Sliding-Mode Control Design of a Boost–Buck Switching Converter for AC Signal Generation

Biel, Domingo; Guinjoan, F.; Fossas, Enric; Chavarria, Javier

doi:10.1109/tcsi.2004.832803

Cited by 60 publications

(39 citation statements)

References 21 publications

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“…This is done by analyzing the derivative and equating it to zero, resulting in (13) If this equation has a positive solution of , it is the optimal capacity. However, if a positive solution does not exist, the maximum lifetime is obtained by choosing .…”

Section: F Optimal Capacity For Increased Operation-time Of Autonomomentioning

confidence: 99%

“…Smaller-scale reservoirs in the form of bulk capacitors are commonly employed in power electronics systems to balance the instantaneous difference between ac and dc powers such as in rectifiers [11], [12] and inverters [11], [13]. It should be noted that optimal power management and energy storage is a critical issue even in very-low-power systems such as in single solar cell powered wireless sensor, in the mWatts range [10], [13], and in micropower energy harvesting, in the range [14]. In contrast to sustainable energy based power sources, fueled power sources, have a finite primary energy reserve.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Power Management in Fueled Systems With Finite Storage Capacity

Levron

Shmilovitz

2010

IEEE Trans. Circuits Syst. I

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Abstract-Fueled power systems using secondary energy storage are analyzed. A generic model of such systems is suggested, and an optimal power management strategy that maximizes efficiency is derived analytically. The model and optimal management solution emphasizes the constraint imposed by finite storage capacity. The optimal generated energy is established independently of the system's capacity, and load, and general characteristics of it are derived and proved. The analytic solution provides an intuitive comprehension into the optimal power management, without needing numeric simulations.Index Terms-Battery lifetime, energy storage, load balancing, load leveling, optimal efficiency, power management.

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Section: F Optimal Capacity For Increased Operation-time Of Autonomomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal Power Management in Fueled Systems With Finite Storage Capacity

Levron

Shmilovitz

2010

IEEE Trans. Circuits Syst. I

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“…According to the SMC principle [29], the control goal here is to maintain the trajectory of the state variable, which is the SOFC's output power, on the sliding surface Sfc(k)=0 for the whole time. With reference to the LMS algorithm [13], the weight vector (Wfc) of the SOFC's controller is updated at each iteration of the online training process as follows:…”

Section: Control Of the Sofc Stackmentioning

confidence: 99%

Adaptive Neural Network-Based Control of a Hybrid AC/DC Microgrid

Chettibi

Mellit

Sulligoi

et al. 2016

IEEE Trans. Smart Grid

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-In this paper, the behavior of a grid-connected hybrid AC/DC Microgrid has been investigated. Different Renewable Energy Sources -photovoltaics modules and a wind turbine generator -have been considered together with a Solid Oxide Fuel Cell and a Battery Energy Storage System. The main contribute of this work is the design and the validation of an innovative online-trained artificial neural network based control system for a hybrid microgrid. Adaptive Neural Networks are used to track the Maximum Power Point of renewable energy generators and to control the power exchanged between the Front-End Converter and the electrical grid. Moreover, a fuzzy logic based Power Management System is proposed in order to minimize the energy purchased from the electrical grid. The operation of the hybrid microgrid has been tested in the Matlab/Simulink environment under different operating conditions. The obtained results demonstrate the effectiveness, the high robustness and the self-adaptation ability of the proposed control system. OWADAYS, the wide diffusion of distributed RES presents a new scenario for the regulation of distribution networks and the availability of new technologies for storage systems encourages their use in power systems [1]. In general, a hybrid AC/DC MG integrates different Distributed Generators (e.g. solar power sources, wind power generators, cogenerators, etc.), a energy storage system and a number of AC and DC loads. A FEC can interface the MG with the electric grid and can operate either in a grid-connected or islanded mode. The use of a PMS is crucial to optimize the power flow through the different components of the MG and the exchange of energy with the electric grid. Moreover, since the power produced by RESs depends on the climatic conditions, MPPT algorithms are needed in order to harvest the maximum available energy. The intermittent nature of RESs with the time-varying loads demand make the use of advanced control structures fundamental in order to make the operation of the MG reliable, economic, and secure under different operating conditions. The MG must also guarantee a high quality power supply to both local loads and electrical grid. Index TermsMany works have focused on hybrid microgrids and have proposed a number of control schemes for different mode of operations [2]- [8]. A multiagent-based energy management system to optimizes the economic operation of a MG is presented in [2]. A reactive power sharing algorithm in hierarchical droop control is developed in [3]. A novel coordinated voltage control scheme with islanding capability for a MG is proposed in [4]. adapt to uncertainties and they can be used also when the model of the system to be controlled is not available. Recently, the NNs with the learning capability are widely applied for the control of complex power systems. In [9], a back-propagation NN is applied for the real-time estimation of the wind speed. A novel discrete-time NN controller for the control of DC distribution system is designed in [10]. In [11], a...

