Investigation of pitch damping derivatives for the Standard Dynamic Model at high angles of attack using neural network

2019 ◽  
Vol 92 ◽  
pp. 685-695 ◽  
Author(s):  
Massoud Tatar ◽  
Mehran Masdari
Keyword(s):  
Neural Network ◽  
Dynamic Model ◽  
Pitch Damping ◽  
High Angles Of Attack

Longitudinal vibration analysis and suppression of electric wheel system driven by in-wheel motor considering unbalanced magnetic pull

2018 ◽  
Vol 233 (11) ◽  
pp. 2729-2745 ◽  
Author(s):  
Shuguang Zuo ◽  
Duoqiang Li ◽  
Yu Mao ◽  
Wenzhe Deng
Keyword(s):  
Neural Network ◽  
Finite Element ◽  
Dynamic Model ◽  
Electric Vehicles ◽  
Bp Neural Network ◽  
Longitudinal Vibration ◽  
Structural Parameters ◽  
Torque Ripple ◽  
Longitudinal Dynamics ◽  
Unbalanced Magnetic Pull

With the blowout of electric vehicles recently, the key parts of the electric vehicles driven by in-wheel motors named the electric wheel system become the core of development research. The torque ripple of the in-wheel motor mainly results in the longitudinal dynamics of the electric wheel system. The excitation sources are first analyzed through the finite element method, including the torque ripple induced by the in-wheel motor and the unbalanced magnetic pull produced by the relative motion between the stator and rotor. The accuracy of the finite element model is verified by the back electromotive force test of the in-wheel motor. Second, the longitudinal-torsional coupled dynamic model is established. The proposed model can take into account the unbalanced magnetic pull. Based on the model, the modal characteristics and the longitudinal dynamics of the electric wheel system are analyzed. The coupled dynamic model is verified by the vibration test of the electric wheel system. Two indexes, namely, the root mean square of longitudinal vibration of the stator and the signal-to-noise ratio of the tire slip rate, are proposed to evaluate the electric wheel longitudinal performance. The influence of unbalanced magnetic pull on the evaluation indexes of the longitudinal dynamics is analyzed. Finally, the influence of motor’s structural parameters on the average torque, torque ripple, and equivalent electromagnetic stiffness are analyzed through the orthogonal test. A surrogate model between the structural parameters of the in-wheel motor and the average torque, torque ripple, and equivalent electromagnetic stiffness is established based on the Bp neural network. The torque ripple and the equivalent electromagnetic stiffness are then reduced through optimizing the structural parameters of the in-wheel motor. It turns out that the proposed Bp neural network–based method is effective to suppress the longitudinal vibration of the electric wheel system.

scholarly journals Omnidirectional Mobile Robot Dynamic Model Identification by NARX Neural Network and Stability Analysis Using the APLF Method

Symmetry ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 1430
Author(s):  
Liang Xin ◽  
Yuchao Wang ◽  
Huixuan Fu
Keyword(s):  
Neural Network ◽  
Stability Analysis ◽  
Mobile Robot ◽  
Dynamic Model ◽  
Complex Dynamics ◽  
Model Identification ◽  
Theoretical Method ◽  
Omnidirectional Mobile Robot ◽  
Path Dependent ◽  
Narx Neural Network

In this paper, the NARX neural network system is used to identify the complex dynamics model of omnidirectional mobile robot while rotating with moving, and analyze its stability. When the mobile robot model rotates and moves at the same time, the dynamic model of the mobile robot is complex and there is motion coupling. The change of the model in different states is a kind of symmetry. In order to solve the problem that there is a big difference between the mechanism modeling motion simulation and the actual data, the dynamic model identification of mobile robot in special state based on NARX neural network is proposed, and the stability analysis method is given. To verify that the dynamic model of NARX identification is consistent with that of the mobile robot, the Activation Path-Dependent Lyapunov Function (APLF) algorithm is used to distinguish the NARX neural network model expressed by LDI. However, the APLF method needs to calculate a large number of LMIs in practice and takes a lot of time, and, to solve this problem, an optimized APLF method is proposed. The experimental results verify the effectiveness of the theoretical method.

scholarly journals Neural Network Identification and Control of a Parametrically Excited Structural Dynamic Model of an F-15 Tail Section

2000 ◽  
Vol 7 (6) ◽  
pp. 355-361 ◽  
Author(s):  
Ayman A. El-Badawy ◽  
Ali H. Nayfeh ◽  
Hugh Van Landingham
Keyword(s):  
Neural Network ◽  
Computer Simulation ◽  
Dynamic Model ◽  
Parametric Excitation ◽  
Vibration Suppression ◽  
Great Promise ◽  
Structural Dynamic ◽  
Neural Network Controller ◽  
Structural Dynamic Model ◽  
Computer Simulation Studies

We investigated the design of a neural-network-based adaptive control system for a smart structural dynamic model of the twin tails of an F-15 tail section. A neural network controller was developed and tested in computer simulation for active vibration suppression of the model subjected to parametric excitation. First, an emulator neural network was trained to represent the structure to be controlled and thus used in predicting the future responses of the model. Second, a neurocontroller to determine the necessary control action on the structure was developed. The control was implemented through the application of a smart material actuator. A strain gauge sensor was assumed to be on each tail. Results from computer-simulation studies have shown great promise for control of the vibration of the twin tails under parametric excitation using artificial neural networks.

scholarly journals Neural Network Control of Space Manipulator Based on Dynamic Model and Disturbance Observer

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 130101-130112 ◽  
Author(s):  
He Jun-Pei ◽  
Huo Qi ◽  
Li Yan-Hui ◽  
Wang Kai ◽  
Zhu Ming-Chao ◽  
...  
Keyword(s):  
Neural Network ◽  
Dynamic Model ◽  
Disturbance Observer ◽  
Network Control ◽  
Neural Network Control ◽  
Space Manipulator

Dynamic-model-based artificial neural network for H2 recovery and CO2 capture from hydrogen tail gas

2020 ◽  
Vol 273 ◽  
pp. 115263 ◽  
Author(s):  
Nguyen Dat Vo ◽  
Dong Hoon Oh ◽  
Jun-Ho Kang ◽  
Min Oh ◽  
Chang-Ha Lee
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Dynamic Model ◽  
Co2 Capture ◽  
Model Based ◽  
Artificial Neural ◽  
H2 Recovery

Neural-network-based adaptive fault-tolerant vibration control of single-link flexible manipulator

2019 ◽  
Vol 42 (3) ◽  
pp. 430-438 ◽  
Author(s):  
Le Li ◽  
Jinkun Liu
Keyword(s):  
Neural Network ◽  
Dynamic Model ◽  
Fault Tolerant ◽  
Rbf Neural Network ◽  
Direct Method ◽  
Fault Tolerant Control ◽  
Flexible Manipulator ◽  
Actuator Failure ◽  
Single Link ◽  
Boundary Disturbance

This paper proposes an adaptive fault-tolerant control scheme for a single-link flexible manipulator with actuator failure and uncertain boundary disturbance. The dynamic model of the flexible manipulator as-described by partial differential equations (PDEs) is derived under Hamilton’s principle. The dynamic model is then used to design an adaptive fault-tolerant control (FTC) scheme which tracks the given angle and regulates vibration in the case of actuator failure. The boundary disturbance is compensated by a radial basis function (RBF) neural network. The whole closed-loop system is proven asymptotically stable by Lyapunov direct method and LaSalle’s invariance principle. Simulation results indicate that the proposed controller is superior to the traditional PD controller.

Sign in / Sign up

Export Citation Format

Share Document