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A nonlinear bounded real lemma for sampled-data systems with nonlinear time-varying parameter uncertainty is provided.
In this paper, a variance-adaptive particle filter (VAPF) algorithm is proposed, and is applied to time-varying parameter estimation.
This paper deals with an observer design for Linear Parameter Varying (LPV) systems with high-order time-varying parameter dependency.
A systematic approach to joint state and time-varying parameter estimation for nonlinear systems is proposed in this paper.
A time-varying parameter is designed to control the maximum velocity of agents in the proposed algorithm.
However, the standard particle filter algorithm is not applicable for time-varying parameter estimation problems, especially incompetent for abrupt parameters.
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Then, we present an extension of this condition to robust admissibility of time-varying parameter-dependent descriptor systems.
The closed loop stability of the MPC algorithm is guaranteed by the use of time-varying parameter-dependent Lyapunov function and the feasibility of the linear matrix inequalities (LMIs).
Output tracking can be achieved against a class of time varying parameter variations and external disturbances.
Asymptotically Median Unbiased Estimation of Coefficient Variance in a Time Varying Parameter Model, (with James Stock), Journal of the American Statistical Association, vol. 93, No. 441, March 1998, pp 349-358.
The difference between the unknown time varying parameter and its corresponding nominal value is assumed to be bounded where the nominal value is not required to be known.
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