Fuzzy Proportion Integration Differentiation Control 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.

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.

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%.

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.

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.

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.

Research on Fuzzy Control Method of Automobile Antilock Braking System

The automobile Antilock Braking System (ABS) can prevent the wheel from locking by automatically adjusting the brake pressure at a high speed during emergency braking. While improving the braking effect of the car, it can also keep the steering ability of the car and ensure the safety of passengers. The automobile braking process has a strong nonlinearity and uncertainty, so exploring a simple and reliable control strategy is the focus of current research. Based on this, this paper proposes a variable-domain fuzzy proportional integral differential (PID) control strategy using a particle swarm optimization (PSO) algorithm to iteratively find the optimal theoretical domain. First, the PSO strategy is used to obtain the optimal regulation parameters. Then, the dynamic antiinterference ability of the control system is guaranteed by the variable theory domain fuzzy PID control, and the PID parameters and variable theory domain expansion factor are optimized by the PSO to increase the utilization degree of fuzzy rules initially set. Finally, compared with the traditional control strategy, the simulation and real vehicle test prove that the proposed system can significantly improve the control accuracy of abs. The proposed system has the advantages of small overshoot, short adjustment time, strong antiinterference ability, and practicability, which greatly improves the tracking performance of the ABS.

Optimized design of battery pole control system based on dual-chip architecture.

At present, the global demand for lithium batteries is still in a high growth state, and the traditional lithium battery pole mill control system is still dominated by ARM (Artificial Intelligence Enhanced Computing), DSP (Digital Signal Processing), and other single-chip control methods. There are problems such as poor anti-interference ability and insufficient real-time online analysis of production data. This paper adopts the dual-chip control system architecture based on "ARM+DSP", starting from the mechanical characteristics and operating signal features of the pole mill. The hardware system adopts a three-unit joint control hardware structure, which separates the control unit from the data processing unit and improves the operation of the system. The software system adopts fuzzy PID algorithm to realize deflection control and tension control, and verifies that the Fuzzy PID (Proportion Integration Differentiation) control algorithm can effectively improve the anti-interference ability of the deflection system and tension system. The results show that the data loss rate is low with the SPI communication between DSP and ARM. The tension error of the "ARM+DSP" control system does not exceed 5%, and the deviation of the correction band is within ±4mm. The dedicated dual-chip hardware architecture effectively improves the robustness and operation efficiency of the pole mill, solves the problem of low tension control accuracy, and provides a theoretical basis for the application of the dual-roll mill.

Optimization and Realization of the Coordination Control Strategy for Extended Range Electric Vehicle

This paper designed a fuzzy adaptive proportional integral differential (PID) control algorithm to optimize the overshoot of speed and torque, fuel consumption and exhaust emissions of the traditional PID control strategy in the process of working condition switching of an extended range electric vehicle. The simulation was carried out in Matlab/Simulink, and the optimization of the control strategy was verified by a bench test. The results show that the fuzzy adaptive PID control strategy effectively reduced the speed overshoot in the process of working condition switching compared with the traditional PID control strategy. The bench test proved that the fuzzy adaptive PID control strategy could effectively optimize the switching process, especially in the speed and torque reduction switching process, and the speed overshoot rate of the fuzzy PID control was greatly reduced to 0.7%, far less than that of the traditional PID control with 6.6%, while the torque overshoot rate was within 0.8%. Additionally, the fuzzy adaptive PID control could effectively reduce the fuel consumption, especially in the switching process of increasing the speed and torque, where the fuel consumption of the fuzzy adaptive PID control was 2.1% and 0.5% lower than that of the traditional PID control, respectively. Additionally, the fuzzy adaptive PID control could also reduce the particulate emissions, especially in the process of increasing the speed and torque, where the number of particles of the fuzzy PID control was 11% and 19% less than that of the traditional PID control, respectively. However, the NOx emissions based on the fuzzy PID control were slightly higher than those of the traditional PID control due to the smooth operation and improved combustion.

Design of three-stage start-up optimal strategy based on fuzzy fractional-order proportion integration differentiation controller of pumped storage unit under low water head

When the pumped storage unit (PSU) operates in the low water heads, it is easy to enter the ‘S’ characteristic area, which leads to the chaotic phenomenon of the hydraulic transient of the PSU and makes the adjustment and control of the PSU under the no-load start-up condition extremely difficult. In this paper, a strategy of integrating three-stage guide vane opening law and fuzzy fractional-order proportion integration differentiation (PID) controller is proposed for the low water head start-up condition of PSU. Firstly, based on the mathematical model of pumped storage unit regulation system (PSURS), the mapping relationship between water head, no-load guide vane opening, the frequency switch point of the controller, and the number of rotational speed fluctuations is analyzed. Then a three-stage start-up optimal strategy based on a fuzzy fractional-order PID controller is proposed. On this basis, the multi-objective optimization method of control parameters for the three-stage start-up strategy is proposed by introducing the rotational speed overshoot and integrated time and absolute error (ITAE) index. Furthermore, to achieve efficient tuning of the high-dimensional control parameters for the multi-objective optimization method, a multi-objective grey wolf algorithm is proposed by introducing quantum space theory. Finally, numerical experiments are carried out to compare the start-up transition process under the three-stage start-up optimal strategy and conventional standard mode. The results indicate that compared with the conventional method, the proposed method has significant advantages in improving the dynamic stability and control quality of the low water head start-up of the PSU. The fluctuation times of the rotational speed of the PSU optimized and regulated by the novel method is 0 times, the regulation time is reduced by 34 s at most compared with the conventional method, and the steady-state error is only 0.003.

