Proportion Integration Differentiation Control Algorithm Research Articles

Suppression of Torque Ripple in Switched Reluctance Motors Which is Based on Synchronization Technology

The double salient pole structure of Switched Reluctance Motor (SRM) makes its electromagnetic field exist nonlinear saturation characteristics, resulting in its large torque pulsation in operation, so it is difficult to achieve speed regulation smoothly by traditional control methods. In view of this problem, a sliding mode control strategy which is based on synchronous transmission technology was proposed.Firstly, the basic structure of switched reluctance motor was analyzed, and the mathematical model of mechanical motion of switched reluctance motor was established. Secondly, an improved sliding mode controller which is based on synchronous signal transmission technology was designed by analyzing the reason of large torque ripple of switched reluctance motor, and the stability of the system was proved. Finally, simulation is used to verify the effectiveness of the control strategy.Compared with the traditional PID (Proportional Integral Differential) control algorithm, this control technology not only suppresses the SRM torque ripple effectively , but also makes the sliding mode controller output the precise target electromagnetic torque quickly by increasing the control variables. The results of research indicate that this design can not only restrain the torque ripple effectively, but also adjust the convergence speed and overshoot of the controller by adjusting the design parameters.

Application of a Neural Network on Steam Drum Pressure Control in a Fixed-Bed Reactor for Exothermic Reactions

The control of steam drums, used to remove heat from Fischer–Tropsch synthesis or diethyl oxalate hydrogenation, is confronted with a challenge on controlling quality. The traditional proportional–integral–differential (PID) controllers with fixed parameters are dissatisfying upon deployment. The backward-propagation neural network (BPNN) self-tuning PID control algorithm was thus developed and implemented via a Python and KINGVIEW software combination. Application experiments showed that, in both setpoint control and step change control of the steam drum pressure, static deviation and the maximum error were less with the BPNN self-tuning PID controller, in comparison to the conventional PID controller. Moreover, it seemed that certain adaptations occurred to the nonlinear change in the reaction system, revealing that it was superior to the traditional PID controller. It is shown that the backward-propagation neural network will improve the control quality in boiling water drum systems for exothermic reactions. It can be predicted that the backward-propagation neural network is qualified for process condition control in the chemical industry.

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.

Research on unmanned navigation and PID control method for aircraft tractor

Unmanned navigation and control systems are gaining traction in various industries, including aviation. The related issues of control systems are also hot topics in research. This paper comprehensively studies the application of unmanned navigation and the implementation of proportional integral differential (PID) control methods for aircraft tractors. Aiming at the characteristics of large deviation of turning trajectory angle error and long lag time in the process of an unmanned tractor, the dynamic characteristics and state equation of the unmanned tractor are established by the PID method, and the motion equation is established by the Cartesian coordinate system. Finally, the simulation results show that the proposed method has good dynamic stability and rapidity. Compared with proportional control or integral control alone, the PID control system has better stability and a faster frequency response, which can be obtained that the PID control algorithm can effectively improve the work efficiency of the unmanned tractor and reduce its instability and slow response.

Study of an improved single neuron PID control algorithm in the Tokamak plasma density control system

Tokamak is an important device for controlled nuclear fusion research. The plasma electronic density control system (PEDCS) is an important system for controlling the Tokamak discharge process, which should be of high stability, rapidity, and accuracy. Gas seeding systems are widely used in many Tokamak devices to achieve plasma electronic density control. According to the mechanism model analysis for the plasma electronic density object, an adapted single neuron proportion integration differentiation (PID) control algorithm with the radial basis function (RBF) neural network tuning is studied. The principle and the implementation of the intelligent control algorithm are described in detail in this paper. The intelligent controller enables the system to optimize the PID parameters online according to the density state in the discharge process. The experimental results show that the adapted algorithm achieves a good control effect and also improves the control performance. The proposed method provides a useful reference for Tokamak devices and other similar control systems.

Magnetic Levitation Belt Conveyor Control System Based on Multi-Sensor Fusion

Belt conveyors are critical in coal transportation systems; however, they are supported by rollers, which incur high energy costs. Magnetic levitation support is adopted. The measurement of a magnetic levitation air gap is important, and a displacement sensor is usually used in real-time. To address the problems of poor linearity and low measurement stability of single displacement sensors in magnetic levitation systems, a magnetic levitation support system based on multi-sensor data fusion is proposed. First, an industrial camera sensor is used to collect images, and the suspended air gap is obtained through image processing. Second, using an extended Kalman filter, the photoelectric position sensor and industrial camera sensor are fused to the suspension air gap value in the process of suspension. The electromagnet current is controlled by the PID (proportion integration differentiation) control algorithm, and experiments are carried out. Experimental results showed that, compared with an estimation of the suspension state of two sensors before and after EKF fusion, the root mean square error is reduced by 0.0597 and 0.0081, respectively, compared with the measurement value of a single sensor. Moreover, the fusion data were more robust, thereby meeting the requirements of magnetic levitation control.

