Attitude Control of Three-dimensional Momentum Wheel Platform Based on Linear Model

2020 ◽  
Author(s):  
Yu Haibo ◽  
Zhao Jing ◽  
Wu Dengyun ◽  
Chen Zhen
Keyword(s):  
Linear Model ◽  
Attitude Control ◽  
Three Dimensional

scholarly journals Sensitivity of a Wave Energy Converter Dynamics Model to Nonlinear Hydrostatic Models

2015 ◽  
Author(s):  
Ryan G. Coe ◽  
Diana L. Bull
Keyword(s):  
Linear Model ◽  
Wave Energy ◽  
Three Dimensional ◽  
Wave Energy Converter ◽  
Domain Model ◽  
Interpolation Model ◽  
Surface Integral ◽  
Energy Converter ◽  
Motion Constraints ◽  
Large Improvement

A three dimensional time-domain model, based on Cummins equation, has been developed for an axisymmetric point absorbing wave energy converter (WEC) with an irregular cross section. This model incorporates a number of nonlinearities to accurately account for the dynamics of the device: hydrostatic restoring, motion constraints, saturation of the power-take-off force, and kinematic nonlinearities. Here, an interpolation model of the hydrostatic restoring reaction is developed and compared with a surface integral based method. The effects of these nonlinear hydrostatic models on device dynamics are explored by comparing predictions against those of a linear model. For the studied WEC, the interpolation model offers a large improvement over a linear model and is roughly two orders-of-magnitude less computationally expensive than the surface integral based method.

Distributed Constrained Attitude and Position Control Using Graph Laplacians

2010 ◽  
Author(s):  
Innocent Okoloko ◽  
Yoonsoo Kim
Keyword(s):  
Attitude Control ◽  
Position Control ◽  
Dimensional Space ◽  
Stochastic Matrix ◽  
Three Dimensional ◽  
Laplacian Matrix ◽  
Semidefinite Program ◽  
Graph Theoretic ◽  
Graph Laplacians ◽  
Quadratically Constrained

We present a graph theoretic and optimization based method for attitude and position consensus of a team of communicating vehicles navigating in three dimensional space. Coordinated control of such vehicles has applications in planetary scale mobile sensor networks, and multiple vehicle navigation in general. Using the Laplacian matrix of the communication graph, and attitude quaternions, a synthesis of the optimal stochastic matrix that drives the attitudes to consensus, is done, by solving a constrained semidefinite program. This novel methodology attempts to extend quadratically constrained attitude control (Q-CAC), to the consensus framework. The solutions obtained are used to realize coordinated rendezvous, and formation acquisition, in the presence of static and dynamic obstacles.

Neural Networks Based Attitude Decoupling Control for AUV with X-Shaped Fins

2013 ◽  
Vol 819 ◽  
pp. 222-228 ◽  
Author(s):  
Xiu Jun Sun ◽  
Jian Shi ◽  
Yan Yang
Keyword(s):  
Neural Networks ◽  
Attitude Control ◽  
Autonomous Underwater Vehicle ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Mathematic Model ◽  
On Line ◽  
The Neural Networks ◽  
Vehicle Motion ◽  
Three Dimensional Space

Attitude control in three-dimensional space for AUV (autonomous underwater vehicle) with x-shaped fins is complicated but advantageous. Yaw, pitch and roll angles of the vehicle are all associated with deflection angle of each fin while navigating underwater. In this paper, a spatial motion mathematic model of the vehicle is built by using theorem of momentum and angular momentum, and the hydrodynamic forces acting on x-shaped fins and three-blade propeller are investigated to clarify complex principle of the vehicle motion. In addition, the nonlinear dynamics equation which indicates the coupling relationship between attitude angles of vehicle and rotation angles of x-shaped fins is derived by detailed deduction. Moreover, a decoupling controller based on artificial neural networks is developed to address the coupling issue exposed in attitude control. The neural networks based controller periodically calculates and outputs deflection angles of fins according to the attitude angles measured with magnetic compass, thus the vehicles orientation can be maintained. By on-line training, twenty four weights in this controller converged according to index function.

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