scholarly journals D-FNN Based Modeling and BP Neural Network Decoupling Control of PVC Stripping Process

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Shu-zhi Gao ◽  
Jing Yang ◽  
Jie-sheng Wang
Keyword(s):  
Neural Network ◽  
Process Model ◽  
Control Method ◽  
Industrial Process ◽  
Actual Process ◽  
Single Input Single Output ◽  
Single Output ◽  
Highly Nonlinear ◽  
Fuzzy Neural ◽  
Process Operation

PVC stripping process is a kind of complicated industrial process with characteristics of highly nonlinear and time varying. Aiming at the problem of establishing the accurate mathematics model due to the multivariable coupling and big time delay, the dynamic fuzzy neural network (D-FNN) is adopted to establish the PVC stripping process model based on the actual process operation datum. Then, the PVC stripping process is decoupled by the distributed neural network decoupling module to obtain two single-input-single-output (SISO) subsystems (slurry flow to top tower temperature and steam flow to bottom tower temperature). Finally, the PID controller based on BP neural networks is used to control the decoupled PVC stripper system. Simulation results show the effectiveness of the proposed integrated intelligent control method.

The Intelligent Scheduling Method of Elevator Group Control Based on Fuzzy Neural Network

2012 ◽  
Vol 155-156 ◽  
pp. 653-657
Author(s):  
Yu Lin Dong ◽  
Xiao Ming Wang
Keyword(s):  
Neural Network ◽  
Control System ◽  
Fuzzy Neural Network ◽  
Control Method ◽  
Scheduling Algorithm ◽  
Mathematic Model ◽  
Group Control ◽  
Fuzzy Neural ◽  
And Performance ◽  
Elevator Group Control

Elevator group control system (EGCS) is a complex optimization system, which has the characteristics of multi-objective, uncertain, stochastic random decision-making and nonlinear. It is hard to describe the elevator group control system in exact mathematic model and to increase the capability of the system with traditional control method. In this paper, we aim at the characters of elevator group control system and intelligent control, introduce the system's control fashion and performance evaluate guidelines and propose an elevator group control scheduling algorithm based on fuzzy neural network.

scholarly journals A Reference-Model-Based Neural Network Control Method for Multi-Input Multi-Output Temperature Control System

Processes ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1365
Author(s):  
Yuan Liu ◽  
Song Xu ◽  
Seiji Hashimoto ◽  
Takahiro Kawaguchi
Keyword(s):  
Neural Network ◽  
Control Method ◽  
Reference Model ◽  
Mimo System ◽  
Network Control ◽  
Temperature Control System ◽  
Neural Network Control ◽  
Model Based ◽  
Neural Network Controller ◽  
Highly Nonlinear

Neural networks (NNs), which have excellent ability of self-learning and parameter adjusting, has been widely applied to solve highly nonlinear control problems in industrial processes. This paper presents a reference-model-based neural network control method for multi-input multi-output (MIMO) temperature system. In order to improve the learning efficiency of the NN control, a reference model is introduced to provide the teaching signal for the NN controller. The control inputs for the MIMO system are given by the sum of the output of the conventional integral-proportional-derivative (I-PD) controller and the outputs of the neural network controller. The proposed NN control method can not only improve the transient response of the system, but can also realize temperature uniformity in MIMO temperature systems. To verify the proposed method, simulations are carried out in MATLAB/SIMULINK environment and experiments are carried out on the DSP (Digital Signal Processor)-based experimental platform, respectively. Both results are quantitatively compared to those obtained from the conventional I-PD control systems. The effectiveness of the proposed method has been successfully verified.

Temperature Control of Batch Reaction Based on Genetic Fuzzy Neural Network

2011 ◽  
Vol 204-210 ◽  
pp. 1968-1971 ◽  
Author(s):  
Chun Tao Man ◽  
Jia Cui ◽  
Xin Xin Yang ◽  
Jun Kai Wang ◽  
Tian Feng Wang
Keyword(s):  
Neural Network ◽  
Temperature Control ◽  
Fuzzy Neural Network ◽  
Control Method ◽  
Design Method ◽  
Batch Reactor ◽  
Batch Processes ◽  
Strong Nonlinearity ◽  
Control Scheme ◽  
Fuzzy Neural

The batch reactor has strong nonlinearity and hysteresis, the conventional control method is hard to meet the control requirements. According to the batch processes temperature control, this thesis proposed an intelligent control scheme. Combined neural networks with fuzzy logic control, searching and optimized parameters of fuzzy neural network by using Genetic Algorithm (GA), displayed the design method and optimization steps, and the simulation results verify the control scheme which proposed is feasible and effective.

