Fuzzy PID Controller Research Articles

Behaviours of thermal management system with micro channels for cylindrical Lithium-ion cells under Fuzzy-PID control strategy

Replacing conventional fuel vehicles with electric vehicles can effectively reduce pollution from fossil fuels. However, quick charging has been an urgently solved problem to promote the broad application of electric vehicles. A water-cooled battery thermal management system has been proposed, in which the cylindrical batteries were embedded into the cold plate with parallel microchannels in the height direction. It greatly enlarges the contact areas between the battery and the cold plate to strengthen the cooling capacity and improve the temperature uniformity in a high-rate charge. The charge experiments for the prototype of the battery module have been carried out at a 4C charge rate. the cooling behaviours of the system have been investigated including the heat dissipation, the response time, and the average state of charge under Switch, PID, and fuzzy PID control strategies. The fuzzy PID controller shows a desired control effect on the maximum module temperature and temperature difference at different ambient temperatures. Compared with the Switch and PID control strategies, the response time of the fuzzy PID controller is the shortest. At the same time, the average state of charge at the ambient temperature of 298.15 K, 308.15 K, and 323.15 K under fuzzy PID control can be improved to 94.3%, 94.2%, and 93.5 %, respectively. The cooling behaviours of the proposed battery thermal management system under fuzzy PID control are outstanding at a charge rate of 4C and the system is suitable for electric vehicles.

Attitude control of quadrotor UAV based on fuzzy PID control under small disturbance

This paper proposes a attitude control method based on fuzzy control. Firstly, the basic model of quadrotor UAV is constructed, then the fuzzy PID controller based on fuzzy control strategy is introduced, and finally the simulation comparison experiments under PID control and fuzzy PID control are conducted. The experimental results show that the parameters of each link in the PID control method are easy to set, but the overshoot amount is large and the adjustment time is long; The simulation model of fuzzy PID control is relatively complex, and it is difficult to reach the predetermined expectation and difficult to accurately control. However, the output of overshoot, short regulation time and the waveform peak are small. In terms of anti-interference, fuzzy PID is more adaptive than PID 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.

Dynamic analysis and control optimization of hydrogen supply for the proton exchange membrane fuel cell and metal hydride coupling system with a hydrogen buffer tank

Dynamic response hysteresis in the hydrogen supply process of a proton exchange membrane fuel cell (PEMFC) and metal hydride (MH) coupling system (PEMFC-MH system) is a significant challenge, especially during transient loads. Accordingly, this study proposes a PEMFC-MH system equipped with a hydrogen buffer tank, and the heat required by MH tanks is supplied by PEMFC waste heat through the circulating water. Additionally, this study develops three control methods, including proportional-integral-derivative control (PID), fuzzy PID control, and PID coupled with model predictive control (PID-MPC) to address the response hysteresis issue. Furthermore, this study analyzes and summarizes the variation regulations of key performance parameters, and evaluates the overall performance and energy-saving effects of the system under periodic variable current conditions. Results demonstrate that dynamic response of the proposed system can be significantly improved by implementing the pressure control target in the buffer tank. Additionally, the PID-MPC control method exhibits the optimal comprehensive control effect, combining the fast regulation rate and strong anti-interference advantages. Under PID-MPC control, the main performance parameters such as the output voltage, output power, and hydrogen supply flow, can be rapidly stabilized following short fluctuations and hysteresis, even during current transient changes. Under periodic variable current conditions, the waste heat can provide sufficient heating power for MH to meet the required transient response when the hydrogen state of charge (SOC) is above approximately 3.1%. However, if the SOC drops below this level, the MH desorption flow will sharply decrease, necessitating prompt supplementation of external heaters or additional hydrogen. Moreover, the MH system can recover around 32.1% of the PEMFC waste heat under overall operating conditions, demonstrating significant energy-saving potential. This study can provide a theoretical basis and novel approaches for controlling the hydrogen supply process of such systems.

