Fuzzy Immune PID Neural Network Control Method Based on Boiler Steam Pressure System

2011 ◽  
Author(s):  
Zhiyong Li
Keyword(s):  
Neural Network ◽  
Control Method ◽  
Steam Pressure ◽  
Network Control ◽  
Neural Network Control ◽  
Pressure System ◽  
Pid Neural Network

scholarly journals Adaptive Neural Network Control of Zero-Speed Vessel Fin Stabilizer Based on Command Filter

2022 ◽  
Vol 12 (2) ◽  
pp. 754
Author(s):  
Ziteng Sun ◽  
Chao Chen ◽  
Guibing Zhu
Keyword(s):  
Neural Network ◽  
Control System ◽  
Control Method ◽  
Large Angle ◽  
Network Control ◽  
Neural Network Control ◽  
Adaptive Neural Network ◽  
Adaptive Neural Network Control ◽  
Command Filter ◽  
Fin Stabilizer

This paper proposes a zero-speed vessel fin stabilizer adaptive neural network control strategy based on a command filter for the problem of large-angle rolling motion caused by adverse sea conditions when a vessel is at low speed down to zero. In order to avoid the adverse effects of the high-frequency part of the marine environment on the vessel rolling control system, a command filter is introduced in the design of the controller and a command filter backstepping control method is designed. An auxiliary dynamic system (ADS) is constructed to correct the feedback error caused by input saturation. Considering that the system has unknown internal parameters and unmodeled dynamics, and is affected by unknown disturbances from the outside, the neural network technology and nonlinear disturbance observer are fused in the proposed design, which not only combines the advantages of the two but also overcomes the limitations of the single technique itself. Through Lyapunov theoretical analysis, the stability of the control system is proved. Finally, the simulation results also verify the effectiveness of the control method.

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.

Study on Multivariable System Based on PID Neural Network Control

2012 ◽  
Vol 591-593 ◽  
pp. 1490-1495 ◽  
Author(s):  
Huai Lin Shu ◽  
Jin Tian Hu
Keyword(s):  
Neural Network ◽  
Control System ◽  
Transfer Function ◽  
Design Process ◽  
Network Control ◽  
Neural Network Control ◽  
Single Variable ◽  
Multivariable System ◽  
Pid Neural Network ◽  
Self Learning

The multivariable PID neural network (MPIDNN) control system is introduced in this paper. MPIDNN is used to perform both the control and the decouple at the same time and to get better performance. It is difficult to control multivariable system by conventional controller because the strong coupling properties of the system. Generally, the decoupling system should be designed first and the multivariable object would be divided into several single variable objects. Then, several simple controller would achieve the control of those objects. The decoupling system and the controller exist in theory but the design process is very difficult actually because the transfer function of the object is difficult to get. Especially, if the number of the object inputs is not equal to that of the object outputs, which is called unsymmetry object, the conventional decoupling is impossible. A actual example is discussed in the paper in order to prove the function of the MPIDNN, in which an un-symmetry multivariable system which has 3 inputs and 2 outputs is controlled by a MPIDNN and the perfect control property is obtained by self-learning process.

A Neural Network Method for Miniature Unmanned Helicopter Heading Control

2012 ◽  
Vol 468-471 ◽  
pp. 93-96
Author(s):  
Meng Bai ◽  
Min Hua Li
Keyword(s):  
Neural Network ◽  
Control Method ◽  
Network Control ◽  
Neural Network Control ◽  
Unmanned Helicopter ◽  
Unknown Parameters ◽  
Neural Network Controller ◽  
The Neural Network ◽  
Heading Control ◽  
Helicopter Aerodynamics

A neural network control method for heading control of miniature unmanned helicopter is proposed. For the complexity of miniature helicopter aerodynamics, it is difficult to identify the unknown parameters of yaw dynamics model. To design heading controller of miniature helicopter without modelling yaw dynamics, BP neural network is designed as heading controller, which is trained by collected flight data. By training, the neural network controller can learn the artificial operation strategy and realize the heading control of miniature unmanned helicopter. Simulation results demonstrate the validity of the proposed neural network control method.

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