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This letter introduces a complete DC-AC conversion system fed by photovoltaic (PV) energy. The system consists of N PV panels, N DC-DC boost converters, N cascaded H-bridge inverters, a DC-link composed of N capacitors and an LCL filter. This work aims at reaching threefold control objectives: i) Extracting the available maximum power by regulating the voltages across the PV panels, ii) Ensuring a unitary power factor, iii) Regulating the DC-link voltage to a desired reference. To achieve the mentioned objectives, a multi-loop regulator is designed. The PV panels are individually controlled to track the maximum power point in order to efficiently operate at either the same or different varying climatic conditions without failures. In addition to the maximum power point tracking (MPPT) controller, two cascaded loops guaranteeing a satisfactory power factor and DC-link voltage regulation are developed. The nonlinear backstepping approach combined with Lyapunov theory are used based on the averaged model for the synthesis of the multi-loop controller. The performance of the studied system is tested via MATLAB / SimPowerSystems environment. The obtained simulation results prove that the proposed controller meets its objectives and demonstrate the efficiency of the chosen control strategy under faulty conditions.

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.

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.

We present a new approach for deriving sampled-data observers from continuous-time observers that feature an Input-to-Output Stability property with respect to the output measurement noise and exponential convergence in the noiseless case. The design approach applies to a wide class of systems and yields sampled-data observers that inherit all performance characteristics of the underlying continuous-time observers. The main component of the proposed sampled-data observer is a novel output predictor that encompasses both inter-sample predictors and ZOH-predictors., Comment: 14 pages, 1 figure, submitted to IEEE TAC for possible publication

This paper studies the problem of designing sampled-data observers and observer-based, sampled-data, output feedback stabilizers for systems with both discrete and distributed, state and output time-delays. The obtained results can be applied to time delay systems of strict-feedback structure, transport Partial Differential Equations (PDEs) with nonlocal terms, and feedback interconnections of Ordinary Differential Equations with a transport PDE. The proposed design approach consists in exploiting an existing observer, which features robust exponential convergence of the error when continuous-time output measurements are available. The observer is then modified, mainly by adding an inter-sample output predictor, to compensate for the effect of data-sampling. Using Lyapunov stability tools and small-gain analysis, we show that robust exponential stability of the error is preserved, provided the sampling period is not too large. The general result is illustrated with different examples including state observation and output-feedback stabilization of a chemical reactor., Comment: 32 pages, submitted for possible publication to SIAM Journal on Control and Optimization

The present work provides a systematic approach for the design of sampled-data observers to a wide class of 1-D, parabolic PDEs with non-local outputs. The studied class of parabolic PDEs allows the presence of globally Lipschitz nonlinear and non-local terms in the PDE. Two different sampled-data observers are presented: one with an inter-sample predictor for the unavailable continuous measurement signal and one without an inter-sample predictor. Explicit conditions on the upper diameter of the (uncertain) sampling schedule for both designs are derived for exponential convergence of the observer error to zero in the absence of measurement noise and modeling errors. Moreover, explicit estimates of the convergence rate can be deduced based on the knowledge of the upper diameter of the sampling schedule. When measurement noise and/or modeling errors are present, Input-to-Output Stability (IOS) estimates of the observer error hold for both designs with respect to noise and modeling errors. The main results are illustrated by two examples which show how the proposed methodology can be extended to other cases (e.g., boundary point measurements)., Comment: 20 pages, submitted to Systems and Control Letters for possible publication

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 (PE) 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.

Existing works on Wiener system identification have essentially been focused on the case where the output nonlinearity is memoryless. When memory nonlinearities have been considered, the focus has been restricted to backlash like nonlinearities. In this paper, we are considering Wiener systems where the output nonlinearity is a general hysteresis operator captured by the well-known Bouc-Wen model. The Wiener system identification problem is addressed by making use of a steady-state property, obtained in periodic regime, referred to as hysteretic loop assumption'. The complexity of this problem comes from the system nonlinearity as well as its unknown parameters that enter in a non-affine way in the model. It is shown that the linear part of the system is accurately identified using a frequency method. Then, the nonlinear hysteretic subsystem is identified, on the basis of a parameterized representation, using a prediction-error approach., Postprint (author's final draft)

info:eu-repo/semantics/nonPublished