Vehicle-Mounted Solar Occultation Flux Fourier Transform Infrared Spectrometer and Its Remote Sensing Application.

For the demand of rapid monitoring of pollution gas disorganized emissions in industrial parks, this paper studies the solar fast tracker system of vehicle-mounted SOF-FTIR (Solar Occultation Flux Fourier Transform Infrared Spectroscopy) system, where the spectrometer directly measures the broadband absorption spectrum of solar radiation light. A fast portable solar tracking system based on PSD (position sensitive detector) is designed, the mathematical model of solar spot position on the PSD surface source is established, and the optimal optical design parameters are simulated using the model. The dead-zone integral separation PID (Proportion Integration Differentiation) control algorithm is used to track the trajectory of the solar, and the light spot position model is used to nonlinearly compensate the output of PID control so that the PID controller has the same control precision and response speed in different error areas. Experimental analysis of the solar tracking performance of the vehicle-mounted SOF-FTIR under static and dynamic conditions, as well as the spectral effects on the measurements under static vehicle, constant speed, and turning driving conditions. The remote sensing application experiment of vehicle-mounted SOF-FTIR pollution gas emission flux was carried out in a tire factory in Hefei City, Anhui Province. A vehicle-mounted SOF-FTIR system realized the qualitative and quantitative analysis of the pollution gas at the boundary of the tire plant and calculated the flux of each component pollution gas. The emission flux of pollution gas was highly consistent with the actual pollution distribution of the tire plant. The results show that the positioning accuracy of PSD in the vehicle tracking experiment can also meet SOF-FTIR requirements for solar tracking. The remote sensing system will be useful in the field of atmospheric environment monitoring, and the mobile monitoring of regional pollutant gases based on solar infrared spectroscopy has application value.

Adaptive Fuzzy Control of Sludge Conditioning and Pressing

The basic technological process of sludge conditioning and pressing is introduced in this paper. The purpose is to set up an adaptive fuzzy control algorithm to treat the problems of conditioning, mixing, feeding, and pressing control, so that the equipment used to process the sludge can run on demand. The algorithm is verified by MATLAB simulation. The results show that the control accuracy of the fuzzy adaptive control system is significantly improved compared with the original PID (proportion integration differentiation) control algorithm. The upper computer program is compiled with VB (visual basic) and applied to industry control to save the energy consumption.

Research on PID Position Control of a Hydraulic Servo System Based on Kalman Genetic Optimization

With the wide application of hydraulic servo technology in control systems, the requirement of hydraulic servo position control performance is greater and greater. In order to solve the problems of slow response, poor precision, and weak anti-interference ability in hydraulic servo position controls, a Kalman genetic optimization PID controller is designed. Firstly, aiming at the nonlinear problems such as internal leakage and oil compressibility in the hydraulic servo system, the mathematical model of the hydraulic servo system is established. By analyzing the working characteristics of the servo valve and hydraulic cylinder in the hydraulic servo system, the parameters in the mathematical model are determined. Secondly, a genetic algorithm is used to search the optimal proportional integral differential (PID) controller gain of the hydraulic servo system to realize the accurate control of valve-controlled hydraulic cylinder displacement in the hydraulic servo system. Under the positioning benchmark of step signal and sine wave signal, the PID algorithm and the genetic optimized PID algorithm are compared in the system simulation model established by Simulink. Finally, to solve the amplitude fluctuations caused by the GA optimized PID and reduce the influence of external disturbances, a Kalman filtering algorithm is added to the hydraulic servo system to reduce the amplitude fluctuations and the influence of external disturbances on the system. The simulation results show that the designed Kalman genetic optimization PID controller can be better applied to the position control of the hydraulic servo system. Compared with the traditional PID control algorithm, the PID algorithm optimized by genetic algorithm improves the system’s response speed and control accuracy; the Kalman filter is a good solution for the amplitude fluctuations caused by GA-optimized PID that reduces the influence of external disturbances on the hydraulic servo system.

