Distributed adaptive high-gain extended Kalman filtering for nonlinear systems

2017 ◽  
Vol 27 (18) ◽  
pp. 4873-4902 ◽  
Author(s):  
M. Rashedi ◽  
J. Liu ◽  
B. Huang
Keyword(s):  
Nonlinear Systems ◽  
Kalman Filtering ◽  
High Gain ◽  
Extended Kalman Filtering

scholarly journals Distributed Adaptive High-Gain Extended Kalman Filtering for Nonlinear systems

2015 ◽  
Vol 48 (8) ◽  
pp. 158-163 ◽  
Author(s):  
Mohammad Rashedi ◽  
Jinfeng Liu ◽  
Biao Huang
Keyword(s):  
Nonlinear Systems ◽  
Kalman Filtering ◽  
High Gain ◽  
Extended Kalman Filtering

scholarly journals Stochastic Optimal Control with Application in Visual Servoing

2021 ◽  
Author(s):  
Aidin Foroughi
Keyword(s):  
Optimal Control ◽  
Nonlinear Systems ◽  
Kalman Filtering ◽  
Stochastic Optimal Control ◽  
Visual Servoing ◽  
Inverted Pendulum ◽  
Extended Kalman Filtering ◽  
Risk Seeking ◽  
Novel Method ◽  
Control Methodology

In this thesis, a new inference-based solution to stochastic optimal control (SOC) for general nonlinear systems is developed. This novel method applies to standard SOC problem, as well as robust and risk-seeking variations. The presented approach unifies many existing works, and makes possible, inference-based approximations to be applied to robust, risk-seeking, and standard SOC problems. Thus, an approximate method based on extended Kalman filtering is developed and tested on the inverted pendulum problem, and compared with existing methods. As an application, the developed algorithm was adapted to a practically important problem in visual control in robotics known as image-based visual servoing (IBVS). The proposed control methodology for visual servoing was implemented for real-time experiments, and was compared with the standard IBVS methodology. The experimental results show that the proposed method can improve the myopic behaviors of standard IBVS methodology.

scholarly journals Stochastic Optimal Control with Application in Visual Servoing

2021 ◽  
Author(s):  
Aidin Foroughi
Keyword(s):  
Optimal Control ◽  
Nonlinear Systems ◽  
Kalman Filtering ◽  
Stochastic Optimal Control ◽  
Visual Servoing ◽  
Inverted Pendulum ◽  
Extended Kalman Filtering ◽  
Risk Seeking ◽  
Novel Method ◽  
Control Methodology

In this thesis, a new inference-based solution to stochastic optimal control (SOC) for general nonlinear systems is developed. This novel method applies to standard SOC problem, as well as robust and risk-seeking variations. The presented approach unifies many existing works, and makes possible, inference-based approximations to be applied to robust, risk-seeking, and standard SOC problems. Thus, an approximate method based on extended Kalman filtering is developed and tested on the inverted pendulum problem, and compared with existing methods. As an application, the developed algorithm was adapted to a practically important problem in visual control in robotics known as image-based visual servoing (IBVS). The proposed control methodology for visual servoing was implemented for real-time experiments, and was compared with the standard IBVS methodology. The experimental results show that the proposed method can improve the myopic behaviors of standard IBVS methodology.

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