Photo-Electro-Thermal Model and Fuzzy Adaptive PID Control for UV LEDs in Charge Management

Inertial sensors can serve as inertial references for space missions and require charge management systems to maintain their on-orbit performance. To achieve non-contact charge management through UV discharge, effective control strategies are necessary to improve the optical power output performances of UV light sources while accurately modeling their operating characteristics. This paper proposes a low-power photo-electro-thermal model for widely used AlGaN-based UV LEDs, which comprehensively considers the interaction of optical, electrical, and thermal characteristics of UV LEDs during low-power operations. Based on this model, an optical power control system utilizing a fuzzy adaptive PID controller is constructed, in which a switch is introduced to coordinate the working state of the controller. Thus, the steady-state performance is effectively improved while ensuring dynamic performance. The results show that the proposed model has an average prediction error of 5.8 nW during steady-state operations, and the fuzzy adaptive PID controller with a switch can reduce the fluctuation of light output to 0.67 nW during a single discharge task, meeting the charge management requirements of high-precision inertial sensors.

Balance Control of a Flywheel Inverted Pendulum by Fuzzy Logic Controller

In this study, a flywheel inverted pendulum was modeled as simulation. The model controlled by fuzzy logic and PID controller for comparison. Fuzzy logic controllers were designed using triangular and Gaussian membership functions and various methods that are "and", "implication" and "aggregation". All gains from fuzzy logic controllers and PID were tuned by the trial-and-error method. The best performance was obtained by fuzzy logic controller that uses a triangular membership function and "prob/probor" functions. The results were evaluated in terms of three phenomena. In terms of Settling Time and Maximum Overshoot, Fuzzy Triangle MF with 0.15 s and 0 degrees, respectively, and PID and Fuzzy Triangle MF models with 0 degrees in terms of Steady-State error achieved the best success. In addition, the robustness of the control system was tested by applying two different types of disturbance inputs, random and impulse. The results show that fuzzy logic is a good alternative for balance control of a flywheel inverted pendulum, but PID has an acceptable performance.

PERFORMANCE ANALYSIS AND SPEED CONTROL USING INDIRECT VECTOR CONTROLLED FOR INDUCTION MOTOR DRIVES

This paper presents the speed control scheme of an indirect vector-controlled induction motor (IM) drive. PWM controlling scheme is based on Voltage source inverter type space vector pulse width modulation (SVPWM) and the Conventional-PID controller or Fuzzy-PID controller is employed in closed-loop speed control. Decoupling of the stator current into torque and flux producing (d-q) current components model of an induction motor is involved in the indirect vector control. The torque component Iq current of an IM is developed by an intelligent-based Fuzzy PID controller. Based on settling time and dynamic response the performance of the Fuzzy Logic Controller is compared with that of the PID Controller to sudden load changes. It provides better control of motor torque with high dynamic performance. The simulated design is tested using various toolboxes in MATLAB. Simulation results of both controllers are presented for comparison.

Design and Experiment of a Targeted Variable Fertilization Control System for Deep Application of Liquid Fertilizer

Given the problems of targeted variable deep application of liquid fertilizer in the field, such as low precision, inaccurate fertilization amount, and poor fertilization effect, a targeted variable fertilization control system of liquid fertilizer based on a fuzzy PID algorithm was designed in this study to realize the combination of precise variable fertilization technology and targeted deep-fertilization technology. Specifically, the fertilization equipment and adaptive fuzzy PID control strategy of targeted variable fertilization were designed first. Then, the mathematical model of the targeted variable fertilization control system of liquid fertilizer was established following the requirements of intertillage and fertilization of corn crops. Afterward, the response time and overshoot of the control system were simulated through the Simulink tool of MATLAB software, in which the fuzzy PID control and traditional PID control were compared. Then, the control effect of the targeted variable fertilization control system was verified through field experiments. The test results demonstrated that in the process of simulation analysis, the response time of the variable fertilization control system based on fuzzy PID control was shortened by nearly 5 s on average compared to the system based on traditional PID control, and the error was controlled within 10%. In the field test, the target rate of targeted variable fertilization equipment for liquid fertilizer reached more than 80%, and the control accuracy of the liquid fertilizer application amount also remained above 90%. Finally, the tracking experiment to check the fertilization effect proved that the targeted variable deep-fertilization method of liquid fertilizer could further improve the yield of maize crops under the premise of reducing the fertilization cost. The study provides a feasible solution for the method of precise variable fertilization combined with targeted fertilization.

