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Romeo Ortega

Publications and source records attributed to Romeo Ortega.

At least 37 records · Page 2Linked to original sources
arXiv2024

Exploiting Monotonicity to Design an Adaptive PI Passivity-Based Controller for a Fuel-Cell System

We present a controller for a power electronic system composed of a fuel cell (FC) connected to a boost converter which feeds a resistive load. The controller aims to regulate the output voltage of the converter regardless of the uncertainty of the load. Leveraging the monotonicity feature of the fuel cell polarization curve we prove that the nonlinear system can be controlled by means of a passivity-based proportional-integral approach. We afterward extend the result to an adaptive version, allowing the controller to deal with parameter uncertainties, such as inductor parasitic resistance, load, and FC polarization curve parameters. This adaptive design is based on an indirect control approach with online parameter identification performed by a ``hybrid'' estimator which combines two techniques: the gradient-descent and immersion-and-invariance algorithms. The overall system is proved to be stable with the output voltage regulated to its reference. Experimental results validate our proposal under two real-life scenarios: pulsating load and output voltage reference changes.

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arXiv2024

A New Adaptive Phase-locked Loop for Synchronization of a Grid-Connected Voltage Source Converter: Simulation and Experimental Results

In [1] a new adaptive phase-locked loop scheme for synchronization of a grid connected voltage source converter with guaranteed (almost) global stability properties was reported. To guarantee a suitable synchronization with the angle of the three-phase grid voltage we design an adaptive observer for such a signal requiring measurements only at the point of common coupling. In this paper we present some simulation and experimental illustration of the excellent performance of the proposed solution.

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arXiv2024

Nonlinear Voltage Regulation of an Auxiliary Energy Storage of a Multiport Interconnection

In this article, we propose a nonlinear voltage control to ensure power exchange in a multiport interconnected system, which consists of a bidirectional DC-DC converter and generating-storing devices. The converter topology under consideration is two-stage, composed of an interconnection of a buck with a boost converter. The motivation for this work is the explosive increase in the use of DC-DC converters due to the massification of renewable energies, electric vehicles powertrains, and energy storage systems, where fuel cells or batteries can be used as power backup or high-power support during transient phenomena. The converter's voltage step-up and step-down capabilities allow the use of supercapacitors with voltage limits that exceed those required by the load, thus enabling its use in a broader range of applications. The control design for this system does not correspond to that in standard applications involving power converters. As it is known, the latter consists of finding a control law such that the closed-loop system has an asymptotically stable equilibrium point fulfilling the voltage regulation objectives. Instead, in this application, the state does not tend to an equilibrium value in order for the system to be regulated. The converter voltage is regulated at desired some setpoint whereas the other variables are only required to be bounded. To achieve a dynamic response that best adapts to changes in system demand and ensure stability over the defined wide operating range we propose a novel control strategy that exploits the partially cascaded structure of the system. Numerical and experimental results validate our approach.

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arXiv2023

On-line Parameter Estimation of the Polarization Curve of a Fuel Cell with Guaranteed Convergence Properties: Theoretical and Experimental Results

In this paper, we address the problem of online parameter estimation of a Proton Exchange Membrane Fuel Cell (PEMFC) polarization curve, that is the static relation between the voltage and the current of the PEMFC. The task of designing this estimator -- even off-line -- is complicated by the fact that the uncertain parameters enter the curve in a highly nonlinear fashion, namely in the form of nonseparable nonlinearities. We consider several scenarios for the model of the polarization curve, starting from the standard full model and including several popular simplifications to this complicated mathematical function. In all cases, we derive separable regression equations -- either linearly or nonlinearly parameterized -- which are instrumental for the implementation of the parameter estimators. We concentrate our attention on on-line estimation schemes for which, under suitable excitation conditions, global parameter convergence is ensured. Due to these global convergence properties, the estimators are robust to unavoidable additive noise and structural uncertainty. Moreover, their on-line nature endows the schemes with the ability to track (slow) parameter variations, that occur during the operation of the PEMFC. These two features -- unavailable in time-consuming off-line data-fitting procedures -- make the proposed estimators helpful for on-line time-saving characterization of a given PEMFC, and the implementation of fault-detection procedures and model-based adaptive control strategies. Simulation and experimental results that validate the theoretical claims are presented.