Wheeled Robot Based on Fuzzy Neural Network Tracking Studies

2013 ◽  
Vol 373-375 ◽  
pp. 181-184
Author(s):  
Su Ying Zhang ◽  
Shao Jie Xu ◽  
Jing Fei Zhu ◽  
Bing Hao Li ◽  
Wen Pan Shi
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Control Method ◽  
External Disturbance ◽  
Network Control ◽  
Learning Ability ◽  
Parameter Uncertainties ◽  
Wheeled Robot ◽  
Fuzzy Neural ◽  
Arbitrary Precision

The wheeled robot with non-integrity constraints is a typical nonlinear system, in order to achieve the ideal path tracing, presented a theory based on fuzzy neural network control. Centralized compensation system based on neural network uncertainty can be arbitrary-precision approximation of continuous nonlinear functions as well as the complex uncertainties with adaptive and learning ability. By MATLAB simulation showed that the control method to ensure fast convergence and error robustness of parameter uncertainties and external disturbance.

scholarly journals Perturbation Observer-Based Robust Control Using a Multiple Sliding Surfaces for Nonlinear Systems with Influences of Matched and Unmatched Uncertainties

Mathematics ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 1371 ◽  
Author(s):  
Ha Le Nhu Ngoc Thanh ◽  
Mai The Vu ◽  
Nguyen Xuan Mung ◽  
Ngoc Phi Nguyen ◽  
Nguyen Thanh Phuong
Keyword(s):  
Robust Control ◽  
Control Method ◽  
Lyapunov Theory ◽  
Loop Control ◽  
Single Input Single Output ◽  
Single Output ◽  
Closed Loop Control System ◽  
Loop Control System ◽  
Unmatched Uncertainties ◽  
Matched And Unmatched Uncertainties

This paper presents a lumped perturbation observer-based robust control method using an extended multiple sliding surface for a system with matched and unmatched uncertainties. The fundamental methodology is to apply the multiple surfaces to approximate the unknown lumped perturbations simultaneously influencing on a nonlinear single input–single output (SISO) system. Subsequently, a robust controller, based on the proposed multi-surface and the approximated values, is designed to highly improve the control performance of the system. A general stability of the lumped perturbation observer and closed-loop control system is obtained through the Lyapunov theory. Results of a numerical simulation of an illustrative example demonstrate the soundness of the proposed algorithm.

scholarly journals Command Filtered Adaptive Fuzzy Neural Network Backstepping Control for Marine Power System

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Xin Zhang ◽  
Longhua Mu
Keyword(s):  
Neural Network ◽  
Power System ◽  
Fuzzy Neural Network ◽  
Control Method ◽  
Chaotic Oscillation ◽  
Lyapunov Stability Theory ◽  
Backstepping Control ◽  
Adaptive Fuzzy Neural Network ◽  
Adaptive Fuzzy ◽  
Fuzzy Neural

In order to retrain chaotic oscillation of marine power system which is excited by periodic electromagnetism perturbation, a novel command-filtered adaptive fuzzy neural network backstepping control method is designed. First, the mathematical model of marine power system is established based on the two parallel nonlinear model. Then, main results of command-filtered adaptive fuzzy neural network backstepping control law are given. And the Lyapunov stability theory is applied to prove that the system can remain closed-loop asymptotically stable with this controller. Finally, simulation results indicate that the designed controller can suppress chaotic oscillation with fast convergence speed that makes the system return to the equilibrium point quickly; meanwhile, the parameter which induces chaotic oscillation can also be discriminated.

Double-Input Multi-Output Pressure Control System Based on Addressable Pressure Component

2021 ◽  
Author(s):  
Qiandiao Wei ◽  
He Xu ◽  
Siqing Chen ◽  
Weiwang Fan
Keyword(s):  
Control System ◽  
Control Method ◽  
Pressure Control ◽  
Supply Source ◽  
Single Input Single Output ◽  
Square Wave ◽  
Output Pressure ◽  
Single Output ◽  
Pressure Control System ◽  
Single Input

Abstract Soft robots driven by pressurized fluid have recently been attracted more attention and achieved a variety of innovative applications in bionics, medical surgery, rehabilitation, search, and rescue system. And they have been demonstrated to be able to perform many different tasks, especially in some conditions of demand a high degree of compliance. Generally, they consisted of multiple actuate channels that require independent works. Consequently, a mass of pressure regulators and input pipelines are demanded, which will increase the complexity of the control system. To solve this problem, we propose a new pressure control method inspired by the control bus of electronic interface technology in this paper. An addressable pressure control bus system based on band-pass valve (BPV) and square wave of pressure (control signal) was designed. It consisted of a pressure supply source and an addressing signal, they are controled by two regulators, respectively. One of the pressure pipelines serves as the control bus to transmit the control pressure signal, which plays an addressing signal role in the system. The other serves as the pressure supply source of the multi-channel actuators. The BPV can be set to different opening pressure bands to realize the setting of diverse outputs address codes on the bus. This method discovered the work mode of double-input multi-output, which will get rids of the traditional control method of single-input single-output. In this paper, we designed the BPV and tested its function. To demonstrate the feasibility of this method proposed, a control system with two output ports was established. The result has shown that the output port can be selected by the pressure square wave signal, which realizes the function of single input multiple outputs. It reduces the complexity of the control strategy of the fluid control system.

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