Single Neuron PID Research Articles

An Asynchronous Motor Soft-Starter with Discrete Variable Frequency Technology

This paper analysis the control principle of the asynchronous motor soft-start with discrete variable frequency technology．A simulation model for the asynchronous motor soft-starter using discrete variable frequency technology is built based on MATLAB SIMULINK/POWER SYSTEM．In start process, positive sequence sub - frequency such as 13、10、7、4 discrete variable frequency is used in the soft-starter to simplified power circuit and its control. While on the power frequency time slice ,a single neuron PID algorithm is used to limit the current. The simulation results verify the asynchronous motor soft-starter with discrete variable frequency technology not only reduces the initial firing current but also has bigger start torque, which has a faster start speed compared with the normal soft starter with current limit starting control technology.

Application and Simulation of Single Neuron PID in IBVS

Visual servo control is one of the central issues in the research field of intelligent robot. In order to obtain more effective information from the environment, visual sensor is used in robot control. This paper mainly studies the application of intelligent algorithm in visual controller. Through the simulation comparison between Single neuron PID and Fuzzy adaptive PID which are introduced to IBVS, we can find that the proposed method could get better effect in convergence and fast performance.

Simulation of Gauge Control System Based on Single Neuron PID

This paper presents that single neuron PID is applied to the Gauge gap control system, and compares it with PID. Simulation results indicate that control accuracy of Single neuron PID has faster response, stronger anti-interference ability and better robustness.

Research on Dissolved Oxygen Intelligent Control System in Waste Water Treatment

By combining with the research hot spot of intelligent control, the oxygen fuzzy-single neuron PID compound controller is designed based on control increment. It contains the advantages of fuzzy controller and the single neuron PID controller. The problem of causing the oscillation in switching two kinds of controllers is solved. The results of the dissolved oxygen fuzzy-single neurons PID controller and fuzzy controller simulation are compared. The results show that the control effect of dissolved oxygen is more ideal. Thus the algorithm is effective and feasible.

Adaptive Single Neuron PID Control with Fuzzy and Self-Tuning in Networked Control Systems

As the varied time-delay caused by the network in NCSs, the analysis and design of NCSs become more difficult. In this paper, the single neuron PID control algorithm is modified by adjusting adaptively the scale factor and learning rate of the neuron with fuzzy rulers subject to NCSs. It is observed from the simulation that the proposed method has a well dynamic and adaptive performance.

Single Neuron PID Adaptive Control for BLDCM Based on Algebraic Algorithm

The brushless DC motor control system often adopts the classic PID control, the advantages of which are as follows: simple to control, easy to adjust the parameter and a certain degree of control precision. But it relies on accurate mathematical model. The permanent magnet brushless DC motor control system is a multi-variable and nonlinear. As to the deficiencies of the classic PID control method, this thesis proposes a method called artificial neural network PID adaptive control method, which is based on algebraic algorithm and overcomes the shortcomings such as the slow convergence of BP algorithm, easy to trap in local minimum, and etc.

Design and Research of Energy Saving Control System for Heating Network

In the central heating system, because the controlled object has the characteristics of time-varying, nonlinear, strong coupling and big lag. The traditional single loop model and the conventional PID control algorithm are difficult to react the mechanism and meet the control requirements. Therefore a model of multiplex heating network is established. By comparing the single neuron PID control algorithm with the traditional PID control algorithm, the results show that the single neuron PID control algorithm has a better control effect.

Single Neuron PID Sliding Mode Parallel Compound Control for Alternating Current Servo System

Considering changes, strong interference of load torque and moment of inertia for alternating current position servo system, a single neuron PID and sliding mode parallel compound control strategy is proposed. The parallel architecture that consists of a sliding mode variable structure controller of the position loop and the single neuron PID controller is designed. Sliding mode control inhibits the parameter perturbations and load disturbance, single neuron PID control realizes the online adjustments of the proportional, integral, differential parameters of traditional PID controller. The simulation results show that the designed parallel compound controller can guarantee static and dynamic system performance.

