Identification and robust control of a quadratic DC/DC boost converter by Hammerstein model

Alonge, F.; Rabbeni, Roberto; Pucci, Marcello; Vitale, G.

doi:10.1109/ecce.2014.6953856

Cited by 4 publications

(5 citation statements)

References 21 publications

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“…The suggested process effectively defines the DC-DC converter model; from this, a robust controller is developed [10,38]. The authors in [44] utilise the same paradigm demonstrated in [10,38] to identify a complex 4 th order DC-DC converter transfer function. In [11], the dynamic characteristic of the DC-DC converter is excited by a step load change and the output response is captured.…”

Section: A Black Box Modellingmentioning

confidence: 99%

Advances on System Identification Techniques for DC–DC Switch Mode Power Converter Applications

Al‐Greer

Armstrong

Ahmeid

et al. 2019

IEEE Trans. Power Electron.

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System identification is fundamental in many recent state-of-the-art developments in power electronic such as modelling, parameter tracking, estimation, self-tuning and adaptive control, health monitoring, and fault detection. Therefore, this paper presents a comprehensive review of parametric, non-parametric, and dual hybrid system identification for DC-DC Switch Mode Power Converter (SMPC) applications. The paper outlines the key challenges inherent with system identification for power electronic applications; speed of estimation, computational complexity, estimation accuracy, tracking capability, and robustness to disturbances and time varying systems. Based on literature in the field, modern solutions to these challenges are discussed in detail. Furthermore, this paper reviews and discusses the various applications of system identification for SMPCs; including health monitoring and fault detection.

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Section: A Black Box Modellingmentioning

confidence: 99%

Advances on System Identification Techniques for DC–DC Switch Mode Power Converter Applications

Al‐Greer

Armstrong

Ahmeid

et al. 2019

IEEE Trans. Power Electron.

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show abstract

“…If the nonlinear static block is assumed to be approached by a finite polynomial expansion, the autoregressive Hammerstein model for LFC of multi-area interconnected power system with HESS can be expressed in the following form [36]:…”

Section: A Hammerstein Model Formulationmentioning

confidence: 99%

“…Therefore, system identification has become one of the core contents of the control system, which has received more and more attention in the power generation system [31]. For identification of nonlinear systems, Hammerstein and Wiener models have proposed the popular method for system modeling [32][33][34][35][36][37]. The Hammerstein model consists of a nonlinear static block in cascade with a linear dynamic block, while the Wiener model is composed of the same blocks but in the reverse order [32].…”

mentioning

confidence: 99%

A Novel Adaptive Neural Network Constrained Control for a Multi-Area Interconnected Power System With Hybrid Energy Storage

Liu

Yan

et al. 2018

IEEE Trans. Ind. Electron.

166

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Abstract-This paper concentrates on the problem of control of a hybrid energy storage system (HESS) for an improved and optimized operation of load-frequency control (LFC) applications. The HESS consists of a supercapacitor served as the main power source, and a fuel cell served as the auxiliary power source. Firstly, a Hammerstein-type neural network (HNN) is proposed to identify the HESS system, which formulates the Hammerstein model with a nonlinear static gain in cascade with a linear dynamic block. It provides the model information for the controller to achieve the adaptive performance. Secondly, a feedforward neural network based on back-propagation training algorithm is designed to formulate the PID-type neural network (PIDNN), which is used for the adaptive control of HESS system. Meanwhile, a dynamic anti-windup signal is designed to solve the operational constraint of the HESS system. Then, an appropriate power reference signal for HESS can be generated. Thirdly, the stability and the convergence of the whole system are proved based on the Lyapunov stability theory. Finally, simulation experiments are followed through on a four-area interconnected power system to demonstrate the effectiveness of the proposed control scheme.Index Terms-Load-frequency control (LFC), hybrid energy storage system (HESS), Hammerstein network identification, PIDtype neural network (PIDNN), dynamic anti-windup, adaptive control

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“…By using the simple autoregressive model with exogenous input (ARX) model, the discrete transfer function (10) can be described as a linear difference equation in order to formulate the identification problem. This model takes into account measurement noise and modelling approximations via adding the error term ( ) as follows [18]:…”

Section: B M-max Kalman Filter Algorithmmentioning

confidence: 99%

“…The relations tips (23-28) and (18) are then used to calculate the controller output (the duty cycle) by: …”

Section: Input Controllermentioning

confidence: 99%

Computationally Efficient Self-Tuning Controller for DC–DC Switch Mode Power Converters Based on Partial Update Kalman Filter

Ahmeid

Armstrong

Al‐Greer

et al. 2018

IEEE Trans. Power Electron.

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Identification and robust control of a quadratic DC/DC boost converter by Hammerstein model

Cited by 4 publications

References 21 publications

Advances on System Identification Techniques for DC–DC Switch Mode Power Converter Applications

Advances on System Identification Techniques for DC–DC Switch Mode Power Converter Applications

A Novel Adaptive Neural Network Constrained Control for a Multi-Area Interconnected Power System With Hybrid Energy Storage

Computationally Efficient Self-Tuning Controller for DC–DC Switch Mode Power Converters Based on Partial Update Kalman Filter

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