Neural Network PID Controller Research Articles

Plastic Tank Temperature Control System Based on OPC Technology

This paper analyzes the characteristics of the PLC and MATLAB, describes the OPC technology and introduces the system structures and procedures which uses the OPC technology to achieve the exchange of data between PLC and MATLAB system. For example, in the plastic tank temperature control system, it mentions the BP neural network PID control algorithm which is applied to the PLC control system by using the MATLAB intelligent controller. The results show that this control method is simple and the control effect is good, much better than the traditional PID control.

MEMS-based low-cost strap-down AHRS research

With the rapid development of modern Micro-Electro-Mechanical Systems (MEMS) technology, the requirement of low cost strapdown AHRS based on MEMS sensor is becoming feasible. This paper presents the results of the attitude determination using low-cost MEMS-based sensor. Aiming at the accumulative deviation incurring by low-cost MEMS gyroscope drifting and integration deviation, this study proposes a multi-sensor data fusion algorithm that uses benchmark reference vectors to detect and correct the gyro drift. The proposed algorithm adopts the method of attitude evaluation based on Direction Cosine Matrix (DCM). A BP neural network PID controller is used to compute gyro drift for real-time correction. Numerical simulation shows that the algorithm meets the requirements of the system. In order to test the actual performance of the system, a hardware platform of strap-down AHRS is designed, based on a three-axis gyroscope, a three-axis accelerometer, three-axis magnetic sensors and a Digital Signal Processor (DSP). Finally, the three-axis flight turntable table test results obtained, show that the static accuracy of the attitude angle is better than 0.5° and the dynamic accuracy is better than 3°. Moreover, the AHRS is tested on a micro-Unmanned Aerial Vehicle (UAV), and the practical application feasibility is validated.

The Study of BP Neural Network PID Control Based on the Cutting Chatter Suppression System

The intelligent control strategy of BP neural combined network with classical PID control is mainly studied and simulated. The advantages of the control strategy are discussed. Based on the simulated data, the BP neural network PID control has the stronger adaptive ability.

The Research and Simulation of PID Algorithm Optimization for Concrete Mixing

In order to fix the defects of the inaccurate control parameters in concrete intelligent mixing, this paper proposes a concrete mixing parameter control method based on fuzzy neural network PID control algorithm. The control method mainly includes two parts-control parameter calculation and mixing data feedback. According to the control parameter calculation, the proposed method is capable of calculating the control parameters required to control the concrete mixing. The industrial concrete mixing condition can be feedback to the control end via data feedback method. The control parameters can be adjusted according to the feedback data to accurately control the concrete mixing. The experimental results illustrate the proposed algorithm can effectively increase the control accuracy with satisfactory performance.

Design of PID Temperature Control System Based on BP Neural Network

Based on BP neural network PID controller has the ability to approximate any nonlinear function, can achieve real-time online tuning PID controller parameter . Through the system simulation analysis, simulation results show that the BP neural network tuning PID control than traditional PID algorithm and BP network algorithm has a greater degree of improvement, the system has better robustness and adaptability, its output can also achieve the desired control accuracy through online adjustments. Suitable for temperature control system.

The Application of PSO-BP Neural Network PID Controller in Variable Frequency Speed Regulation System

Variable frequency speed control system hold good stability,more efficient,more energy conservation etc, so it has been widely used in the industrial areas,but the control strategy of traditional was difficult to achieve the desired control effect.This paper adopt particle swarm algorithm and BP neural network to construct the PID controller of PSO-BP neural network , the M-Files of PSO-BP neural network PID based on MATLAB through S-Function, and the mode of PSO-BP neural network PID variable frequency speed control system was established in SIMULINK platform.Simulation results show that the controller hold well robustness, follow and stability,and the dynamic characteristics of the original system was improved, the application value of this method in the variable frequency speed control system was proved.

Research on the Control Strategy of Passive Torque Servo System for Loading at High Frequency

In view of the problem of difficult to suppress surplus torque and to obtain high servo accuracy at high frequency in the passive torque servo system(PTSS), the neural network PID control strategy with surplus torque compensation based on dynamic fuzzy neural network (DFNN) and double-stator motor is proposed. Firstly the model of the PTSS is built and the mathematical model of surplus torque is derived. Then the model of surplus torque is identified by DFNN. The surplus torque is estimated in real time and converted directly to the control signals of outer stator of double-stator motor which produces torque to compensate the surplus torque. Finally the parameters of the neural network PID (NNPID) controller are adjusted in real time according to the Jacobian information and system error. The Jacobian information is obtained from the online identification of PTSS by RBF neural network. The simulation results show that surplus torque model is close to the actual surplus torque system, that compensation control largely eliminates the surplus torque, that system performance is improved and that the control strategy is successful.