Automatic Driving Research of Cloud Model High-Speed Train based on Genetic Algorithm Optimization

High-speed train is a complex nonlinear system with strong coupling, which is easily disturbed by uncertain factors. The traditional model of single-mass point train does not consider the influence of train length and interaction force between trains. Given that high-speed train is vulnerable to time-varying disturbance in the complex and changeable external environment, and the traditional single-mass point train model does not consider the train length and interaction force between vehicles, a genetic algorithm (GA) optimised cloud model proportion integration differentiation (PID) speed controller based on a rigid multi-mass point model was designed. The numerical features of the cloud model are first optimized by the global optimization capability of the GA, then the cloud model reasoner corrects the parameters of the PID controller in real time through the corresponding reasoning rules. Moreover, the PID controller with adjustable parameters completes the control output of the speed controller. The rigid multi-mass point model of the train is established, and CRH3 train is selected to simulate the selected line to prove the feasibility of the cloud model PID control algorithm based on GA optimisation. Under the same conditions, PID and fuzzy PID controllers are set for speed-tracking performance comparison, which verifies that the cloud model PID controller based on GA optimisation has small speed-tracking error and strong robustness. It can more effectively reduce the influence of interference caused by uncertain factors on the automatic driving operation speed controller of high-speed train and has better control effect.

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 of Bridge Crane Control System Using Fuzzy PID Control and Speed Control of Frequency Converter

Objective: As a carrier, bridge cranes are widely used in port cargo handling. Due to the structure of the crane itself, the load inevitably produces displacement deviation and lifting weight swing. Therefore, the optimization investigation of the bridge crane control system is carried out to guarantee the fast and secure operation of the crane. Method: According to the characteristics of bridge cranes, combined with the experience of on-site operating staff, the fuzzy rules that meet the working conditions of the site are designed by using the advantages of fuzzy PID (Proportion-Integration-Differentiation) control algorithm without relying on an accurate mathematical model, to set the parameters of the fuzzy PID controller. The speed control technique of frequency converter is adopted to design the system to improve the safety of the overall system. Results: The results show that the adjustment time of the swing angle of crane weight of the traditional PID control requires 6.79s, and the maximum swing angle of crane weight is 0.039rad, and the adjustment time under the fuzzy control is 4.89s, and the swing angle of crane weight is 0.013rad. The fuzzy PID controller surpasses the traditional PID controller in the positioning accuracy. It is not sensitive to changes in the internal parameters of the system. Also, it has strong adaptability to changes in operating conditions. The swing angle of crane weight is smaller, which further improves the robustness. The vector frequency conversion speed control has a more precise control effect on the speed and running track of the operating mechanism of the bridge crane. This method can be applied to the automatic operation mechanism of bridge cranes and achieve good control performance. Conclusion: The conducted optimization investigation of the bridge crane control system makes the control system flexible, simple and robust, providing important theoretical support for related explorations.

Active-Passive Combined Control System in Crane Type for Heave Compensation

For the purpose of researching the displacement control system of heave compensation for offshore drilling platform, a set of crane type active and passive combined heave compensation device are designed on the basis of the similarity principle. As it known that platform heaving will be caused by the wind, sea wave and ocean current when conducting the drilling operations on the offshore drilling platform, which then will disturb the drilling operation. Therefore, the compensation device must be adopted to keep the vertical relative motion between the drill string system and the drilling platform to zero during the operation. Meanwhile, to improve the real-time performance of the heave compensation, the control system is optimized by establishing the simulation model of the active-passive combined crane, and LS-SVM (Least Squares Support Vector Machine) is improved by the artificial immune algorithm to predict the motion trend of offshore platforms. Eventually, in order to acquire the best control scheme, the Proportion Integration Differentiation (PID), fuzzy PID, BP neural network PID control method are utilized to carry out the simulation analysis, and the BP neural network PID control is found to be the optimum. Experiments showed that after using the BP neural network PID control algorithm, the displacement compensation rate of hook for active-passive combined crane device is more than 90%, the performance of the heave compensation is better, and the control is in time.

Damping characteristics of magneto‐rheological fluid absorber based on closed‐loop proportion integration differentiation control

AbstractAiming at investigating the working of magneto‐rheological fluid absorber, a test system used for detecting the damping characteristics of the magneto‐rheological fluid absorber was designed. The test system included sensors, data acquisition card, and so on. The vibration signals were detected by the sensors, put into LabVIEW data acquisition system, and then collected through the processing of closed‐loop Proportion Integration Differentiation control algorithm. Considering that the feedback current could be output to the magneto‐rheological fluid absorber by the data acquisition card, thus the damping force could be changed. The test results showed that the amplitude of the structure vibration response was obviously reduced through the closed‐loop Proportion Integration Differentiation control algorithm, and the control damping characteristics of the magneto‐rheological fluid absorber was improved.