Application of MPP database and artificial intelligence system in online evaluation of college students' mental health

Based on MPP database, we have conducted research and investigation on big data processing and analysis, and provided a good solution for big data. However, due to the large data storage requirements in the local file system may not be fully met. Therefore, the storage and management of big data, reliability, availability and scalability are more and more adopted by distributed file system. Therefore, MPP database based on distributed file system has become a research hotspot. In this paper, in the network control, through the control algorithm based on two kinds of artificial intelligence system, through the fuzzy PID controller to reduce the impact of system control performance. The dynamic BP network error correction and predictive PID controller are introduced into the system to reduce the influence. College Students' mental health education is an important part of the current training. Rapid and accurate online evaluation and analysis of College Students' mental health can provide reference for college students and effective help for the follow-up activities and consultation of the school, At the same time, it provides some basis for the balance of psychological scale and the revision of item setting. In this paper, MPP database and artificial intelligence system are introduced into the application of mental health assessment, which can effectively assess the mental health of college students, and thus promote the continuous development of assessment application.

Research on Hydro-pneumatic Suspension Test Bench Based on Electro-hydraulic Proportional Control

Compared with the traditional automotive suspension, hydro-pneumatic suspension has the characteristics of large energy storage ratio, nonlinear stiffness and can change the ground clearance of the vehicle body, which makes the vehicle have good ride comfort and handling stability during driving. In order to improve the performance of hydro-pneumatic suspension, it is necessary to design hydro-pneumatic suspension test bench for performance test. Aiming at the problem that the output signal of the mechanical test bench used in China is single and has large error, which is difficult to meet the performance test requirements of hydro-pneumatic suspension, a hydro-pneumatic suspension test bench based on electro-hydraulic proportional control is designed. Through AMESim/MATLAB joint system modeling and simulation, in the tracking comparison of sinusoidal signal, compared with the traditional PID control method, the fuzzy PID control method reduces the error by 56.8% and the lag time by 70%; Through the experimental analysis of hydro-pneumatic suspension elastic force characteristic diagram, indicator diagram and damping force velocity characteristic diagram, the error rate of the test bench in sinusoidal signal tracking experiment is less than 15%, which meets the test requirements of hydro-pneumatic suspension.

Frequency Control of Power System with Distributed Sources by Adaptive Type 2 Fuzzy PID Controller

The unpredictability of solar and wind energy sources affects contemporary power networks and adds frequency variations. An appropriate intelligent controller is necessary to balance electricity between generation and demand. As a result, in this research, we present a sine-cosine adaptive improved equilibrium optimization (SCaIEO) method tuned to Adaptive Type 2 Fuzzy PID Controller (AT2FPID) for frequency management of cutting-edge power systems. The efficiency of the SCaIEO approach is assessed by comparing it to the original equilibrium optimization (EO) and other comparable algorithms for the test function. Moreover, engineering applications of the SCaIEO technique are carried out by constructing an AT2FPID controller to manage the frequency of power systems that include renewable energy and dispersed sources. First, we show that SCaIEO outperforms EO, Particle Swarm Optimization, Genetic Algorithm, Moth Flame Optimization, and Gravitational Search Algorithm in the PID controller (Gravity Search Algorithm). The AT2FPID is next evaluated, and the SCaIEO AT2FPID controller’s dominance is proven by equating the outcome to the original, PID Type 1 Fuzzy PID, Type 2 Fuzzy PID controller.

Modelling and Control of a Two-Wheel Inverted Pendulum Using Fuzzy-PID-Modified State Feedback

Control of mechanical systems by electronic systems controlled by computer programs is one of the most active research areas in mechatronic systems engineering. These programs carry out control laws, which are algorithms. This study focuses on Segway control (a two-wheeled inverted pendulum which is a highly nonlinear and unstable open-loop system). Our research entails creating a control law to stabilize this system. We proposed using a state feedback controller, which provided us with a stable system and a lower error margin; however, to correct this error, we used a combination of the state feedback controller and the fuzzy-PID controller. The effectiveness of the proposed method is demonstrated using simulation results.

