Abstract
Model disturbances result from model uncertainties or external factors acting on the system. They usually affect the closed-loop performance in a control loop system. However, they are often unknown and cannot be then compensated. Therefore, it is crucial to develop estimation methods for the effective estimation of the disturbances which can be then considered appropriately in the control design. This paper proposes a hybrid method for the joint estimation of the state and the disturbance for a class of nonlinear systems in two steps. The approach consists in a neural network with time-varying weights used to approximate the disturbance term and a modulating function method for the finite-time estimation of the state and the weights. The modulating functions approach simplifies the estimation problem into solving an algebraic systems of equations. Both offline and online frameworks are presented and discussed. An example is presented to demonstrate the performance of the proposed algorithm.
Original language | English (US) |
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Title of host publication | 2023 American Control Conference (ACC) |
Publisher | IEEE |
DOIs | |
State | Published - Jul 3 2023 |
Bibliographical note
KAUST Repository Item: Exported on 2023-07-06Acknowledged KAUST grant number(s): BAS/1/1627-01-01
Acknowledgements: Research reported in this publication was supported by King Abdullah University of Science and Technology (KAUST) with the Base Research Fund (BAS/1/1627-01-01).