Adaptive Observers and Parameter Estimation for a Class of Systems Nonlinear in the Parameters

Abstract

We consider the problem of asymptotic reconstruction of the state and parameter values in systems of ordinary differential equations. A solution to this problem is proposed for a class of systems of which the unknowns are allowed to be nonlinearly parameterized functions of state and time. Reconstruction of state and parameter values is based on the concepts of weakly attracting sets and non-uniform convergence and is subjected to persistency of excitation conditions. In absence of nonlinear parametrization the resulting observers reduce to standard estimation schemes. In this respect, the proposed method constitutes a generalization of the conventional canonical adaptive observer design.

Preliminary version is presented at the 17-th IFAC World Congress, 6-11 Seoul, 2008

References in corpus (1)

• Supplementary material for: Adaptive Observers and Parameter Estimation for a Class of Systems Nonlinear in the Parameters

Cited by in corpus (6)

• Model Identification and Adaptive State Observation for a Class of Nonlinear Systems
• Adaptive observers for biophysical neuronal circuits
• Further Results on Lyapunov-Like Conditions of Forward Invariance and Boundedness for a Class of Unstable Systems
• Adaptive observers for nonlinearly parameterized systems subjected to parametric constraints
• Supplementary material for: Adaptive Observers and Parameter Estimation for a Class of Systems Nonlinear in the Parameters
• Adaptive Observation-Based Efficient Reinforcement Learning for Uncertain Systems