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arXiv2023

Synthesis of an adaptive observer of state variables for a linear stationary object in the presence of measurement noise

The article is devoted to the problem of synthesis of observers of state variables for linear stationary objects operating under conditions of noise or disturbances in the measurement channel. The paper considers a fully observable linear stationary system with known parameters. It is assumed that the state variables are not measured, and the measured output variable contains a small amplitude (in general, modulo less than one) additive noise or disturbance. It is also assumed that there is no a priori information about the disturbance or noise in the measurement channel (for example, frequency spectrum, covariance, etc.). It is well known that a large number of methods of observer synthesis have been obtained for this type of objects, including the Kalman filter, which has proven itself in practice. Under the condition of complete observability and the presence of some a priori information about the process (which is typical for the case when a disturbance in the measurement channel can be represented as white noise), approaches based on Kalman filtering demonstrate the highest quality of convergence of estimates of state variables to true values. Without taking into account the numerous results obtained using the application of the Kalman filter, an alternative idea of constructing an observer of state variables is considered in this paper. The alternative of the new approach is primarily due to the fact that there is no need to use the usual approaches based on the Luenberger observer. The paper proposes an approach based on the evaluation of unknown parameters (in this case, an unknown vector of initial conditions of the variables of the object state) of a linear regression model.

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arXiv2023

Output Voltage Regulation of a Fuel Cell/Boost Converter System: A PI-PBC Approach

In this paper we consider the problem of voltage regulation of a proton exchange membrane fuel cell connected to an uncertain load through a boost converter. We show that, in spite of the inherent nonlinearities in the current-voltage behaviour of the fuel cell, the voltage of a fuel cell/boost converter system can be regulated with a simple proportional-integral (PI) action designed following the {Passivity-based Control (PBC) approach. The system under consideration consists of a DC-DC converter interfacing a fuel cell with a resistive load. We show that the output voltage of the converter converges to its desired constant value for all the systems initial conditions -- with convergence ensured for all positive values of the PI gains. This latter feature facilitates the, usually difficult, task of tuning the gains of the PI. An Immersion and Invariance parameter estimator is afterwards proposed which allows the operation of the PI-PBC when the load is unknown, maintaining the output voltage at the desired level. The stable operation of the overall system is proved and the approach is validated with extensive numerical simulations considering real-life scenarios, where a robust behavior in spite of load variations is obtained.

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arXiv2023

A Globally Convergent Estimator of the Parameters of the Classical Model of a Continuous Stirred Tank Reactor

In this paper we provide the first solution to the challenging problem of designing a globally exponentially convergent estimator for the parameters of the standard model of a continuous stirred tank reactor. Because of the presence of non-separable exponential nonlinearities in the system dynamics that appear in Arrhenius law, none of the existing parameter estimators is able to deal with them in an efficient way and, in spite of many attempts, the problem was open for many years. To establish our result we propose a novel procedure to obtain a suitable nonlinearly parameterized regression equation and introduce a radically new estimation algorithm - derived applying the Immersion and Invariance methodology - that is applicable to these regression equations. A further contribution of the paper is that parameter convergence is guaranteed with weak excitation requirements.

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arXiv2022

Parameter Estimation of Two Classes of Nonlinear Systems with Non-separable Nonlinear Parameterizations

In this paper we address the challenging problem of designing globally convergent estimators for the parameters of nonlinear systems containing a non-separable exponential nonlinearity. This class of terms appears in many practical applications, and none of the existing parameter estimators is able to deal with them in an efficient way. The proposed estimation procedure is illustrated with two modern applications: fuel cells and human musculoskeletal dynamics. The procedure does not assume that the parameters live in known compact sets, that the nonlinearities satisfy some Lipschitzian properties, nor rely on injection of high-gain or the use of complex, computationally demanding methodologies. Instead, we propose to design a classical on-line estimator whose dynamics is described by an ordinary differential equation given in a compact precise form. A further contribution of the paper is the proof that parameter convergence is guaranteed with the extremely weak interval excitation requirement.

