Single Neuron PID Controller Research Articles

Velocity Tracking Control for Hydraulic Lifting System Based on Adaptive PSD Controller

The single neutron adaptive PID controller is applied to the angular velocity tracking control of the hydraulic lifting system. The angle velocity tracking control strategy of the lifting process is proposed, and the lifting angle velocity is designed based on the sine acceleration function, and the lifting angle velocity dynamic programming based on the real-time angle is proposed. The single neutron adaptive PID control method is studied, and in order to improve its performance, a method utilizing genetic algorithm to optimize these parameters of single neuron PID controller is presented. The control algorithm is applied to the large mechanical lifting process successfully, and the simulation results show that the control performance of the Adaptive PSD Controller is more effective.

Based on Single Neuron PID Control of Vehicle Active Suspension System

Relative to the passive suspension, automotive active suspension car driving more ride comfort and stability, has a vital role to further improve the performance of the vehicle. For such a typically complex active suspension system research, the key issue is the selection of control strategies. The problems in the currently active suspension control strategy, the principle of a simple, effective, this paper, a single neuron PID control strategy used in the automotive active suspension system. The results show that compared with other control strategies, single neuron PID control strategy is reliable, has more advantages.

Position Tracking System of Filling Machine Based on Compound Control Strategy

In view of the complexity and periodic motion of automatic filling machine, a novel compound control strategy based on single neuron PID model reference adaptive control and repetitive control is proposed. Diagonal recurrent neural network (DRNN) is used as on-line identifier of system for the single neuron PID controller to adjust its weights and PID parameters by self-learning and self-adapting. The dynamic state performance can be improved by adaptive PID controller based on DRNN on-line Identification and the steady state performance is improved by modified repetitive controller. Simulation results show that the control system has good ability of restraining disturbances and high position tracking precision and good robustness.

Single Neuron PID Control of a Turbofan Engine with Inlet Pressure Distortion

In this paper, a preliminary exploration and research about the adaptive control law has been carried on for the turbofan engine with inlet pressure distortion. First, a real-time dynamic component-based model which contains the inlet distortion effects was established. Then, we select a single neuron PID controller with adaptive parameters to complete the dynamic acceleration process. The results illustrate that the single neuron control algorithm with gain adaptive has strong robustness, self-learning and anti-jamming capability and the performance of a turbofan engine can be effectively improved by using the turbine expansion ratio adaptive control law.

Reduction of Wind Turbine Torque Fluctuation Using Individual Pitch Control Based on Edgewise Moment

In order to eliminate the nonuniform force on the wind rotor caused by wind shear, tower shadow and turbulence and smooth the torque fluctuation of the wind turbine and the unbalanced loads on the wind turbine, a new individual pitch control strategy based on edgewise moment using single neuron PID controller is proposed. That is, the presented control strategy directly controls the blade edgewise moment generated by aerodynamic force. At the same time, to simulate the wind turbine loads, a dynamic model of three-bladed upwind horizontal axis wind turbine is built. Thus, the influence rules of the wind turbine torque fluctuation are deduced at length. Finally, to verify the effectiveness of the proposed individual pitch control system with the single neuron PID controller, the 2MW wind turbine generator system is modeled and simulated. The performance of the controller is illustrated its capability of not only reducing the wind turbine fluctuation, but also smoothing the fluctuation of the flapwise moment, the yaw moment and the tilt moment.

Single Neuron PID Control of Aircraft Deicing Fluids Rapid Heating System

Aircraft deicing fluids rapid heating system is widely used in aircraft ground deicing to ensure that the operation of flights can be safe and efficient. Aiming at the temperature turbulence problem of aircraft deicing system, this paper presents the single neuron PID control strategy which combine the advantage of conventional PID control with artificial neuron control. The aircraft deicing fluids rapid heating system and the scheme and working principle of the system is introduced. Simulation is executed on the basis of the mathematical model of aircraft deicing fluids rapid heating system, which is built in this paper, according to a number of data collected by experiments which are operated on the experimental platform of deicing fluids rapid heating system. The simulation results show that the single neuron PID control strategy perform effectively on the temperature turbulence problem of aircraft deicing fluids rapid heating system. Experiments are conducted to vertify the single neuron PID control strategy, the results of which show that the single neuron PID control strategy can achieve the request in practical application of the aircraft deicing fluids rapid heating system.

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.

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.

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.

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.

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.

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.

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.

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.

A single-neuron PID adaptive multicontroller scheme based on RBFNN

In order to improve the control performance of the multicontroller proposed by Guo and Jutan (Canadian Journal of Chemical Engineering, 79, 817-22, 2001), a single-neuron PID multicontroller scheme based on a radial base function neural network (RBFNN) is proposed in this paper. This scheme has four controllers, specifically a set-point controller, two load controllers and a proportional controller. These controllers may be designed independently to achieve good control performance for both set-point tracking and load rejection. In particular, the set-point controller and the load controller have been chosen as single-neuron PID controllers. The model parameters and the parameters of the two single-neuron PID controller are updated in real time. For simplicity, the feedforward controller can be chosen as a unity gain proportional controller. It guarantees physical realizability and provides complete compensation for measurable disturbance. The simulation results show that the single-neuron PID adaptive multicontroller scheme based on RBFNN is very effective and the controller is of relatively strong robustness.