Decoupling Control of the Gas Collectors’ Pressure System Based on Neural Network Inverse System

2014 ◽  
Vol 530-531 ◽  
pp. 985-989
Author(s):  
Fan Wei Meng ◽  
Qing Tian ◽  
Bin Xu
Keyword(s):  
Neural Network ◽  
Composite System ◽  
Nonlinear Approximation ◽  
Original System ◽  
Inverse System ◽  
Decoupling Control ◽  
Pressure System ◽  
Approximate Inverse ◽  
The Neural Network ◽  
The Mathematical Model

For collectors’ pressure system strong interference, coupled, nonlinear, multi-parameter and other characteristics, based on the inverse system decoupling principle, the reversibility of the mathematical model of the gas collectors’ pressure system is analyzed. BP neural network which has strong nonlinear approximation ability is applied, to approximate inverse system of gas collectors’ pressure system. Neural network inverse system with the original system composes of the pseudo linear decoupling composite system. The neural network inverse decoupling control of gas collectors’ pressure system is implemented. The simulation results show that this method realizes decoupling, has a certain application.

Decoupling Control of the Gas Collectors' Pressure System Based on Neural Network Inverse System

2013 ◽  
Vol 457-458 ◽  
pp. 888-892
Author(s):  
Qing Tian ◽  
Jie Sun
Keyword(s):  
Neural Network ◽  
Composite System ◽  
Nonlinear Approximation ◽  
Original System ◽  
Inverse System ◽  
Decoupling Control ◽  
Pressure System ◽  
Approximate Inverse ◽  
The Neural Network ◽  
The Mathematical Model

For collectors pressure system strong interference, coupled, nonlinear, multi-parameter and other characteristics, based on the inverse system decoupling principle, the reversibility of the mathematical model of the gas collectors pressure system is analyzed. BP neural network which has strong nonlinear approximation ability is applied, to approximate inverse system of gas collectors pressure system. Neural network inverse system with the original system composes of the pseudo linear decoupling composite system. The neural network inverse decoupling control of gas collectors pressure system is implemented. The simulation results show that this method realizes decoupling, has a certain application.

Neural network inverse system decoupling control strategy of BLIM considering stator current dynamics

2018 ◽  
Vol 41 (3) ◽  
pp. 621-630 ◽  
Author(s):  
Wenshao Bu ◽  
Fangzhou He ◽  
Ziyuan Li ◽  
Haitao Zhang ◽  
Jingzhuo Shi
Keyword(s):  
Neural Network ◽  
Control System ◽  
Control Strategy ◽  
Displacement Component ◽  
Inverse System ◽  
Decoupling Control ◽  
Motor Speed ◽  
State Equations ◽  
Stator Current ◽  
The Neural Network

The bearingless induction motor (BLIM) is a multi-variable, non-linear, strong coupling system. To achieve higher performance control, a novel neural network inverse system decoupling control strategy considering stator current dynamics is proposed. Taking the stator current dynamics of the torque windings into account, the state equations of the BLIM system is established first. Then, the inverse system model of the BLIM is identified by a three-layer neural network; by means of the neural network inverse system method, the BLIM system is decoupled into four independent second-order linear subsystems, include a rotor flux subsystem, a motor speed subsystem and two radial displacement component subsystems. On this basis, the neural network inverse decoupling control system is constructed, the simulation verification and analyses are performed. From the simulation results, it is clear that when the proposed decoupling control strategy is adopted, not only can the dynamic decoupling control between relevant variables be achieved, but the control system has a stronger anti-load disturbance ability, smaller overshoot and better tracking performance.

Application of Multivariable Non-Linear Decoupling Control Based on Neural Network

2012 ◽  
Vol 214 ◽  
pp. 786-791
Author(s):  
Jian Bo Zhang ◽  
Dong Hai Fan ◽  
Ren Zhi Hu
Keyword(s):  
Neural Network ◽  
Nonlinear System ◽  
Distributed Control ◽  
Decoupling Control ◽  
Distributed Control System ◽  
Neural Network Controller ◽  
Mimo Nonlinear System ◽  
The Neural Network ◽  
Network Controller ◽  
The Mathematical Model

Aimed at Neural Network can approach any nonlinear system with arbitrary accuracy, the frame of distributed NN decoupling system are proposed to decouple the MIMO nonlinear system. In this paper, we designed and finished the Distributed Control System based on ABB’s Freelance 800F, and collected experimental data to model the thermostatic heater, then we have carried out the mathematical model by means of MATLAB dynamic simulation. In sequence, we trained the neural network controller in MATLAB. When the decoupling is completed, we used controller to control the MIMO nonlinear system in DCS. Experiment result shows that it is conscientiously feasible and deserves to be widely applied in the process of controlling industry.

