Design of Fuzzy Neural Network PID Controller for Hypersonic Vehicle

2019 ◽  
Author(s):  
YingXue Zhang ◽  
GuangRun Sheng
Keyword(s):  
Neural Network ◽  
Pid Controller ◽  
Fuzzy Neural Network ◽  
Hypersonic Vehicle ◽  
Fuzzy Neural ◽  
Neural Network Pid

Application of Fuzzy Neural Network PID Controller in Networked Control Systems

2013 ◽  
Vol 273 ◽  
pp. 689-693
Author(s):  
Zi Yi Fu ◽  
Lu Wang ◽  
Lei Wang
Keyword(s):  
Neural Network ◽  
Control Systems ◽  
Pid Controller ◽  
Fuzzy Neural Network ◽  
Networked Control Systems ◽  
Control Object ◽  
Networked Control ◽  
Control Performance ◽  
Fuzzy Neural ◽  
Neural Network Pid

Aiming at the performance degradation and system destabilization which are caused by time delay in networked control systems (NCSs), a novel fuzzy neural network PID controller is proposed to alleviate the adverse effect. This approach enjoys the advantage of functional mapping of the fuzzy neural network, and gives better performance in tuning the PID controller parameters online. The simulation of the improved networked controller is carried out through the matlab/truetime, and the DC motor which has higher real-time performance is chosen as a control object. The simulation results illustrate that the controller can effectively improve the control performance and keep the system stable.

scholarly journals Single-Phase Photovoltaic Grid-Connected Inverter Based on Fuzzy Neural Network

2021 ◽  
Vol 25 (3) ◽  
pp. 310-316
Author(s):  
Shenping Xiao ◽  
Zhouquan Ou ◽  
Junming Peng ◽  
Yang Zhang ◽  
Xiaohu Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Pid Controller ◽  
Fuzzy Neural Network ◽  
Single Phase ◽  
Fuzzy Inference ◽  
Learning Ability ◽  
Proportional Integral ◽  
Fuzzy Neural ◽  
Grid Connected Inverter ◽  
Self Learning

Based on a single-phase photovoltaic grid-connected inverter, a control strategy combining traditional proportional–integral–derivative (PID) control and a dynamic optimal control algorithm with a fuzzy neural network was proposed to improve the dynamic characteristics of grid-connected inverter systems effectively. A fuzzy inference rule was established after analyzing the proportional, integral, and differential coefficients of the PID controller. A fuzzy neural network was applied to adjust the parameters of the PID controller automatically. Accordingly, the proposed dynamic optimization algorithm was deduced in theory. The simulation and experimental results showed that the method was effective in making the system more robust to external disruption owing to its excellent steady-state adaptivity and self-learning ability.

scholarly journals Fuzzy Neural Network PID–based constant deceleration compensation device for the brakes of mining hoists

2020 ◽  
Vol 12 (7) ◽  
pp. 168781402093756
Author(s):  
Chi Ma ◽  
Suzhi Tian ◽  
Xinming Xiao ◽  
Yuqiang Jiang
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Lower Cost ◽  
Constant Torque ◽  
Fuzzy Neural ◽  
Safety And Reliability ◽  
Complete Set ◽  
Compensation Device ◽  
Short Time ◽  
Neural Network Pid

In comparison with constant torque brakes, constant deceleration brakes are more advantageous for the safety of mining hoists, but complete set of such products manufactured by big companies are not what ordinary mining enterprises can afford. As an alternative solution, this article develops a constant deceleration compensation device, which adds the function of constant deceleration brake onto the original brakes. Control strategy based on Fuzzy Neural Network PID is investigated and simulated with the combination of AMEsim and Simulink. An actual device is built and tested in real industrial field. The application illustrates the feasibility of this constant deceleration compensation device, which can achieve constant decelerations within a very short time. This device will prevent dangerous decelerations from happening to hoists at a much lower cost, and greatly improve the safety and reliability of mining hoists.

Development and Research of Intelligent PID Controller Based on Fuzzy Neural Network

2014 ◽  
Vol 644-650 ◽  
pp. 341-345
Author(s):  
Ming Jun Li ◽  
Hua Tian ◽  
Xiao Jing Guo
Keyword(s):  
Neural Network ◽  
Intelligent Control ◽  
Pid Controller ◽  
Fuzzy Neural Network ◽  
Network System ◽  
Validity And Reliability ◽  
Fuzzy Neural ◽  
Neural Network System ◽  
Intelligent Pid ◽  
Matlab Programming

In this paper, intelligent fuzzy control theory is introduced in the model of neural network algorithm, and the neural network system is improved by the PID controller, which has realized the feedback and adjustment function of neural network system, and has made the reaction of the system be more accurate and stable. In order to verify the validity and reliability of the designed intelligent control PID algorithm based on the fuzzy neural network in this paper, the algorithm is carried on the programming by using Matlab programming software, and the control process of PID is calculated by NNbox simulation toolbox, at last, it has obtained the curve of PID control response changing over time. From the response curve, it can be seen that after the PID proportional coefficient is regulated by using fuzzy neural network intelligent control algorithm, it can quickly and steadily obtain the control curve, which has realized better intelligent control effect, and has provided technical reference for the research of intelligent PID controller.

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