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arXiv2022

A high performance globally exponentially stable sensorless observer for the IPMSM: Theoretical and experimental results

In a recent paper [18] the authors proposed the first solution to the problem of designing a {\em globally exponentially stable} (GES) flux observer for the interior permanent magnet synchronous motor. However, the establishment of the stability proof relies on the assumption that the adaptation gain is sufficiently {\em small} -- a condition that may degrade the quality of the transient behavior. In this paper we propose a new GES flux observer that overcomes this limitation ensuring a high performance behavior. The design relies on the use of a novel theoretical tool -- the generation of a {\em ``virtual" invariant manifold} -- that allows the use of the more advanced Kreisselmeier's regression extension estimator, instead of a simple gradient descent one. We illustrate its superior transient behavior via extensive simulations and {\em experiments}.

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arXiv2022

On-line Identification of Photovoltaic Arrays' Dynamic Model Parameters

This paper deals with the problem of on-line identification of the parameters of a realistic dynamical model of a photovoltaic array connected to a power system through a power converter. It has been shown in the literature that, when interacting with switching devices, this model is able to better account for the PV array operation, as compared to the classical five parameter static model of the array. While there are many results of identification of the parameters of the latter model, to the best of our knowledge, no one has provided a solution for the aforementioned more complex dynamic model since it concerns the parameter estimation of a nonlinear, underexcited system with unmeasurable state variables. Achieving such objective is the main contribution of the paper. We propose a new parameterisation of the dynamic model, which, combined with the powerful identification technique of dynamic regressor extension and mixing, ensures a fast and accurate online estimation of the unknown parameters. Realistic numerical examples via computer simulations are presented to assess the performance of the proposed approach -- even being able to track the parameter variations when the system changes operating point.

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arXiv2022

A New Least Squares Parameter Estimator for Nonlinear Regression Equations with Relaxed Excitation Conditions and Forgetting Factor

In this note a new high performance least squares parameter estimator is proposed. The main features of the estimator are: (i) global exponential convergence is guaranteed for all identifiable linear regression equations; (ii) it incorporates a forgetting factor allowing it to preserve alertness to time-varying parameters; (iii) thanks to the addition of a mixing step it relies on a set of scalar regression equations ensuring a superior transient performance; (iv) it is applicable to nonlinearly parameterized regressions verifying a monotonicity condition and to a class of systems with switched time-varying parameters; (v) it is shown that it is bounded-input-bounded-state stable with respect to additive disturbances; (vi) continuous and discrete-time versions of the estimator are given. The superior performance of the proposed estimator is illustrated with a series of examples reported in the literature.

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arXiv2022

Adaptive state observer for linear time-varying system with partially unknown parameters of the state matrix and the input vector

The article deals with the problem of synthesis of an adaptive observer of state variables of a linear time-varying SISO dynamic system. It is assumed that the control signal and the output variable are measurable. It is assumed that the state matrix of the plant contains known variables and unknown constant parameters, and the control matrix (vector) is unknown. The synthesis of the observer is based on the GPEBO method (generalized parameter based observer) proposed in $[1]$. Synthesis of adaptive provides for preliminary parametrization of the initial system and its transformation to a linear regression model with further identification of unknown parameters. To identify unknown constant parameters a classical estimation algorithm was used (the least squares method with a forgetting factor). This approach has proven itself well in cases where the known regressor is frequency poor (that is, the spectral composition of the regressor contains less than $r/2$ harmonics, where r is the number of unknown parameters) or does not satisfy the so-called undamped excitation condition. To illustrate the efficiency of the proposed method an example is presented in the article. A time-varying second-order object with four unknown parameters was considered. Parameterization of the initial dynamic model was performed and a linear static regression containing six unknowns parameters was obtained (including the vector of unknown initial conditions of system state variables). An adaptive observer was synthesized and the results of computer modeling illustrating the achievement of a given goal were presented. The main difference from the results published earlier in $[2]$ is the new assumption that the linear a time-varing system contains not only unknown parameters in the state matrix, but also the matrix (vector) for control contains unknown constant coefficients.

