Model predictive neural control with applications to a 6 DOF helicopter model
EA Wan, AA Bogdanov - Proceedings of the 2001 American …, 2001 - ieeexplore.ieee.org
EA Wan, AA Bogdanov
Proceedings of the 2001 American Control Conference.(Cat. No …, 2001•ieeexplore.ieee.orgWe present a method for optimal control of MIMO non-linear systems based on a
combination of a neural network (NN) feedback controller and a state-dependent Riccati
equation (SDRE) controller. Optimization of the NN is performed within a receding horizon
model predictive control (MPC) framework, subject to dynamic and kinematic constraints.
The SDRE controller augments the NN controller by providing an initial feasible solution and
improving stability. The resulting technique is applied to a 6 degree of freedom (DOF) model …
combination of a neural network (NN) feedback controller and a state-dependent Riccati
equation (SDRE) controller. Optimization of the NN is performed within a receding horizon
model predictive control (MPC) framework, subject to dynamic and kinematic constraints.
The SDRE controller augments the NN controller by providing an initial feasible solution and
improving stability. The resulting technique is applied to a 6 degree of freedom (DOF) model …
We present a method for optimal control of MIMO non-linear systems based on a combination of a neural network (NN) feedback controller and a state-dependent Riccati equation (SDRE) controller. Optimization of the NN is performed within a receding horizon model predictive control (MPC) framework, subject to dynamic and kinematic constraints. The SDRE controller augments the NN controller by providing an initial feasible solution and improving stability. The resulting technique is applied to a 6 degree of freedom (DOF) model of an autonomous helicopter.
ieeexplore.ieee.org
以上显示的是最相近的搜索结果。 查看全部搜索结果