scholarly journals An Intelligent Adaptive Neural Network Controller for a Direct Torque Controlled eCAR Propulsion System

2021 ◽  
Vol 12 (1) ◽  
pp. 44
Author(s):  
Gururaj Banda ◽  
Sri Gowri Kolli
Keyword(s):  
Neural Network ◽  
Pulse Width Modulation ◽  
Simulation Software ◽  
Propulsion System ◽  
Torque Control ◽  
Matlab Simulation ◽  
Time Data ◽  
Adaptive Neural Network ◽  
Adaptive Controllers ◽  
Neural Network Controller

This article deals with an intelligent adaptive neural network (ANN) controller for a direct torque controlled (DTC) electric vehicle (EV) propulsion system. With the realization of artificial intelligence (AI) conferred adaptive controllers, the torque control of an electric car (eCAR) propulsion motor can be achieved by estimating the stator reference flux voltage used to synthesize the space vector pulse width modulation (SVPWM) for a DTC scheme. The proposed ANN tool optimizes the parameters of a proportional integral (PI) controller with real-time data and offers splendid dynamic stability. The response of an ANN controller is examined over standard drive cycles to validate the performance of an eCAR in terms of drive range and energy efficiency using MATLAB simulation software.

The Optimal Control of Vehicle`s Steering and Braking System

2013 ◽  
Vol 473 ◽  
pp. 243-246
Author(s):  
Guo Li ◽  
Cheng Yao Jia ◽  
Wen Zheng Zhang
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Simulation Analysis ◽  
Simulation Software ◽  
Braking System ◽  
Simulink Simulation ◽  
Neural Network Controller ◽  
Network Controller ◽  
Fuzzy Neural ◽  
The Membership Function

In order to make a research on the vehicle`s ABS and AFS system,the fuzzy neural network controller was designed on the basis of the electric vehicle`s steering and braking models. Then the genetic algorithms was used to improve the parameters of the membership function. Finally, the Matlab/Simulink simulation software has been used in the simulation analysis. The result of simulation proves that the designed system has good tracking performance and more stronger systemic robustness .

Prediction of Coal Production Based on BP Neural Network

2011 ◽  
Vol 217-218 ◽  
pp. 1647-1651
Author(s):  
Ming Ming Wen
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Historical Data ◽  
Supply And Demand ◽  
Simulation Software ◽  
Coal Production ◽  
Matlab Simulation ◽  
Coal Supply

It is significance to predict coal production for balancing coal supply and demand in China. The primary goal of this research is the prediction of coal production in china. The method used in the study is known as the BP neural network. The BP neural network is designed with the MATLAB simulation software based on coal production historical data from 1980 to 2007. The studies we have performed showed that the prediction of coal production based on BP neural network is reasonable and valuable. Finally, we get the prediction of coal production from 2010 to 2015, and the prediction indicates that the coal production will increase in the next 5 years.

scholarly journals Adaptive Neural Network Control with Backstepping for Surface Ships with Input Dead-Zone

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Guoqing Xia ◽  
Xingchao Shao ◽  
Ang Zhao ◽  
Huiyong Wu
Keyword(s):  
Neural Network ◽  
Explicit Knowledge ◽  
Dead Zone ◽  
Nonlinear Function ◽  
Lyapunov Theory ◽  
Adaptive Neural Network ◽  
Neural Network Controller ◽  
Network Controller ◽  
Adaptive Neural Network Control ◽  
Fuzzy Logic Technique

This paper addresses the problem of adaptive neural network controller with backstepping technique for fully actuated surface vessels with input dead-zone. The combination of approximation-based adaptive technique and neural network system is used for approximating the nonlinear function of the ship plant. Through backstepping and Lyapunov theory synthesis, an indirect adaptive network controller is derived for dynamic positioning ships without dead-zone property. In order to improve the control effect, a dead-zone compensator is derived using fuzzy logic technique to handle the dead-zone nonlinearity. The main advantage of the proposed controller is that it can be designed without explicit knowledge about the ship motion model, and dead-zone nonlinearity is well compensated. A set of simulations is carried out to verify the performance of the proposed controller.

Torque Control of Servo Load Simulator with Generalized Dynamic Fuzzy Neural Network Based on Grey Prediction

2011 ◽  
Vol 148-149 ◽  
pp. 707-712
Author(s):  
Li Wang ◽  
Lin Fang Qian ◽  
Qi Guo
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Learning Algorithm ◽  
Torque Control ◽  
Grey Prediction ◽  
Neural Network Controller ◽  
Network Controller ◽  
Fuzzy Neural ◽  
Load Simulator ◽  
Load Simulation

Considering the testing requirements of dynamically loaded about servo system in weapons, a load simulator is presented in this paper and the transfer function of “extraneous torque" is obtained. In order to curb the amplitude of extra torque and achieve dynamic load simulation, this paper proposes a grey prediction-based fuzzy neural network controller, which selects Generalized Dynamic Fuzzy Neural Network as the learning algorithm and selects the ε-completeness as a criterion to determine the width of Gaussian functions. Simulation and experimental results show that the proposed torque controller can significantly reduce the amplitude of the extra torque.

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