Design of a Discrete Tracking Controller for a Magnetic Levitation System: A Nonlinear Rational Model Approach

Gómez-Salas, Fernando; Wang, Yongji; Zhu, Quanmin

doi:10.1155/2015/360783

Cited by 4 publications

(3 citation statements)

References 19 publications

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“…This system is the subject of several research studies, with a view to their modelling and control by different techniques, such as conventional techniques such as control by PD/PID regulator (Vischer & Bleuler, 1993) (Bleuler et al, 1994) (Arif et al, 2019) (Duka et al, 2016) (Salman et al, 2016), Artificial intelligence techniques (fuzzy logic, neural networks) (Moinuddin et al, 2000) (Mokhtari et al, 1998) (Qin et al, 2014) (Sun et al, 2019), linearization techniques (El Hajjaji et al, 2001) (Maggiore et al, 2004) (Šuster et al, 2012) (Balko et al, 2017) (Khan et al, 2019) for linear control of the system, control techniques based on stability studies and performance criteria, as the Backstepping control (Wai et al, 2008), LQR control (Maggiore et al, 2004) (Acero et al, 2016) (Yaseen et al, 2018), observers (Acero et al, 2016) (Sharma et al, 2017), predictive control (Qin et al, 2014) (Zhang et al, 2020), numerical resolution techniques (AR, ARX) (Qin et al, 2014) (Gómez-Salas et al, 2015.…”

Section: Figure 1 -Synoptic Diagram Of An Electromagnetic Levitation ...mentioning

confidence: 99%

Bond Graph-Based Modelling and Control of Electromagnetic Levitation System Using Firefly Optimization

Dif

Benmahammed

2023

J. Eng. Exact Sci.

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This paper focuses on the modelling and control of an electromagnetic suspension using the bond graph tool. This technique combines graphical and relational aspects to establish connections between bond graph elements, particularly in deducing junction relationships and employing bicausal inversion for trajectory tracking controller design. To determine the controller parameters, we utilized a meta-heuristic method called Firefly for optimization, thus avoiding the need for trial and error. The simulation was conducted using 20-Sim software and a Matlab/Simulink script, which yielded improved results. This paper presents an initial contribution that demonstrates the integration of meta-heuristic optimization and the bond graph tool for parameter selection in 20-sim simulation software.

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Section: Figure 1 -Synoptic Diagram Of An Electromagnetic Levitation ...mentioning

confidence: 99%

Bond Graph-Based Modelling and Control of Electromagnetic Levitation System Using Firefly Optimization

Dif

Benmahammed

2023

J. Eng. Exact Sci.

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“…Magnetic levitation system is a class of typical nonlinear system, which is difficult to establish accurate mathematical model for the natural parameter of electromagnetic part dependent times [1,2]. Normally, a standard magnetic levitation system consists of four parts: the sensors, the controller, the power amplifier, and the electromagnetic drives.…”

Section: Introductionmentioning

confidence: 99%

Energy‐Based Controller Design of Stochastic Magnetic Levitation System

Sun

Wang

Nie

et al. 2017

Mathematical Problems in Engineering

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This paper investigates the control problem of magnetic levitation system, in which velocity feedback signal is influenced by stochastic disturbance. Firstly, single-degree-freedom magnetic levitation is regarded as an energy-transform action device. From the view of energy-balance relation, the magnetic levitation system is transformed into port-controlled Hamiltonian system model. Next, based on the Hamiltonian structure, the control law of magnetic levitation system is designed by applying Lyapunov theory. Finally, the simulation verifies the correctness of the proposed results.

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“…5) Rational models have been gradually adopted in various applications of non-linear system modelling and control (Ford, Titterington, and Kitsos 1989, Ponton 1993, Correay and Aguirre 2000, Knežević-stevanović et al 2014, Gómez-Salas, Wang, and Zhu 2015, particularly the importance of modelling of chemical kinetics has increased sharply as a consequence of the applicability of modelling of catalytic reactions (Dimitrov andKamenski 1991, Kamenski andDimitrov1993). Rational models are not only alternative expressions in approximating a wide range of data set in chemical engineering, but also are class mechanistic models, which most previous experience or theoretical considerations had not put forwarded (Dimitrov and Kamenski 1991).…”

Section: Introductionmentioning

confidence: 99%

An enhanced linear Kalman filter (EnLKF) algorithm for parameter estimation of nonlinear rational models

Zhu

Zhao

2016

International Journal of Systems Science

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Abstract:In this study an enhanced Kalman Filter formulation for linear in the parameters models with inherent correlated errors is proposed to build up a new framework for nonlinear rational model parameter estimation. The mechanism of Linear Kalman Filter (LKF) with point data processing is adopted to develop a new recursive algorithm. The novelty of the Enhanced Linear Kalman Filter (EnLKF in short and distinguished from Extended Kalman Filter (EKF)) is that it is not formulated from the routes of extended Kalman Filters (to approximate nonlinear models by linear approximation around operating points through Taylor expansion) and also it includes LKF as its subset while linear models have no correlated errors in regressor terms. No matter linear or nonlinear models in representing a system from measured data, it is very common to have correlated errors between measurement noise and regression terms, the EnLKF provides a general solution for unbiased model parameter estimation without extra cost to convert model structure. The associated convergence is analysed to provide a quantitative indicator for applications and reference for further research. Three simulated examples are selected to bench-test the performance of the algorithm. In addition, the style of conducting numerical simulation studies provides a user-friendly step by step procedure for the readers/users with interest in their ad hoc applications. It should be noted that this approach is fundamentally different from those using linearization to approximate nonlinear models and then conduct state/parameter estimate.

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Design of a Discrete Tracking Controller for a Magnetic Levitation System: A Nonlinear Rational Model Approach

Cited by 4 publications

References 19 publications

Bond Graph-Based Modelling and Control of Electromagnetic Levitation System Using Firefly Optimization

Bond Graph-Based Modelling and Control of Electromagnetic Levitation System Using Firefly Optimization

Energy‐Based Controller Design of Stochastic Magnetic Levitation System

An enhanced linear Kalman filter (EnLKF) algorithm for parameter estimation of nonlinear rational models

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