3D dynamic simulation of heat transfer and melt flow in an inductively heated crystallization furnace for mc-silicon with PID temperature control

A heat transfer model of a semi-industrial induction furnace has been build, using a 3D finite element model in order to analyze the entire process cycle, based on the heating, melting, solidification and cooling phases of a multi-crystalline square ingot. In the modeling of the entire process, heat transfer phenomena such as radiation and conduction in the furnace have been taken into account. A PID (Proportional Integral Differential) control algorithm has been implemented into the model for adjusting the power input in the heaters, so that the heater temperature is kept at prescribed time-varying values. The furnace model and the PID control algorithm are validated by temperature measurements from a crystallization experiment. Subsequently the validated model was used to investigate the melt flow field and its impact on the solid–liquid interface shape.

PID Algorithm Applied in the Refrigeration of EMBCCD

For EMBCCD (Electron Multiplying Back Illuminated CCD) which is low light level imaging device, temperature has an important effect on the imaging performance. When EMBCCD enhances signal, it also amplifies the noise of the dark current. The most effective means of cooling chip is to reduce the noise of the dark current. When the EMBCCD is in the stable environment in low temperature, the signal to noise ratio can be improved. For this problem, the article describes the PID (Proportion Integration Differentiation) control algorithm. The unit step response curve for the controlled object is obtained by absolute refrigeration. As the system unit step response curve meets the requirements of the S-shaped curve, the Ziegler-Nichols method is used and the parameter is obtained from the S-shaped curve. The deviation between the actual temperature and set temperature values according to the proportional, integral and differential linear combination to form the control volume, incremental PID control algorithm. Put the parameters into the algorithm to control the refrigeration temperature. After that, the initial control curve is gotten. By repeated testing, we can get more appropriate parameters, and the temperature can be controlled at the target temperature. And the temperature curve is in the flat, almost with no fluctuation. The corresponding PID parameters and processing strategies are gotten from the debugging process. At last, calculate PID parameters and processing strategies, and implement the PID algorithm, as well as the numerical quantification of the specific applications the basic method are obtained. The experimental results show that the algorithm can make the temperature at 10.5°Cwith error0.5°C.

Physiological control of an in-series connected pulsatile VAD: numerical simulation study

This paper investigates ventricular assist device (VAD)-assisted cardiovascular dynamics under proportion–integration–differentiation (PID) feedback control. Previously, we have studied the cardiovascular responses under the support of an in-series connected reciprocating-valve VAD through numerical simulation, and no feedback control was applied in the VAD. In this research, we explore the contribution of the VAD control on the circulatory dynamics assisted by the reciprocating-valve VAD, in response to the changing physiological conditions. The classical PID control algorithm is implemented to regulate the VAD stroke beat-to-beat, based on the error signal between the expected and the realistic mean aortic pressures. Simulation results show that under the PID VAD control, physiological variables such as left atrial, ventricular and systemic arterial pressures, cardiac output and ventricular volumes are satisfactorily maintained in the physiological ranges. With the online PID feedback control, operation of the reciprocating-valve VAD can be satisfactorily regulated to accommodate metabolic requirements under various physiological conditions including normal resting and exercise situations.

Microgyroscope Temperature Effects and Compensation-Control Methods

In the analysis of the effects of temperature on the performance of microgyroscopes, it is found that the resonant frequency of the microgyroscope decreases linearly as the temperature increases, and the quality factor changes drastically at low temperatures. Moreover, the zero bias changes greatly with temperature variations. To reduce the temperature effects on the microgyroscope, temperature compensation-control methods are proposed. In the first place, a BP (Back Propagation) neural network and polynomial fitting are utilized for building the temperature model of the microgyroscope. Considering the simplicity and real-time requirements, piecewise polynomial fitting is applied in the temperature compensation system. Then, an integral-separated PID (Proportion Integration Differentiation) control algorithm is adopted in the temperature control system, which can stabilize the temperature inside the microgyrocope in pursuing its optimal performance. Experimental results reveal that the combination of microgyroscope temperature compensation and control methods is both realizable and effective in a miniaturized microgyroscope prototype.

Nonlinear decoupling PID control using neural networks and multiple models

For a class of complex industrial processes with strong nonlinearity, serious coupling and uncertainty, a nonlinear decoupling proportional-integral-differential (PID) controller is proposed, which consists of a traditional PID controller, a decoupling compensator and a feedforward compensator for the unmodeled dynamics. The parameters of such controller is selected based on the generalized minimum variance control law. The unmodeled dynamics is estimated and compensated by neural networks, a switching mechanism is introduced to improve tracking performance, then a nonlinear decoupling PID control algorithm is proposed. All signals in such switching system are globally bounded and the tracking error is convergent. Simulations show effectiveness of the algorithm.