Robust Dynamic Inverse Controller For Spacecraft Model

2012 ◽  
Vol 3 (5) ◽  
pp. 113-117 ◽  
Author(s):  
Settar S Keream ◽  
◽  
Ahmed N Abdalla ◽  
Ruzlaini Ghoni ◽  
Mohd Razali Daud ◽  
...  
Keyword(s):  
Dynamic Inverse

Inverse problems for finite Jacobi matrices and Krein–Stieltjes strings

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Alexander Mikhaylov ◽  
Victor Mikhaylov
Keyword(s):  
Dynamical System ◽  
Inverse Problems ◽  
Jacobi Matrix ◽  
Jacobi Matrices ◽  
Unknown Parameters ◽  
Stieltjes String ◽  
Propagation Of Waves ◽  
Dynamic Inverse

Abstract We consider dynamic inverse problems for a dynamical system associated with a finite Jacobi matrix and for a system describing propagation of waves in a finite Krein–Stieltjes string. We offer three methods of recovering unknown parameters: entries of a Jacobi matrix in the first problem and point masses and distances between them in the second, from dynamic Dirichlet-to-Neumann operators. We also answer a question on a characterization of dynamic inverse data for these two problems.

Wavelet Neural Network Based Adaptive Robust Control for a Class of MIMO Nonlinear Systems

2011 ◽  
Vol 383-390 ◽  
pp. 290-296
Author(s):  
Yong Hong Zhu ◽  
Wen Zhong Gao
Keyword(s):  
Neural Network ◽  
Robust Control ◽  
Nonlinear Systems ◽  
Wavelet Neural Network ◽  
Adaptive Robust Control ◽  
Loop Control ◽  
Control Approach ◽  
Mimo Nonlinear Systems ◽  
Output Tracking Control ◽  
Dynamic Inverse

Wavelet neural network based adaptive robust output tracking control approach is proposed for a class of MIMO nonlinear systems with unknown nonlinearities in this paper. A wavelet network is constructed as an alternative to a neural network to approximate unknown nonlinearities of the classes of systems. The proposed WNN adaptive law is used to compensate the dynamic inverse errors of the classes of systems. The robust control law is designed to attenuate the effects of approximate errors and external disturbances. It is proved that the controller proposed can guarantee that all the signals in the closed-loop control system are uniformly ultimately bounded (UUB) in the sense of Lyapunov. In the end, a simulation example is presented to illustrate the effectiveness and the applicability of the suggested method.

scholarly journals Deformation Reconstruction and High-Precision Attitude Control of a Launch Vehicle Based on Strain Measurements

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Liang Zhuang ◽  
Zhang Yulin
Keyword(s):  
High Precision ◽  
Attitude Control ◽  
Control Method ◽  
Launch Vehicles ◽  
Bending Vibration ◽  
Inverse Control ◽  
Deformation Parameters ◽  
Control Framework ◽  
Fbg Sensors ◽  
Dynamic Inverse

The development of launch vehicles has led to higher slenderness ratios and higher structural efficiencies, and the traditional control methods have difficulty in meeting high-quality control requirements. In this paper, an incremental dynamic inversion control method based on deformation reconstruction is proposed to achieve high-precision attitude control of slender launch vehicles. First, the deformation parameters of a flexible rocket are obtained via fiber Bragg grating (FBG) sensors. The deformation and attitude information is introduced into the incremental dynamic inverse control loop, and an attitude control framework that can alleviate bending vibration and deformation is established. The simulation results showed that the proposed method could accurately reconstruct the shapes of flexible launch vehicles with severe vibration and deformation, which could improve the accuracy and stability of attitude control.

scholarly journals Dynamic inverse problem for Jacobi matrices

2019 ◽  
Vol 13 (3) ◽  
pp. 431-447 ◽  
Author(s):  
Alexandr Mikhaylov ◽  
◽  
Victor Mikhaylov ◽  
Keyword(s):  
Inverse Problem ◽  
Jacobi Matrices ◽  
Dynamic Inverse ◽  
Dynamic Inverse Problem

Dynamic Inverse Model for Multi-Sensor Measurement System Based on Wavelet Neural Network

2012 ◽  
Vol 499 ◽  
pp. 335-339
Author(s):  
Dong Zhi Zhang ◽  
Bo Kai Xia ◽  
Kai Wang ◽  
Jun Tong ◽  
Nian Zhen Yang
Keyword(s):  
Neural Network ◽  
Measuring Method ◽  
Wavelet Neural Network ◽  
Inverse Model ◽  
Measuring System ◽  
Water Mixture ◽  
Accuracy Measurement ◽  
Sensing Technology ◽  
Dynamic Inverse ◽  
Digital Oilfield

As traditional measuring method based on dielectric coefficients shows cross-sensitivity for multi-factor in the measurement of oil/water mixture, it can not meet the requirements of digital oilfield construction. Therefore, this paper presents an inverse model of wavelet neural network (WNN) combining with multi-sensing technology for achieving high-accuracy measurement of water content in crude oil. The simulation and experimental results demonstrate that the proposed method is available to eliminate the cross-coupling effects of multi-factors. The method has higher measurement accuracy and stronger generalization than the model built by BP-NN, and opens a versatile approach in nonlinear error calibration for multi-factors measuring system.

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