Fuzzy Adaptive PID Control Algorithm Research Articles

Application study of the power control system for air-cooled proton exchange membrane fuel cell based on fuzzy adaptive with heuristic adaptive period strategy

As a zero-emission, non-polluting power generation device, hydrogen fuel cells are of great interest to the global industry with the advantages of high-power generation efficiency, wide source, and wide application scenarios. Influenced by various factors, it leads to a complex fuel cell control system and unstable output characteristics, which is one of the difficulties in the current technology development. In this section, an embedded power control system according to an STM32 microcontroller is developed to improve the steady-state characteristics of cathode open air-cooled fuel cell (PEMFC), and a fuzzy adaptive PID control algorithm according to the heuristic adaptive strategy of time interval is proposed. The control system achieves real-time detection of operating status. It maintains the stable output of cathode open air-cooled PEMFC by predicting vehicle speed in real-time to precisely control the hydrogen supply and external additional fans to control the operating temperature by the control strategy.

Non-Invasive Intraoral Stand-Alone Tongue Control System Based on RSIC-V Edge Computing

The intelligent tongue control system is of great significance for assisting the independent life of patients with a limb disability. In order to more accurately control the assisted living equipment of incompetent patients and solve the power-loss problem of the intelligent tongue control system, this research designs a non-invasive pressure sensor array for tongue touch signal detection in the oral cavity and proposes a tongue control system based on RSIC-V edge computing. The system converts the tongue touch pressure data into specific control instructions on the edge of the RSIC-V chip and transmits them to the receiver, thus reducing the transmission of data. This study takes control of the wheelchair motor as the test object. In the experiment, the speed response time test, the center click task, and the power consumption experiment are carried out, whose results show that the adaptive fuzzy PID control algorithm has good robustness in the system; when the DC motor with a given speed of 750 r/min reaches the steady state, its rise time is 0.108 s and the adjustment time is 0.59 s. The dynamic power consumption of the non-intrusive intraoral stand-alone tongue control system proposed in this paper is found to be 3.745 MW, which is 11.5% lower than the total power consumption of the sTD system.

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.

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.

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.

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.

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>

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.

A Sport Monitoring System Based on the Optimized Adaptive Fuzzy PID Control Algorithm in OneNet Internet of Things and Cloud Platform.

Aiming at some problems existing in the existing sports monitoring system, based on the joint action of OneNet Internet of Things (IoT) and cloud platform, an optimized adaptive fuzzy PID control algorithm is adopted to monitor and analyze sports. Finally, the accuracy of the optimization model is verified through the comparison of different models, and the algorithm is used to predict and analyze sports. The research shows that (1) as the index calculation of cloud platform shows, with the increase of iteration time, the change curve of relevant indexes can be divided into four different stages, namely, rapid fluctuation stage, slow decline stage, slow fluctuation stage, and rapid decline stage. (2) The conventional calculation method (CPID) cannot well describe the change rule of the test data in the early stage of settlement. The fuzzy adaptive calculation method (NPID) also exposes some errors in the fitting and description of the test curve in the calculation process, while the improved adaptive calculation method (GPID) can describe the change characteristics and rules of the test curve well for different stages. (3) Compared with the original model, the optimization model can better describe the first and second stages of index change, indicating the accuracy of the optimization model. And the algorithm can be used to predict and analyze the changes of indicators and sports monitoring better, and the analysis results can provide relevant guidance for sports monitoring. This optimization scheme provides basis and theoretical support for the application of OneNet IoT and cloud platform.

Research on Shipborne Helicopter Electric Rapid Secure Device: System Design, Modeling, and Simulation.

The research objective of this paper is to propose a new type of ERSD to solve the problem of the uncontrollable velocity of the claw in the current RSD. Firstly, the working characteristics of the RSD in ASIST are analyzed, and the design scheme of the transmission system of the ERSD is provided. The control system is designed by combining the vector control algorithm with the fuzzy adaptive PID control algorithm. On this basis, the trajectory planning of claw capture velocity is completed. Finally, the dynamics model of the transmission system of the ERSD is built by power bond graph theory, and the system simulation is carried out. The results show that the maximum capture time, velocity, and force were reduced by 47%, 53%, and 80%. In addition, when the ERSD is towing the helicopter, the mechanical claw can still provide good velocity tracking performance under a maximum interference load of 34,000 N.