Research on Neural Network Control Technology for Satellite-Based Laser Communication Tracking Turntable

In space laser communication tracking turntable work environment characteristics, we design a neural network PID control system which makes the system’s parameter self-tuning. The control system cans self-tune parameters under the changes of the object’ mathematic model, it solves the problem for the control object’s model changes under the space environment. It also looks for method for optimum control through the function of neural network's self-learning in order to solve the problem of the precision’s decline which arouse from vibration and disturbance. The simulation experiments confirmed the self-learning ability of neural network, and described the neural PID controller dynamic performance is superior to the classical PID controller through the output characteristic curves contrast.

Study of Multivariable PID Neural Network Control for the Compression Station of Large-Scale Helium Refrigerator

With the fast development of superconducting, nuclear-energy and high-energy physics, further requirement of the system of large-scale helium refrigeration is brought forward by some associated subjects. The present controlling system of the system abovementioned is not well able to deal with the interruption such as the heat pulse impact. Thus, it leads to a new challenge that must be confronted with by the system of large-scale helium refrigeration.This paper aims at the control of gas management panel (GMP) in compression station of large-scale helium refrigerator. The neural network PID control of GMP in the system is introduced and simulated.

Self-learning PID Control for X-Y NC Position Table with Uncertainty Base on Neural Network

An adaptive radical basis function (RBF) neural network PID control scheme for X-Y position table is proposed by the paper. Firstly, X-Y position table model is established, controller based on neutral network is used to learn adaptive and compensate uncertainty model of X-Y position table, neutral network is used to study model. PID neural network controller base on augmented variable method is designed. PID controller is used as assistant direction error controller, neural network parameters base on stochastic gradient algorithm can be adjust adaptive on line. The simulation results show that the presented controller has important engineering value.

인공신경망 PID를 이용한 무인항공기 터보제트 엔진 제어

In this paper, controller Propose to prevent compressor surge and improve the transient response of the fuel flow control system of turbojet engine. Turbojet engine controller is designed by applying Artificial Neural Network PID control algorithm and make an inference by applying Artificial Neural Network Error Back Propagation Algorithm. To prevent any surge or a flame out event during the engine acceleration or deceleration, the ANN PID controller effectively controls the fuel flow input of the control system. ANN PID results are used as the fuel flow control inputs to prevent compressor surge and flame-out for turbo-jet engine and the controller is designed to converge to the desired speed quickly and safely. Using MATLAB to perform computer simulations verified the performance of the proposed controller. Response characteristics pursuant to the gain were analyzed by simulation.

Study on Parameters Optimization Method of Fuzzy Neural Network PID Controller

In allusion to the insufficient of the traditional parameters optimization of the traditional fuzzy neural network PID controller, the parallel search characteristics of the ant colony algorithm in the whole parameter space is used. A parameters optimization method of the PID controller based combining the ant colony algorithm and fuzzy theory and neural network is proposed in this paper. The method used the ant colony algorithm to comprehensively optimize the parameters and structure of fuzzy neural network, which to be used to train and determine the parameters of the PID controller in order to get the fuzzy neural network PID controller. This method is used in the practical application of nonlinear coupled system, the experimental results show that the optimized fuzzy neural network PID controller takes the faster approximation control objectives, the shorter response time, the smaller overshoot and higher control accuracy. Consequently, the research has the theoretical significance and practical application value.

고이득 관측기가 적용된 터보제트엔진의 인공신경망 PID 제어기 설계

In this paper, controller propose to prevent compressor surge and improve the transient response of the fuel flow control system of turbojet engine. Turbojet engine controller is designed by applying Artificial Neural Network PID control algorithm and make an inference by applying Levenberg-Marquartdt Error Back Propagation Algorithm. Artificial Neural Network inference results are used as the fuel flow control inputs to prevent compressor surge and flame-out for turbojet engine for UAV. High Gain Observer is used to estimate to compressor rotation speed of turbojet engine. Using MATLAB to perform computer simulations verified the performance of the proposed controller. Response characteristics pursuant to the gain were analyzed by simulation.