Research on Single Neuron Adaptive PID Control

As a branch of the intelligent control, neural networks is applied in control more and more widely, the single neuron adaptive PID control algorithm is studied in this paper, and the program is written by MATLAB, the common object of single neuron adaptive PID is simulated, and the effect of single neuron adaptive PID control parameters on control effect is analyzed, experimental results show that the single neuron PID control has more obvious advantages than general PID control.

Design and Achievement of the Erection Test System Controlled by Single Neuron PID Controller

To overcome the nonlinear and slow-varying factor in hydraulic system and the uneasy-building model of erection system, the single neuron self-adaptive PID controller is designed. To solve the conflict between the speed and the stability in erecting, The erecting system based on virtual instrument is designed. The system uses a data acquisition card simultaneously to acquire the test data and the feedback signal and control the hydraulic system, utilizes the module method and the technique of mixed-program between LabWindows/CVI and Matlab based on ActiveX technique to design application software. The results of experiment shows that the system with high precision of testing and controlling is operated conveniently and universally and can satisfy the requirement of the erection system.

PID Controll of Single Neuron for Switched Reluctance Motors Based on RBF Neural Network

This paper presents a novel approach of single neuron PID control for switched reluctance motors based on RBF neural network on-line identification. The method is adjusted to the nonlinearity of switched reluctance motors, and use the single neurons capable of self-learning and self-adaption to form the single neuron adaptive controller of switched reluctance motors. It not only has simple structure and strong robustness, but but can adapt to environmental changes. Also we construct a RBF network system to identify the system online, and to build its online reference model, using a single neuron controller to achieve self-learning of its parameters, in order to achieve their online adjustment, and to obtain better control effect.

Modeling and Analysis of Electro Hydraulic Brake System Based on AMESim/Matlab

Electro Hydraulic Brake (EHB) system plays an important role for improving vehicle active safety. In this paper, the dynamic behavior of EHB system is discussed. From its working principle, a dynamic model of quarter EHB system is established based on AMESim and Matlab. To analyze performance of the proposed EHB system, the strategy of Single-Neuron PID regulation is presented and a typical numerical case is given. The results show that this strategy can obtain good performance and proper parameters can improve the dynamic characteristics of EHB system effectively.

Control System Development of Grating Ruling Engine Based on MATLAB/Simulink

This paper presents the control system development of a diffraction grating ruling engine. The grating ruling engine which consists of a ruling subsystem and a indexing subsystem including a outside stage (coarse stage) and a inside stage (fine stage) and which realize groove densities of 6000 grooves/mm, maximum ruled area of 300 (groove length) × 300 (width) and nanometer level positioning error has been designed. The ultra-precision requirement demands not only precision mechanical structure but also advanced control system, both of whose development take long time. To shorten development period and reduce the costs, concurrent control system development combined with MATLAB/Simulink is implemented. The control software constructs graphic user interface to show the virtual grating ruling engine and provides interface with Simulink module. Various control algorithms, which are written in C language to facilitate future code porting, is verified in Simulink. Finally, single neuron PID control algorithm is adopted. The control software starts Simulink simulation and emulated data which is transferred from Simulink to software is used to control virtual engine to move corresponding. After the completion of simulation, the algorithm is ported into software to control the real ruling engine. The simulation and experimental results demonstrate that combined simulation is an effective approach and that the positional accuracy has been readily achieved within 9.2 nm.

Application of Single Neuron PID in the Control of Loader Working Device

In order to improve the maneuverability and the automation of loader working device, this paper establishes it’s Electro-Hydraulic Servo Position System mathematical model based on the working principle of its Hydraulic System, and designs a position controller for the loader working device using Single Neuron PID Control Algorithm. The tests of simulation were carried out on the platform of Matlab/Simulink. The simulation results show that Single Neuron PID Control Algorithm can improve the system control effect, process its real-time and static-dynamic characteristics with a simple controller.

