Robust discrete-time nonlinear sliding mode state estimation of uncertain nonlinear systems

2007 ◽  
Vol 17 (9) ◽  
pp. 803-828 ◽  
Author(s):  
K. C. Veluvolu ◽  
Y. C. Soh ◽  
W. Cao
Keyword(s):  
Nonlinear Systems ◽  
State Estimation ◽  
Discrete Time ◽  
Sliding Mode ◽  
Uncertain Nonlinear Systems

Dynamic Neural Network-Based Output Feedback Tracking Control for Uncertain Nonlinear Systems

2017 ◽  
Vol 139 (7) ◽  
Author(s):  
Huyen T. Dinh ◽  
S. Bhasin ◽  
R. Kamalapurkar ◽  
W. E. Dixon
Keyword(s):  
Neural Network ◽  
Nonlinear Systems ◽  
State Estimation ◽  
Output Feedback ◽  
Sliding Mode ◽  
Estimation Error ◽  
Uncertain Nonlinear Systems ◽  
Dynamic Neural Network ◽  
Asymptotic Tracking ◽  
Bounded Disturbances

A dynamic neural network (DNN) observer-based output feedback controller for uncertain nonlinear systems with bounded disturbances is developed. The DNN-based observer works in conjunction with a dynamic filter for state estimation using only output measurements during online operation. A sliding mode term is included in the DNN structure to robustly account for exogenous disturbances and reconstruction errors. Weight update laws for the DNN, based on estimation errors, tracking errors, and the filter output are developed, which guarantee asymptotic regulation of the state estimation error. A combination of a DNN feedforward term, along with the estimated state feedback and sliding mode terms yield an asymptotic tracking result. The developed output feedback (OFB) method yields asymptotic tracking and asymptotic estimation of unmeasurable states for a class of uncertain nonlinear systems with bounded disturbances. A two-link robot manipulator is used to investigate the performance of the proposed control approach.

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