Design of lane changing warning system for high speed vehicle based on fuzzy adaptive PID

In order to overcome the low precision and efficiency of the traditional lane changing warning system for high-speed vehicles, a new lane changing warning system based on fuzzy adaptive PID is proposed in this paper. Firstly, the hardware structure of the control system is designed. The hardware mainly includes the early warning system control module and the fuzzy adaptive PID controller module to achieve high-precision early warning. In the software part of the system, in order to improve the turning sensitivity of the car, the fuzzy adaptive PID control algorithm is used for yaw rate feedback control, and the lane change warning algorithm is constructed. The experimental results show that, compared with the traditional lane changing early warning system, the early warning accuracy and efficiency of the designed system are significantly improved, and the maximum early warning accuracy reaches 96%.

Application of Digital Twin Technology in Energy and Power Industry

Constructing digital twin of physical entity and analyzing twin data, and transfer learning method can be used to transfer the characteristics of twin to physical entity for analysis. This paper mainly studies the application of digital twinning technology in the energy and power industry. Aiming at the energy saving and control problem of building energy demand terminal, the fuzzy adaptive PID control algorithm is used to simulate the indoor HVAC system and lighting system, so as to solve the indoor temperature and illumination intensity are greatly affected by the weather, time-varying and unpredictable factors. By comparing with the conventional PID, we can see that the fuzzy adaptive PID controller adjusts faster, the output duty ratio is more accurate, and can achieve better control effect.

Design of and Experiment with Seedling Selection System for Automatic Transplanter for Vegetable Plug Seedlings

In the process of vegetable plug seedling cultivation, packaging, and transportation, there may be missing, unhealthy or injured seedlings in the tray, which results in a missed planting or a low seedling survival rate after automatic transplanting. In this study, a seedling selection system with the function of seedlings identification, week seedlings elimination, and missing seedlings supplement was developed for an automatic transplanter. A plug seedling identification system based on a machine vision was used to detect vegetable plug seedlings based on the area characteristics of plug seedlings, stem leaves and plug bodies. The identification results were transmitted to a programmable logic controller (PLC), which controlled a nozzle to eliminate the unqualified seedlings from the conveyor belt lattice. When the empty conveyor belt lattice reaches the seedling throwing funnel, the rear conveyor belt lattice with the plug seedling is accelerated to ensure the continuity of seedlings supply. The adaptive fuzzy PID control algorithm was used to control the stepper motor of the conveyor belt to realize accurate seedling conveying and a seedling supplement. Using 30 days pepper plug seedlings as experimental seedlings, a comparative field experiment was carried out to evaluate the performance of the seedling selection system. The results showed that when the seedling selection system was turned on and the seedling extracting frequencies were 60, 80, and 100 plants/min, the success rates of plug seedling identification were 98.84%, 98.38%, and 96.99%, and the robust seedling rates were 98.05%, 97.78%, and 95.83%. The robust seedling rates were increased by 15.64%, 16.07%, and 13.89%, respectively, in contrast to turning off the seedling selection system.

Adaptive fuzzy control method of hydrogen fuel cell gas supply system

This paper takes the hydrogen fuel cell gas supply system as the research object. Firstly, it analyzes the working principle of the hydrogen fuel cell and the control requirements of the gas supply system, then uses the fuzzy adaptive PID control algorithm to regulate the gas pressure of the system. Finally, Matlab/Simulink software simulation platform is used for simulation verification, and the control effects of conventional PID control and fuzzy adaptive PID control are compared. The results show that the gas supply system based on fuzzy adaptive PID control can effectively regulate the gas pressure value and has a good control effect.