Study on Spindle Servo Control of Four-Axis Vertical CNC Machining Center

This paper studies swinging rule of the fourth spindle in Four-axis vertical CNC machining center relying on the vertical axis head. Through current regulator, the speed regulator and improved single neuron PID speed controller the servo control system model is realized. Test results show that good performance is reached according to design of servo control system. The best is to read these instructions and follow the outline of this text.

Immune Single Neuron Control of Level in Spherical Vessel

To solve the problem of nonlinear characteristics of spherical vessels, an immune single neuron PID controller (ISNPID) is proposed in this paper. The ISNPID controller combines the immune principle with single neuron PID, it can improve the learning rate and reduce the adjusting time of single neuron PID controller. The interim process of the spherical vessel system could also be optimized. Good control performance of nonlinear system could be realized according to the on-line self-adjusting gain by the ISNPID controller. Simulation result shows that ISNPID controller has strong adaptability and robustness. Efficiency and applicability of the ISNPID controller could be proved in practical application.

Design of Networked Control Systems Based on Single-Neuron PID

With the development of control theory and communication technology, networked control systems have got more and more attention. The traditional PID controller is no longer suitable to meet performance requirements of time-delay control systems. And communication network is introduced into control systems that makes systems arise some problems, such as time delays, data dropout and so on. These problems affect the control performance badly. A model based on the single-neuron PID controller for networked control systems is proposed to improve the control performance. The design method for controller is based on neural network, traditional PID controller and Smith predictive control. The simulation results represent the model we proposed has better control performance.

Research on Multidisciplinary Modeling and Robust Control for Electric Power Steering System

The first of all, the multidisciplinary modeling and analyzing for the electric power steering system (EPS) is made. The design method based on the model is proposed and the EPS control system is analyzed. On the basis of deeply observing and studying S/T singular value curves, a robust control algorithm of back-calculation is put forward. After analyzing the deficiency in the single-loop control structure of the EPS, the dual-loop control structure of the EPS is developed. The inner loop controller is designed using the single neuron PID algorithm, and the new robust control algorithm is proposed to design the outer loop controller. The simulation results in the frequency-domain S/T curves of the EPS system and the hand force step input of the steering wheel show that the designed controller is simple and effective, and has good robust stability and robust performance.

Application of Single Neuron PID Control Based on Variable Scale Algorithm in Tar-Ammonia Separation

In the process of recycling chemical product in coking object, ammonia and tar were indispensable both metallurgy and agriculture, so the control of separation process for tar-ammonia was one of the most important control problems. Due to the density difference between the tar and ammonia was greater, easier to separate, the control method based on PID was used in field at present. But the control effect of traditional PID was not good because of environment change and fluctuation in material composition. Separation process for tar-ammonia was analyzed firstly, in view of the shortcoming of traditional PID control algorithm, single neuron PID control algorithm based on variable scale method was adopted through using optimization method. Detailed algorithm steps were designed and applied to tar-ammonia separation system. Simulation results show that by comparison with traditional PID algorithm, the algorithm have the following advantages: faster learning speed, shorter adjusted time and good convergence performance.

Universal learning network and its application for nonlinear system with long time delay

This paper discusses universal learning network (ULN) and its application to control a class of nonlinear systems with long time delay. Two control architectures, model predictive control based on ULN model and single neuron PID (SN-PID) controller based on ULN predictor, are designed to control pH neutralization process, respectively. In addition, to verify the performance of ULN, two comparisons are also made. One is the generalization ability between ULN and back-propagation (BP) network, the other comparison is ULN predictor and Smith predictor, in which the same controller is used. Simulation results prove the applicability and effectiveness of the ULN model. The special architecture and its learning algorithm give ULN more representing abilities to model and control complicated nonlinear systems with long time delay.