Research on Pump Speed Control System Based on Fuzzy PID

In this paper, based on MATLAB/simulink, the PID vector frequency conversion control of permanent magnet synchronous motor has better drive control performance in the multi-stage centrifugal pump speed control system. The electric-hydraulic coupling system of centrifugal pump has a good evaluation and research function. In view of the low efficiency of the traditional PID control method and the poor adaptability to the change of working environment, a vector control method of permanent magnet synchronous motor based on adaptive and self-adjusting fuzzy control principle is proposed for the speed regulation system of water pump. The simulation results show that the PMSM system controlled by the fuzzy PID controller is superior to the traditional PID control, and has better disturbance resistance, dynamic and steady performance.

Process stability control of corner structures in robotic gas tungsten arc additive manufacturing via arc sensing

Aiming at a higher deposition rate, robotic gas tungsten arc additive manufacturing (GTA AM) always uses the front feed mode in which the wire is fed in front of the arc along the torch travel direction. However, fabricating corner structures in this mode is still a challenging issue in robotic GTA AM, since the serious hump height at corner points during torch rotation will hinder the process stability and automation of corner structures. The novelty of this study is to solve this issue by developing an in-situ monitoring and control strategy. An arc voltage sensor is designed to monitor the arc voltage that can characterize the deposition height. The arc voltage decreases first and then keeps stable at the corner points during torch rotation. It increases rapidly as the torch runs away from the corner points. An empirical mode decomposition (EMD) algorithm filters noises in sampled arc voltages. The peak arc voltage is selected to characterize the arc length since it presents fewer fluctuations than the base arc voltage. A Fuzzy-PID controller is developed for the peak arc voltage control by regulating the wire feed speed (WFS). Open-loop and closed-loop control tests of corner structures confirm that the deposition process with the fixed WFS is forced to terminate due to the serious hump height, and that with the controlled WFS can lower the hump height markedly at the corner points. This study demonstrates that the developed arc voltage sensing and control system can remove metal humps at the corner points and enhance the process automation of corner structures in robotic GTA AM.

Modified Flower Pollination Optimization Based Design of Interval Type-2 Fuzzy PID Controller for Rotary Inverted Pendulum System

The Type 2 Fuzzy Logic System (T2FLS) is an enhanced form of the classical Fuzzy Logic System (FLS). The T2FLS based control technics demonstrated a lot of improvements for the past few decades. This is based on the advantage of its membership function (MF). Many experimental studies indicated the superiority of Type 2 Fuzzy Logic Controller (T2FLC) over the ordinary Type 1 Fuzzy Logic Controller (T1FLC), particularly in the event of non-linearities and complex uncertainties. However, the organized design method of T2FLCs is still an interesting problem in the control engineering community. This is due to the difficulties in computing the parameters associated it. A novel application of the Modified Flower Pollination (MFP) optimization algorithm in the design of T2FL is presented. The optimized Cascade Interval Type 2 Fuzzy PID Controller (IT2FPIDC) structure is proposed in this study. The best values of the parameters of the antecedent MFs and the PID gains of IT2FPIDC are found using the MFP algorithm. The MFP optimization technique was used because of its lower computational effort and high convergence speed, in view of the higher number of variables to be optimized in cascaded IT2FPIDC. The MFP-based Type-1 Fuzzy Proportional Integral Derivative Controller (T1FPIDC) is compared with the proposed MFP-based cascade-optimized IT2FPIDC. The rotary inverted pendulum (RIP) which is a non-minimum phase, non-linear, and unstable system is employed as a benchmark for testing the proposed controller. Balance and trajectory-tracking controls of the RIP are considered. Furthermore, the disturbance rejection ability of the proposed controller is analysed. The presented control methos is evaluated on the RIP manufactured by Quanser over many simulations and real-world experiments. The performance characteristics considered are steady state error (Ess), settling time (ts), maximum overshoot (Mp) and rise time (tr). The improvement of the effectiveness and robustness proposed controller in the presence of load disturbance, noise effects and parameter variation is shown.