Temperature and water level control system in water thermal mixing process using adaptive fuzzy PID controller

Thermal Mixing Process, which is important in various industries, is a multiple-input multiple-output process (MIMO). It works by regulating hot-water and cold-water flows to control the temperature and level of the mixture. The Adaptive Fuzzy PID Control (AFPIDC) algorithm is a combination of two types of controller, has a simple PID basis with added Fuzzy aspects to speed up control. The AFPIDC algorithm is applied to the simulation of water thermal mixing process and is done with MATLAB/SIMULINK program. The fuzzy algorithm uses two inputs (errors & changes of errors) and has three outputs (changes of PID constants). The system is tested by simulating setpoint changes and adding disturbance. The testing result shows that AFPIDC controllers performs better than PID in controlling temperature and level. In temperature control, the PID settling time is 830 seconds, AFPIDC is 328 seconds and the PID overshoot is 6,3% and AFPIDC is 0%. In level control, the settling time of PID is 3221 seconds, while AFPIDC is 235 seconds, with PID overshoot is 10,5%, while AFPIDC 0%. From testing the system with leakage disturbance, the temperature controller needs time to regain stability at PID 780 seconds, AFPIDC 250 seconds. Meanwhile the level controlling stabilizes at PID 4510 seconds, and AFPIDC at 225 seconds.

A Fuzzy Adaptive PID Control Algorithm with Improved Quantification Factor

The phase-shifting full bridge digital power supply based on Fuzzy PID control is difficult to realize the full mapping from the fuzzy universe to the actual output domain, resulting in the loss of fuzzy control quantity, resulting in the dynamic performance problems of large amplitude and long time output fluctuation. In this paper, a fuzzy adaptive PID control algorithm with improved quantification factor is proposed. The online adjustment function of quantification factor in fuzzy universe is introduced in the quantification link. The function can adjust the fuzzy quantification factor in real time according to the current sampling error and error change rate of the system. In order to verify the effectiveness of the improved algorithm, the traditional PID control, the traditional fuzzy PID control and the quantification factor improved fuzzy PID control are applied to the phase-shifting full bridge small signal disturbance model respectively. The results show that the fuzzy adaptive PID control algorithm with the quantification factor online adjustment function has higher dynamic performance.

Discrete fuzzy adaptive PID control algorithm for automotive anti-lock braking system

Due to the low slip rate and poor braking performance of automobile anti-lock braking system, a discrete fuzzy adaptive PID control algorithm for automobile anti-lock braking system is designed in this paper. Based on the theory of discrete fuzzy adaptive PID control algorithm, the braking force of a vehicle is analyzed and the anti-lock control target is set. The automobile adhesion coefficient and wheel slip rate are calculated as the input and output of the PID control algorithm to complete the control of the anti-lock brake system of the network automobile. Experiments show that the sliding rate of the discrete fuzzy adaptive PID control algorithm of the designed anti-lock system is close to 20%, which proves that the design method has good braking effect.

Optimal Trajectory Planning and Control for Automatic Lane Change of in Wheel Motor Driving Vehicles on Snow and Ice Roads

In the process of vehicles changing lanes, ice and snow pavement is a very common special working condition, and the research on automatic lane change is usually based on medium and high adhesion coefficient roads. Aiming at the special situation of low adhesion coefficient of ice and snow pavement, this paper studies the process of automatic lane change of vehicles on low adhesion roads driven by the front drive automobile driven by in-wheel motor. First, a dynamic model of a front drive automobile driven by in-wheel motor is established. Then, the trajectory planning is performed based on the fifth-degree polynomial method; the lateral acceleration threshold to ensure the safety of the lane change is deduced; the optimization function of the lane change time and the longitudinal driving displacement is established; and the optimal trajectory to ensure the safety and the lane change efficiency is obtained. Finally, the controller is established based on the fuzzy adaptive PID control algorithm, and the driving torque of the hub motor is distributed through the torque distribution strategy to perform the trajectory tracking control of the vehicle. Simulation results show that the vehicle can safely and smoothly change lanes along the planned trajectory.