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In the proposed control architecture, an auxiliary observer is derived to generate a disturbance representation in a parametric uncertainty form.
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Based on the predictions, an auxiliary nonlinear observer is used to generate a reference subsystem state estimate for each subsystem.
In this work, a robust moving horizon estimation scheme augmented by an auxiliary nonlinear observer is proposed for nonlinear systems with bounded model uncertainties.
In the design of each subsystem estimator, an auxiliary nonlinear observer is also taken advantage of to calculate a reference subsystem state estimate.
Specifically, an auxiliary deterministic nonlinear observer that asymptotically tracks nominal system state is taken advantage of to calculate a confidence region that contains the actual system state taking into account the effects of bounded model uncertainties at every sampling time.
In the design of each subsystem MHE, an auxiliary nonlinear deterministic observer that can asymptotically track the corresponding nominal subsystem state when the subsystem interactions are absent is taken advantage of.
In the presence of unknown time-varying disturbances and input saturation, this paper develops a robust nonlinear control law for the dynamic positioning (DP) system of ships using a disturbance observer, an auxiliary dynamic system, and the dynamic surface control (DSC) technique.
In the following, we explore a novel method for parameter estimation by introducing an auxiliary subsystem in adaptive synchronized observer instead of Lyapunov's direct method and LaSalle's principle.
This paper presents a dynamic model for studying the cold start dynamics and observer design of an auxiliary power unit (APU) for automotive applications.
We propose a novel approach of parameter identification using the adaptive synchronized observer by introducing an auxiliary subsystem, and some sufficient conditions are given to guarantee the convergence of synchronization and parameter identification.
This paper presents a full-order state observer-based scheme that transforms a causal, LTI, minimally realized system into a strongly strict negative-imaginary system by defining an auxiliary output based on the observed states.
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