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“…In many applications, where voltage and current regulation are needed from unregulated sources such as battery charging and discharging, power factor correction, fuel cell regulation, maximum power tracking of solar panels, among others, step-up/ step-down converters are required [1] [13]. These converters do not operate in buck-boost mode, because it is more efficient to work them in buck or boost mode, depending on their input and output voltages [2].…”

Section: Introductionmentioning

confidence: 99%

LMI Control Design of a Non-Inverting Buck-Boost Converter: a Current Regulation Approach

Murillo¹,

Huertas²,

Pinzón³

et al. 2017

TECCIENCIA

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This paper presents an analytical study of an input current-mode control based on a linear matrix inequalities (LMI) for a noninverting buck-boost converter. The LMI control technique makes better the dynamic response of this converter in comparison with previous research works, where its currents has been regulated using a classical analogue PI controller with an additional pole. The main features of the selected converter are its voltage step-up and step-down properties, high efficiency, wide bandwidth and low input and output current ripples. All of these converter's properties allows it to be used as a modular converter capable of being positioned at any converter locations in hybrid systems, which are formed by varying-voltagesources, current controlled dc-dc converters and auxiliary storage devices such as batteries or capacitors. The designed statefeedback controller has the following aims, among others: pole placement constraints, control effort limitation, and decay rate and bandwidth improvement. The use of state-space averaging (SSA) method allows to describe LMI constraints which guarantees stability and provide good performances under a close loop pole region and control signal bound. The theoretical analysis have been simulated by means of Matlab and PSIM on an 800-W coupled-inductor buck-boost dc-dc switching converter.Keywords: Coupled Inductors, Current Control, DC-DC Power Converters, Linear Matrix Inequality (LMI), Non-Inverting Buckboost Converter. ResumenEste artículo muestra un estudio analítico de control de entrada de modo-corriente basado en Desigualdades Lineales Matriciales (DLM) para un convertidor no-inversor buck-boost. La técnica de control DLM mejora la respuesta dinámica de este convertidor en comparación con trabajos de investigación anteriores, donde las corrientes han sido reguladas usando un controlador clásico análogo PI con un polo adicional. Las principales características del convertidor seleccionado son sus propiedades de subir y bajar por paso, alta eficiencia, amplio ancho de banda y bajas oscilaciones de corrientes de entrada y salida. Todas estas propiedades permiten que el convertidor sea usado de manera modular, siendo capaz de ser posicionado en cualquier posición de convertidor en sistemas híbridos, los cuales son conformados por fuentes de voltaje variable, convertidores dc-dc controlados por corriente, dispositivos de almacenamiento auxiliares como baterías y capacitores. El controlador de estado de retroalimentación diseñado tiene los siguientes objetivos, entre otros: restricciones de la ubicación del polo, limitación en el esfuerzo de control, tasa de decaimiento y mejora en ancho de banda. El uso del método de promedio en espacio de estado (PEE) permite describir restricciones DLM que garantizan la estabilidad y proveen buen rendimiento en una región de polo de ciclo cerrado y sujeto a la señal de control. El análisis teórico fue simulado usando Matlab y PSIM en un convertidor conmutable dc-dc buck-boost de inductor acoplado de 800-W.

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Sliding-Mode Control Design of a Boost–Buck Switching Converter for AC Signal Generation

Cited by 60 publications

References 21 publications

Optimal Power Management in Fueled Systems With Finite Storage Capacity

Optimal Power Management in Fueled Systems With Finite Storage Capacity

Adaptive Neural Network-Based Control of a Hybrid AC/DC Microgrid

LMI Control Design of a Non-Inverting Buck-Boost Converter: a Current Regulation Approach

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