System identification of PWM dc-dc converters during abrupt load changes

Al‐Greer, Maher; Armstrong, Matthew; Giaouris, Damian

doi:10.1109/iecon.2009.5414842

Cited by 13 publications

(22 citation statements)

References 8 publications

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“…In system identification and adaptive controller, it is important to estimate the system model and update the control parameters quickly and accurately [7]. Among other existing methods, the literature shown that RLS algorithms offer a simple, accurate, fast convergence time method, as well as it is directly applied in on-line and real time adaptive controller [3].…”

Section: Recursive Least Square Algorithm (Rls)mentioning

confidence: 99%

“…The first step in parametric system identification is to define the discrete model of the plant [7]. Many of the systems can mathematically be described, while others are derived experimentally based on the observed input-output data.…”

Section: Discrete Time Modelling Of the Dc-servo Motor And Erls Algoritmentioning

confidence: 99%

“…In order to successfully tune the PID gains, an accurate model of the controlled system is required [6]. This can be achieved via system identification algorithms; two paradigms are considered: parametric method and nonparametric method [7]. As presented in the literature, a nonadaptive PID controller is less successful for controlling the DC motor, where some of system parameters are uncertain or unknown such as external load torque, disturbance etc.…”

Section: Introductionmentioning

confidence: 99%

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Real Time Self-Tuning Controller for Position Control of DC Motor System using Pole-Placement Technique

Mohammad¹,

Basim²,

Zaki³

et al. 2016

IJCA

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This paper presents a real time self-tuning controller for DC motor system. Pole-placement adaptive controller based on Exponentially Recursive Least Square (ERLS) algorithm is proposed. The parameters of the DC motor are estimated using ERLS algorithm. Once the estimation error is minimized, the identified parameters are forward to the supervisory control unit to find the corresponding PID gains. A PCI-6251 data acquisition card from National Instrument (NI) and the prototyped control system (33-100 & 33-110) from feedback device are deployed for real time implementation of the proposed solution. Simulation and experimental results verified the effectiveness of the ERLS algorithm, where the parameters of DC motor are estimated rapidly and accurately. Results, also shows the validation of the proposed self-tuning controller for position control of the DC motor system. General TermsControl Algorithm.

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Section: Recursive Least Square Algorithm (Rls)mentioning

confidence: 99%

Section: Discrete Time Modelling Of the Dc-servo Motor And Erls Algoritmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Real Time Self-Tuning Controller for Position Control of DC Motor System using Pole-Placement Technique

Mohammad¹,

Basim²,

Zaki³

et al. 2016

IJCA

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“…In addition, the performance of the proposed algorithm is not investigated during abrupt load changes. Regardless of the improvement introduced by ERLS in terms of estimating abrupt changes, it is reported that a compromise must be made between noise sensitivity and dynamic tracking performance [15]. Typically, this technique applies equal weight to all parameters during the estimation process.…”

mentioning

confidence: 99%

“…The second paradigm, parametric system identification assumes a known model structure with prespecifie order and number of coefficient to be estimated [5]. According to the literature, conventional least squares [5], [13] and its recursive version, recursive least squares (RLS) [4], [14], [15], are the most commonly used algorithms for parameter estimation of dc-dc converters. In [4], the classical RLS algorithm is reviewed and tested in real time on an open-loop buck converter.…”

mentioning

confidence: 99%

Real-Time Parameter Estimation of DC–DC Converters Using a Self-Tuned Kalman Filter

Ahmeid

Armstrong

Gadoue

et al. 2017

IEEE Trans. Power Electron.

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Abstract-To achieve high-performance control of modern dcdc converters, using direct digital design techniques, an accurate discrete model of the converter is necessary. In this paper, a new parametric system identification method, based on a Kalman filter (KF) approach is introduced to estimate the discrete model of a synchronous dc-dc buck converter. To improve the tracking performance of the proposed KF, an adaptive tuning technique is proposed. Unlike many other published schemes, this approach offers the unique advantage of updating the parameter vector coefficients at different rates. The proposed KF estimation technique is experimentally verified using a Texas Instruments TMS320F28335 microcontroller platform and synchronous step-down dc-dc converter. Results demonstrate a robust and reliable real-time estimator. The proposed method can accurately identify the discrete coefficients of the dc-dc converter. This paper also validates the performance of the identification algorithm with time-varying parameters, such as an abrupt load change. The proposed method demonstrates robust estimation with and without an excitation signal, which makes it very well suited for real-time power electronic control applications. Furthermore, the estimator convergence time is significantly shorter compared to many other schemes, such as the classical exponentially weighted recursive least-squares method.

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