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arXiv2022

An Observer-Based Composite Identifier for Online Estimation of the Thevenin Equivalent Parameters of a Power System

We consider a Thévenin equivalent circuit capturing the dynamics of a power grid as seen from the point of common coupling with a power electronic converter, and provide a solution to the problem of online identification of the corresponding circuit parameters. For this purpose, we first derive a linear regression model in the conventional abc coordinates and next design a bounded observer-based composite identifier that requires local measurements and knowledge of the grid frequency only. An extension that guarantees exponential convergence of the estimates, under the additional assumption of knowledge of the grid X/R ratio, is further provided. The performance of the proposed identifier, which subsumes a conventional gradient descent algorithm, is illustrated via detailed computer simulations.

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arXiv2022

Generation of new exciting regressors for consistent on-line estimation of unknown constant parameters

The problem of parameter estimation from a standard vector linear regression equation in the absence of sufficient excitation in the regressor is addressed. The first step to solve the problem consists in transforming this equation into a set of scalar ones using the well-known dynamic regressor extension and mixing technique. Then a novel procedure to generate new scalar exciting regressors is proposed.} The superior performance of a classical gradient estimator using this new regressor, instead of the original one, is illustrated with comprehensive simulations.

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arXiv2022

Conditions for Convergence of Dynamic Regressor Extension and Mixing Parameter Estimator Using LTI Filters

In this note we study the conditions for convergence of recently introduced dynamic regressor extension and mixing (DREM) parameter estimator when the extended regressor is generated using LTI filters. In particular, we are interested in relating these conditions with the ones required for convergence of the classical gradient (or least squares), namely the well-known persistent excitation (PE) requirement on the original regressor vector, $ϕ(t) \in \mathbb{R}^q$, with $q \in \mathbb{N}$ the number of unknown parameters. Moreover, we study the case when only interval excitation (IE) is available, under which DREM, concurrent and composite learning schemes ensure global convergence, being the convergence for DREM in finite time. Regarding PE we prove that, under some mild technical assumptions, if $ϕ(t)$ is PE then the scalar regressor of DREM, $Δ(t) \in \mathbb{R}$, is also PE, ensuring exponential convergence. Concerning IE we prove that if $ϕ(t)$ is IE then $Δ(t)$ is also IE. All these results are established in the almost sure sense, namely proving that the set of filter parameters for which the claims do not hold is of zero measure. The main technical tool used in our proof is inspired by a study of Luenberger observers for nonautonomous nonlinear systems recently reported in the literature.

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arXiv2022

Indirect Adaptive Control of Nonlinearly Parameterized Nonlinear Dissipative Systems

In this note we address the problem of indirect adaptive (regulation or tracking) control of nonlinear, input affine dissipative systems. It is assumed that the supply rate, the storage and the internal dissipation functions may be expressed as nonlinearly parameterized regression equations where the mappings (depending on the unknown parameters) satisfy a monotonicity condition -- this encompasses a large class of physical systems, including passive systems. We propose to estimate the system parameters using the "power-balance" equation, which is the differential version of the classical dissipation inequality, with a new estimator that ensures global, exponential, parameter convergence under the very weak assumption of interval excitation of the power-balance equation regressor. To design the indirect adaptive controller we make the standard assumption of existence of an asymptotically stabilizing controller that depends -- possibly nonlinearly -- on the unknown plant parameters, and apply a certainty-equivalent control law. The benefits of the proposed approach, with respect to other existing solutions, are illustrated with examples.

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arXiv2022

An Almost Globally Stable Adaptive Phase-Locked Loop for Synchronization of a Grid-Connected Voltage Source Converter

In this paper we are interested in the problem of adaptive synchronization of a voltage source converter with a possibly weak grid with unknown angle and frequency, but knowledge of its parameters. To guarantee a suitable synchronization with the angle of the three-phase grid voltage we design an adaptive observer for such a signal requiring measurements only at the point of common coupling. Then we propose two alternative certainty-equivalent, adaptive phase-locked loops that ensure the angle estimation error goes to zero for almost all initial conditions. Although well-known, for the sake of completeness, we also present a PI controller with feedforward action that ensures the converter currents converge to an arbitrary desired value. Relevance of the theoretical results and their robustness to variation of the grid parameters are thoroughly discussed and validated in the challenging scenario of a converter connected to a grid with low short-circuit-ratio.

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