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Summary: We are considering the problem of sampled‐data observer design for nonlinear time‐varying systems that are state‐affine. The novelty lies in that both distributed and discrete delays are considered in the output equation. The latter is also subject to a parameter uncertainty of nonaffine nature due to output sampling. Interestingly, all system delays are modeled using a single distributed representation, involving a distribution function, allowing thus for a unified treatment of delays. A Kalman‐like observer is developed to cope with both state and parameter uncertainty. Its main components are (i) a time‐varying‐gain state‐estimator involving both output and parameter rate injections; (ii) a distributed‐nature adaptive output‐predictor that compensate for all delay effects, including that of output sampling; (iii) an parameter‐estimator that is optimized in the sense that it makes use of all available information. The resulting observer is shown to be exponentially convergent, for small delays and sampling intervals, provided the input signal is sufficiently exciting. The analysis is performed using a Lyapunov–Krasovskii functional, Halanay's lemma, Wirtinger's inequality, and other tools. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of sampled-data observer design to estimate the state of health (SoH), the state of charge (SoC), and the internal temperature for batteries. The observation is based on the estimation of the battery internal resistance, which is supposed to be unknown and varying with temperature. The difficulty is that the system state equation contains an unknown parameter (which represents the internal resistance) and an output-dependent term only accessible to measurement at sampling time. The proposed approach consists of several levels of estimation and calculation using non-linear observers and fitting functions exploiting experimental data. It has been shown, using theoretical analysis, simulations, and experimental results, that the proposed method gives a novel approach to estimate the quantities (SoH, SoC, and temperature) useful for the battery management system (BMS). The point is that this approach only involves the estimation of the internal resistance of the battery. [ABSTRACT FROM AUTHOR]

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We are considering the problem of state observation for a class of infinite dimensional systems modeled by parabolic type PDEs. The model is subject to parametric uncertainty entering in both the domain equation and the boundary condition. An adaptive boundary observer, providing online estimates of the system state and parameters, is designed using finite- and infinite-dimensional backstepping-like transformations. The observer is exponentially convergent under an ad hoc persistent excitation condition. [ABSTRACT FROM AUTHOR]

This paper presents a novel sensorless high-gain observer for wind energy conversion systems (WECS) incorporating Vienna rectifiers. Vienna rectifiers offer several advantages for WECS, including improved efficiency and signal quality. The main contribution of this study is the design of a high-gain observer that provides online estimates of magnetic and mechanical variables, such as rotor speed and load torque, based solely on electrical parameters. Addressing the challenges posed by the nonlinearity and relatively high dimensionality of the system dynamics, the proposed observer's effectiveness is demonstrated through simulations and theoretical analysis. Furthermore, experimental validation using the RTDS (Real-Time Digital Simulator) platform further confirms the performance and reliability of the observer. • Observability analysis. • Online Estimation of Magnetic and Mechanical Variables. • Sensorless control of WECS involve a Vienna rectifier. • Experimental Validation: RTDS (Real-Time Digital Simulator). [ABSTRACT FROM AUTHOR]

Abstract The problem of sampled-data observer design is addressed for a class of state- and parameter-affine nonlinear systems. The main novelty in this class lies in the fact that the unknown parameters enter the output equation and the associated regressor is nonlinear in the output. Wiener systems belong to this class. The difficulty with this class of systems comes from the fact that output measurements are only available at sampling times causing the loss of the parameter-affine nature of the model (except at the sampling instants). This makes existing adaptive observers inapplicable to this class of systems. In this paper, a new sampled-data adaptive observer is designed for these systems and shown to be exponentially convergent under specific persistent excitation conditions that ensure system observability and identifiability. The new observer involves an inter-sample output predictor that is different from those in existing observers and features continuous trajectories of the state and parameter estimates. [ABSTRACT FROM AUTHOR]