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Estimating unbounded unknown inputs in nonlinear systems
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DOI：10.1016/j.automatica.2019.02.050.png)
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Abstract
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Unknown inputs such as attacks on safety-critical systems is a major concern in the design of modern engineering systems. If the underlying system to be protected exhibits nonlinear dynamics, few observers/estimators in the current literature can handle unknown input estimation, particularly when the unknown inputs are unbounded. This paper proposes an extended state observer methodology for designing observers capable of reconstructing the state along with unknown inputs for a class of nonlinear systems that can be characterized via incremental quadratic constraints. Two classes of unknown inputs are considered: completely unknown inputs that have bounded derivatives, or (the generalization of the first) unknown inputs that are generated by nonlinear internal state-space models, where the model states are driven by completely unknown inputs: for instance, completely unknown inputs with bounded higher-order derivatives. The potential of the proposed design is demonstrated via numerical simulations. (C) 2019 Elsevier Ltd. All rights reserved.
Keywords:
Unknown input observers
Convex programming
Nonlinear disturbance model
Internal model
Incremental quadratic constraints
Fault detection and isolation
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