An intelligent integrated EDI module based on fuzzy PID

Electrodeionization (EDI) is a popular method of water treatment in the current field of water treatment. EDI module mainly absorbs the charged ions in water through the cation exchange resin and realizes the ion removal of water by applying an electric field to make the charged ions move in a directional way and pass through the cation exchange membrane with selective permeability. The existing EDI module has a low integration degree and the working current cannot be adjusted intelligently according to the field environment. In order to solve the problem of low integration degree, this paper designs an EDI module that integrates the EDI module, sensor, controllable constant current source module, and main controller. Meanwhile, in order to solve the problem that the current EDI working current cannot be adjusted in real-time with the environmental water quality, an EDI module output current control method based on fuzzy PID and a main controller hardware circuit based on the adjustable constant current source circuit of a microcontroller digital-to-analog converter (DAC) are designed to adjust the output current of EDI module intelligently. Then the control model of the control system is established by Matlab respectively using the PID control method and fuzzy PID control method, and the simulation is compared. In this paper, the hardware and software of the module are developed based on STM32. The experimental results show that the designed EDI module can realize accurate and fast control of water quality.

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.

A novel PID controller for pressure control of artificial ventilator using optimal rule based fuzzy inference system with RCTO algorithm

In order to improve the pressure tracking response of an artificial ventilator system, a novel proportional integral derivative (PID) controller is designed in the present work by utilizing an optimal rule-based fuzzy inference system (FIS) with a reshaped class-topper optimization algorithm (RCTO), which is named as (Fuzzy-PID). Firstly, a patient-hose blower-driven artificial ventilator model is considered, and the transfer function model is established. The ventilator is assumed to operate in pressure control mode. Then, a fuzzy-PID control structure is formulated such that the error and change in error between the desired airway pressure and actual airway pressure of the ventilator are set as inputs to the FIS. The gains of the PID controller (proportional gain, derivative gain, and integral gain) are set as outputs of the FIS. A reshaped class topper optimization algorithm (RCTO) is developed to optimize rules of the FIS to establish optimal coordination among the input and output variables of the FIS. Finally, the optimized Fuzzy-PID controller is examined for the ventilator under different scenarios such as parametric uncertainties, external disturbances, sensor noise, and a time-varying breathing pattern. In addition, the stability analysis of the system is carried out using the Nyquist stability method, and the sensitivity of the optimal Fuzzy-PID is examined for different blower parameters. The simulation results showed satisfactory results in terms of peak time, overshoot, and settling time for all cases, which were also compared with existing results. It is observed in the simulation results that the overshoot in the pressure profile is improved by 16% with the proposed optimal rule based fuzzy-PID as compared with randomly selected rules for the system. Settling time and peak time are also improved 60–80% compared to the existing method. The control signal generated by the proposed controller is also improved in magnitude by 80–90% compared to the existing method. With a lower magnitude, the control signal can also avoid actuator saturation problems.

Temperature Regulation of Hot Vapor Preservation Treatment of Litchi Based on PSO-Fuzzy PID

In this paper, a control method was designed based on particle swarm optimization (PSO)–fuzzy PID (PSO-Fuzzy PID). In order to address the issues of significant nonlinearity and extensive hysteresis in the vapor temperature of litchi hot vapor preservation treatment equipment, as well as the inadequacy of traditional control schemes, a PSO-Fuzzy PID controller was developed through the integration of particle swarm optimization and fuzzy logic algorithms. This controller incorporates a scale factor and quantization factor, building upon the foundation of the Fuzzy PID controller. Additionally, a transfer function model was established for both the heating and disturbance links, utilizing a combination of test data modeling methods and relevant theory. Thirdly, a system simulation model was built to compare and verify PID, Fuzzy PID, and PSO-Fuzzy PID control methods, which were carried out on the MATLAB/Simulink simulation platform. Finally, the litchi load test was conducted to further verify the control performance of the PSO-Fuzzy PID method, where the litchi variety was “Jinggang Hongnuo”. The simulation results showed that the overshoot of the optimized control system was reduced by 93.5% and 42.9% compared with the traditional PID control and Fuzzy PID, and the regulation time was shortened by 46% and 31%, which indicates a better control effect. The test results showed that PSO-Fuzzy PID showed better system stability and accuracy than the fuzzy PID control method in different processing temperature and processing time for litchi’s hot vapor treatment. The maximum temperature difference decreased by 0.56 °C on average and the interference adjustment time was reduced by 7.5 s on average. PSO-Fuzzy PID has a good control effect on the temperature of litchi hot vapor treatment, which can provide a reference for model building and temperature control of other similar fruit and vegetable heat treatment equipment.

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.