scholarly journals A Novel Reference Model-Based Neural Network Approach to Temperature Control System

2019 ◽  
Vol 692 ◽  
pp. 012048
Author(s):  
Song Xu ◽  
Seiji Hashimoto ◽  
Wei Jiang
Keyword(s):  
Neural Network ◽  
Control System ◽  
Temperature Control ◽  
Reference Model ◽  
Temperature Control System ◽  
Network Approach ◽  
Neural Network Approach ◽  
Model Based

scholarly journals A Reference-Model-Based Artificial Neural Network Approach for a Temperature Control System

Processes ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 50
Author(s):  
Song Xu ◽  
Seiji Hashimoto ◽  
YuQi Jiang ◽  
Katsutoshi Izaki ◽  
Takeshi Kihara ◽  
...  
Keyword(s):  
Control System ◽  
Temperature Control ◽  
Digital Signal Processor ◽  
Reference Model ◽  
Learning Performance ◽  
Temperature Control System ◽  
Neural Network Approach ◽  
Model Based ◽  
Artificial Neural ◽  
Self Learning

Artificial neural networks (ANNs), which have excellent self-learning performance, have been applied to various applications, such as target detection and industrial control. In this paper, a reference-model-based ANN controller with integral-proportional-derivative (I-PD) compensation has been proposed for temperature control systems. To improve the ANN self-learning efficiency, a reference model is introduced for providing the teaching signal for the ANN. System simulations were carried out in the MATLAB/SIMULINK environment and experiments were carried out on a digital-signal-processor (DSP)-based experimental platform. The simulation and experimental results were compared with those for a conventional I-PD control system. The effectiveness of the proposed method was verified.

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.

Design of Temperature Control System Based on DSP and Fuzzy Neural Network

2013 ◽  
Vol 823 ◽  
pp. 384-387
Author(s):  
Ya Juan Chen ◽  
Yue Hong Zhang ◽  
Gen Wang Ying
Keyword(s):  
Neural Network ◽  
Control System ◽  
Temperature Control ◽  
Fuzzy Neural Network ◽  
Good Control ◽  
Temperature Control System ◽  
Control Effect ◽  
Fuzzy Neural ◽  
Electric Boiler ◽  
Self Tuning

Using fuzzy neural network to tune PID parameters, and DSP as processor, it was designed that a set of electric boiler temperature control system based on PID parameters self-tuning, including the design of each hardware module and each software subroutine of the system. Experimental results show that compared with the traditional PID temperature control system, this temperature control system has the advantages such as good control effect, easy parameter adjustment, strong anti-jamming capability, better adaptability and robustness, has the feasibility and practical value.

scholarly journals Simulation of PID Temperature Control System Based on Neural Network

2018 ◽  
Vol 23 ◽  
pp. 637-640
Author(s):  
Yujie Yan ◽  
Fengzhi Dai ◽  
Lingran An ◽  
Yuxing Ouyang ◽  
Zhongyong Ye ◽  
...  
Keyword(s):  
Neural Network ◽  
Control System ◽  
Temperature Control ◽  
Temperature Control System
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