Design of Memristive Neural Network PID Controller

Memristor is a novel kind of electronic device whose resistance can continuously change depending on the charge or flux passing through it,and has unique advantages in the applications of nonvolatile memory and artificial neural networks.The intelligent PID controllers play an essential role in modern control field.Based on the study of conventional single neuron and neural network PID controller,this paper presents an approach to the memristive weight updating which is similar to the biologic synapse,and puts forward a model of the memristive single neuron and neural network controllers.According to the stipulating theory,the program simulation and Simulink modeling have been already provided.Experimental results verify the effectiveness of proposed scheme.It would be beneficial for the development of the modern intelligent PID controller and expect to promote the hardware realization of the neural network PID controller.

Design of Control System of Eddy Current Retarder Based on BP Neural Network PID Controller

A new kind of algorithm of controller for eddy current retarder is designed in this paper. The eddy current retarder control system with traditional PID controller can't achieve a perfect performance in the rapid response. Back propagation (BP) neural network is one of artificial neural networks which has a good learning ability with a simple and recurrent structure, so it is suitable for controlling complicated eddy current retarder system. This paper introduces the principle, characteristics and learning algorithm of the BP neural network and designs the control system of eddy current retarder based on BP neural network PID controller by combining BP neural network and traditional PID. Making use of MATLAB, simulate this new kind of controller for eddy current retarder in the rapid response. Simulation results show it can improve the dynamic response performance and enhance the static precision compared to the traditional PID controller.

Heating exchange process PIDNN control system research based on T-S fuzzy model

The principle of heating exchange system is analysed in this paper. According to the strong coupling between the variations and parameters time variable characteristics, it is difficult to set up the arithmetical model and achieve better control effects. To overcome these difficulties, this paper adopts T-S fuzzy model to describe the input-output relationship based on collected data of system. Due to the time variable character, it is hard to control with traditional PID controller. To improve the control effect, combining neural network control algorithm, this paper adopts PIDNN PID Neural Network controller. The PIDNN algorithm is not just tuning PID parameters by neural network, and improves the system control ability by defining proportional, integral, derivative characteristics neurons. Basing on the theoretical analysis, simulations are carried out by using MATLAB. The results of simulation experiment show that the designed model is effective and the control system achieves satisfied control effect.

Study on RBF Neural Network PID Control for Hydro-Viscous Drive System

To verify the applicability of RBF neural network PID control on speed regulating start control for hydro-viscous drive system, analyze the principle of RBF neural network PID control, the simulation model is established based on SIMULINK and the control characteristics are analyzed based on the AMESim/MATLAB co-simulation. The results show that RBF neural network PID control has a good self-correcting effect on speed regulating start of hydro-viscous; it can make right judgments according to the error and error rate and adjust the output speed towards opposite direction of error; meanwhile, it ensures the smoothness of output curve and avoids excessive mechanical impact. The results play a guiding role for control strategy selection of speed regulating start.

Design of Tiltrotor Flight Control System in Conversion Mode Using Improved Neutral Network PID

Considering the high-order, time-varying and nonlinear characteristics of the tiltrotor aircraft in conversion mode, the control requirements and the specific conversion scheme are presented first. An improved back propagation (BP) neural network PID control algorithm is then proposed, utilizing the strong nonlinear mapping characteristics of neural network. In the proposed algorithm, an inertia term is added to the gradient-descent-based weight adjustment formula of the conventional BP neutral network in order to increase the convergence rate and obtain satisfactory control effects. Finally simulation results indicate the effectiveness of the proposed algorihm.

Study on BP Neural Network PID Control for Hydro-Viscous Drive System

Establish simulation model based on BP neural network PID control to solve the issue of hydro-viscous drive speed regulating start control strategies, experimental verifications prove its adaptability through AMESim/MATLAB co-simulation, research shows that: BP neural network PID control for hydro-viscous drive has a good self-correcting effect, the output speed adjusts towards the opposite direction according to the error and the error rate, while maintaining the smoothness of the output curve, thereby it can avoid over-large mechanical shocks, it indicates the BP neural network PID control is suitable for speed regulating start.

Beer Fermentation Temperature Control Curve Optimization Based on the Fuzzy - Neural Network PID Control Algorithm

An auto-control model is presented to the process of beer fermentation, which has the characteristics such as time-varying, inertia, time-delay and nonlinear. The traditional PID control is difficult to accurately control. This paper according to the beer fermentation of problems puts forward a new control algorithm: fuzzy-neural network PID control algorithm. The fuzzy logistic differential control and intelligent integral control were supplemented into the fuzzy set-point weight tuning, so that the insufficiency of original PID control method was effectively improved. The advantages of this control algorithm are not only constitute a simple, small static error, dynamic response speed but also the ability to learn, etc. Therefore it not only can strengthen robust and intelligence of the system, but also make design simple and easily be required.