Dynamic Decoupling Control of Electro-Spindle Supported by AC Hybrid Magnetic Bearings Based on Neural Network Inverse Method

2010 ◽  
Vol 97-101 ◽  
pp. 2716-2719 ◽  
Author(s):  
Wei Yu Zhang ◽  
Huang Qiu Zhu ◽  
Ze Bin Yang
Keyword(s):  
Neural Network ◽  
System Theory ◽  
Control Method ◽  
Coupled System ◽  
Original System ◽  
Rotor System ◽  
Inverse System ◽  
Decoupling Control ◽  
Magnetic Bearings ◽  
Dynamic Decoupling

A dynamic decoupling control method based on neural network inverse system theory is developed for the 5 degrees of freedom (5-DOF) rotor system. The rotor system suspended by AC hybrid magnetic bearings (HMBs) is a multivariable, nonlinear and strong coupled system. Firstly, the configuration of 5-DOF HMBs and the mathematical equations of suspension forces are set up. Secondly, it is demonstrated the system is reversible by analyzing mathematical model. On the basis, the neural network inverse system which is composed of the static neural networks and integrators, and original system are in series to constitute pseudo linear systems. Finally, linear system theory is applied to these linearization subsystems for designing close-loop controllers. The simulation results show that this kind of control strategy can realize dynamic decoupling control, and control system obtains good dynamic and static performances.

Robust sideslip angle observer of commercial vehicles based on cornering stiffness estimation using neural network

2022 ◽  
pp. 095440702110724
Author(s):  
Chenyu Zhou ◽  
Liangyao Yu ◽  
Yong Li ◽  
Jian Song
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Stability Control ◽  
Estimation Accuracy ◽  
Accurate Estimation ◽  
Commercial Vehicles ◽  
Sideslip Angle ◽  
The Neural Network ◽  
Wide Range ◽  
The Mathematical Model

Accurate estimation of sideslip angle is essential for vehicle stability control. For commercial vehicles, the estimation of sideslip angle is challenging due to severe load transfer and tire nonlinearity. This paper presents a robust sideslip angle observer of commercial vehicles based on identification of tire cornering stiffness. Since tire cornering stiffness of commercial vehicles is greatly affected by tire force and road adhesion coefficient, it cannot be treated as a constant. To estimate the cornering stiffness in real time, the neural network model constructed by Levenberg-Marquardt backpropagation (LMBP) algorithm is employed. LMBP is a fast convergent supervised learning algorithm, which combines the steepest descent method and gauss-newton method, and is widely used in system parameter estimation. LMBP does not rely on the mathematical model of the actual system when building the neural network. Therefore, when the mathematical model is difficult to establish, LMBP can play a very good role. Considering the complexity of tire modeling, this study adopted LMBP algorithm to estimate tire cornering stiffness, which have simplified the tire model and improved the estimation accuracy. Combined with neural network, A time-varying Kalman filter (TVKF) is designed to observe the sideslip angle of commercial vehicles. To validate the feasibility of the proposed estimation algorithm, multiple driving maneuvers under different road surface friction have been carried out. The test results show that the proposed method has better accuracy than the existing algorithm, and it’s robust over a wide range of driving conditions.

Prediction and analysis of College Students’ mental health based on BP neural network

2020 ◽  
pp. 1-7
Author(s):  
Tang Yushou Su Jianhuan
Keyword(s):  
Neural Network ◽  
Mental Health ◽  
College Students ◽  
Bp Neural Network ◽  
Structural Characteristics ◽  
Prediction System ◽  
The Neural Network ◽  
Data Processing Method ◽  
Training Samples ◽  
The Mathematical Model

College Students’ mental health is an important part of higher education, so the current research and prediction of College Students’ mental health are of great significance to better solve the problem of College Students’ mental health. Taking a local university as an example, the data from 2011 to 2019 are selected and analyzed. The normalized data processing method is used to assign weights to 11 kinds of factors that affect the health of college students. The training samples of a neural network are selected, and the structural characteristics of the neural network and the artificial neural network toolbox of MATLAB are used to establish the BP based model the mathematical model of the prediction system of College Students’ mental health based on neural network. The results show that the error between the predicted value and the measured value is only 0.88%. On this basis, this paper uses the model to predict the weight of the influencing factors of the mental health status of college students in a local university in 2020 and analyzes the causes of the prediction results, to provide the basis for the current mental health education of college students.

Neural Network Inverse Based Omnidirectional Rotation Decoupling Control to the Electrodynamic Reaction Sphere

2019 ◽  
Author(s):  
Zhouyu Huai ◽  
Ming Zhang ◽  
Yu Zhu ◽  
Anlin Chen ◽  
Xin Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Attitude Control ◽  
Inverse Dynamics ◽  
Back Propagation ◽  
Good Control ◽  
Original System ◽  
Decoupling Control ◽  
Field Analysis ◽  
Element Analysis ◽  
Control Scheme

Abstract The electrodynamic reaction sphere is a novel actuator for the spacecraft attitude control subsystem. This paper proposes a neural network inverse based decoupling control scheme to actualize the omnidirectional rotation of the electrodynamic reaction sphere which has strong multivariable nonlinear coupling features due to the induction-based drive. And an integrated electromagnetic torque model of the reaction sphere is firstly derived from the electromagnetic field analysis and modified with the finite element analysis method. Then based on the integrated torque model, a back propagation feedforward neural network is constructed and trained to approach the inverse dynamics which transforms the original system into a pseudo-linear system. Furthermore, an additional PI controller is introduced to achieve good control performance against the unmodelled dynamics. Finally, the effectiveness of the proposed method is validated by simulations.

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