Kalman-filter-based optimal control law for star-oriented and layer-oriented multiconjugate adaptive optics

2004 ◽  
Author(s):  
Brice Le Roux ◽  
Roberto Ragazzoni ◽  
Carmelo Arcidiacono ◽  
Jean-Marc Conan ◽  
Caroline Kulcsar ◽  
...  
Keyword(s):  
Optimal Control ◽  
Kalman Filter ◽  
Adaptive Optics ◽  
Control Law

Optimal control law for multiconjugate adaptive optics

2003 ◽  
Author(s):  
Brice Le Roux ◽  
Jean-Marc Conan ◽  
Caroline Kulcsar ◽  
Henri-Francois Raynaud ◽  
Laurent M. Mugnier ◽  
...  
Keyword(s):  
Optimal Control ◽  
Adaptive Optics ◽  
Control Law

Optimal control law for classical and multiconjugate adaptive optics

2004 ◽  
Vol 21 (7) ◽  
pp. 1261 ◽  
Author(s):  
Brice Le Roux ◽  
Caroline Kulcsár ◽  
Laurent M. Mugnier ◽  
Thierry Fusco ◽  
Henri-François Raynaud ◽  
...  
Keyword(s):  
Optimal Control ◽  
Adaptive Optics ◽  
Control Law

scholarly journals Adaptive Optimal Robust Control for Uncertain Nonlinear Systems Using Neural Network Approximation in Policy Iteration

2021 ◽  
Vol 11 (5) ◽  
pp. 2312
Author(s):  
Dengguo Xu ◽  
Qinglin Wang ◽  
Yuan Li
Keyword(s):  
Neural Network ◽  
Optimal Control ◽  
Robust Control ◽  
Nonlinear Systems ◽  
Policy Iteration ◽  
Control Law ◽  
Globally Asymptotically Stable ◽  
Control Approach ◽  
Input Uncertainties ◽  
Theoretical Results

In this study, based on the policy iteration (PI) in reinforcement learning (RL), an optimal adaptive control approach is established to solve robust control problems of nonlinear systems with internal and input uncertainties. First, the robust control is converted into solving an optimal control containing a nominal or auxiliary system with a predefined performance index. It is demonstrated that the optimal control law enables the considered system globally asymptotically stable for all admissible uncertainties. Second, based on the Bellman optimality principle, the online PI algorithms are proposed to calculate robust controllers for the matched and the mismatched uncertain systems. The approximate structure of the robust control law is obtained by approximating the optimal cost function with neural network in PI algorithms. Finally, in order to illustrate the availability of the proposed algorithm and theoretical results, some numerical examples are provided.

Sign in / Sign up

Export Citation Format

Share Document