Research on the cloud computing fuzzy proportion integration differentiation control strategy for permanent-magnet homopolar motor with salient pole solid rotor used on new-energy vehicle

Due to the complex operating conditions of new-energy vehicles, traditional driving motor system has been unable to meet their high-quality requirements for performance. To solve this problem, permanent-magnet homopolar motor with salient pole solid rotor was selected as the research object in this paper, and based on the analysis of its mechanism and mathematical model, a cloud computing fuzzy Proportion Integration Differentiation (PID) control strategy was proposed and applied to the speed control of homopolar motor system. By system modeling on simulation platform, the result showed that cloud computing fuzzy PID control strategy obviously improved the responsiveness, control precision and stability of homopolar motor system comparing with PID control strategy. This verified the validity and applicability of the design and provided a reference and theoretical basis for improving the control quality of driving motor system used by new-energy vehicles (NEV).

Control System of Liquid Fertilizer Variable-Rate Fertilization Based on Beetle Antennae Search Algorithm

In order to solve the problems of low precision of variable-rate fertilization and uneven fertilization flow of field liquid fertilizer applicator, a control system of variable-rate fertilization of liquid fertilizer based on beetle antennae search algorithm was proposed. First of all, this study established a mathematical model for the variable-rate fertilization control system of liquid fertilizer. Then, according to the control requirements, the search algorithm is used to optimize the three parameters of Proportion Integration Differentiation (PID). Finally, the response time and overshoot of the system are analyzed by software simulation, and the PID control based on beetle antennae search algorithm is compared and analyzed with fuzzy PID control and traditional PID control. The control effect of the control system is verified by a bench test. The results show that the actual response time of the variable-rate fertilization control system based on the beetle antennae search algorithm can reach 2 s, and the average relative error can reach 1.27%. Therefore, the control system of this study can achieve a better control effect, and the control method of this study provides a feasible scheme for the study of variable-rate fertilization.

Electrothermal response optimization of nozzle structure for multi-material rapid prototyping based on fuzzy adaptive control

PurposeMost rapid prototyping (RP) relies on energy fields to handle materials, among which electricity has been much more utilized, resulting in distinctive responsiveness of non-linear, overshoot, variable inertia, etc. The purpose of this paper is to eliminate the drawbacks of array nozzle clogging, stringing, melt sagging, particularly in multi-material RP, by focusing on the electrothermal response so as to adaptively distribute thermal more accurate, rapid and balanced.Design/methodology/approachThis paper presents an electrothermal response optimization method of nozzle structure for multi-material RP based on fuzzy adaptive control (FAC). The structural, physical and control model are successively logically built. The fractional order electrothermal model is identified by Riemann Liouville fractional differential equation, using the bisection method to approximate the physical model via least square method to minimize residual sum of squares. The FAC is thereafter implemented by defining fuzzy proportion integration differentiation control rules and fuzzy membership functions for fuzzy inference and defuzzification.FindingsThe transient thermodynamic and structural statics, as well as flow field analysis, are conducted. The response time, mean temperature difference and thermal deformation can be found using thermal-solid coupling finite element analysis. In physical experimental research, temperature change, together with material extrusion loading, were measured. Both numerical and physical studies have revealed findings that the electrothermal responsiveness varies with the three-dimensional structure, materials and energy sources, which can be optimized by FAC.Originality/valueThe proposed FAC provides an optimization method for extrusion-based multi-material RP between the balance of thermal response and energy efficiency through fulfilling potential of the hardware configuration. The originality may be widely adopted alongside increasing requirements on high quality and high efficiency RP.

Fuzzy-PID Strategy Based on PSO Optimization for pH Control in Water and Fertilizer Integration

In the process of crop growth, irrational fertilizer application methods have caused waste of fertilizer resources and water, as well as damaged the soil’s structure. To puzzle the above problems out, the research constructs the model of water-fertilizer machine by gathering relevant parameters in the field. Considering the system’s plenty of defects and combining it with the MATLAB/simulink system, such as non-linearity, time-varying, large inertia, uncertain mathematical model and severe lag, a fuzzy proportional integral differential control based on particle swarm optimization is proposed in this paper for water and fertilizer integration system. Primarily, the research is done for precision fertilization control of fertilizer integration system and water, and the parameters of fuzzy proportional integral differential gain that schedules controllers are optimized through a particle swarm algorithm. The effectiveness of the suggested controller has been validated by comparing with the control algorithms (proportional integral differential control, fuzzy proportional integral differential control) commonly applied in current fertilizer application systems. Simulation experiments for this research are devised through MATLAB/Simulink simulation platform. Significant improvement in the system’s tuning capabilities by incorporating particle swarm algorithm in the hysteretic non-linear system. Eventually, four control algorithms are experimentally validated in this research at different pH values through Experiments čáre designed in the experimental field. The outcomes demonstrate that the control algorithm in this paper possesses better regulation effect, smaller overshoot, excellent stability and more inadequate rising steady state time compared with the previous controls, which can enables precise control of the fertiliser system.