Optimal control of nonlinear systems using RBF neural network and adaptive extended Kalman filter

2009 ◽  
Author(s):  
Peda V. Medagam ◽  
Farzad Pourboghrat
Keyword(s):  
Neural Network ◽  
Optimal Control ◽  
Kalman Filter ◽  
Nonlinear Systems ◽  
Extended Kalman Filter ◽  
Rbf Neural Network ◽  
Adaptive Extended Kalman Filter

Research on Learning Algorithm of RBF Neural Network Based on Extended Kalman Filter

2014 ◽  
Vol 989-994 ◽  
pp. 2705-2708
Author(s):  
Xu Sheng Gan ◽  
Hai Long Gao
Keyword(s):  
Neural Network ◽  
Kalman Filter ◽  
Radial Basis Function ◽  
Extended Kalman Filter ◽  
Basis Function ◽  
Learning Algorithm ◽  
Rbf Neural Network ◽  
Learning Capability ◽  
Radial Basis ◽  
Neural Network Algorithm

To improve the learning capability of Radial Basis Function (RBF) neural network, a RBF neural network algorithm based on Extended Kalman Filter (EKF) is proposed. First the basic idea of EKF algorithm and RBF neural network are introduced, and then EKF is used to optimize the parameters combination of RBF neural network to obtain the better model. The experiment proves its feasibility.

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.

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