Design of Memristive Neural Network PID Controller

This paper proposes using BP neural network PID to improve the yaw stability of the vehicle with active front steering system. A dynamic model of vehicle with active front steering is built firstly, and then the BP neural network PID controller is designed in detail. The controller generates the suitable steering angle so that the vehicle follows the target value of the yaw rate. The simulation at different conditions is carried out based on the fore established model. The simulation results show the BP neural network PID controller can improve the vehicles yaw stability effectively.

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Adaptive Neural Network PID Controller

2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe) ◽

10.1109/eeeic.2019.8783803 ◽

2019 ◽

Cited By ~ 1

Author(s):

Benrabah Mohamed ◽

Kamel Kara ◽

AitSahed Oussama ◽

Laid Hadjili

Keyword(s):

Neural Network ◽

Pid Controller ◽

Adaptive Neural Network ◽

Neural Network Pid

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Wavelet neural network PID controller for a UAS transporting a cable-suspended load

IFAC-PapersOnLine ◽

10.1016/j.ifacol.2017.08.419 ◽

2017 ◽

Vol 50 (1) ◽

pp. 2335-2340 ◽

Cited By ~ 3

Author(s):

Ricardo A. Barrón-Gómez ◽

Luis E. Ramos-Velasco ◽

Eduardo S. Espinoza Quesada ◽

Luis R. García Carrillo

Keyword(s):

Neural Network ◽

Pid Controller ◽

Suspended Load ◽

Wavelet Neural Network ◽

Neural Network Pid

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Application of BP Neural Network PID Controller in Landing Gear Based on MRF Damper

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.779.226 ◽

2015 ◽

Vol 779 ◽

pp. 226-232 ◽

Cited By ~ 1

Author(s):

Shi Xing Zhu ◽

Yue Han ◽

Bo Wang

Keyword(s):

Neural Network ◽

Bp Neural Network ◽

Pid Controller ◽

Experimental Testing ◽

Computation Time ◽

Landing Gear ◽

Time Varying ◽

Control Effect ◽

Simulation And Experiment ◽

Neural Network Pid

For characteristics of nonlinearity and time-varying volatility of landing gear based on MR damper, a BP neural network PID controller with a momentum was designed on basis of established dynamic mathematical model. BP neural network would adjust three parameters of PID online in time. The controller was inputted the energy which was combined by the feedback of acceleration and displacement of the control system, which greatly reduced the computation time of controller and the control effect was more obvious. After compared with PID, the simulation and experiment have showed that BP neural network PID has a better effect. The arithmetic can be put into practice through experimental testing.

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