The effectiveness and superiority of the improved PSO compound control strategy are verified.