Neural network adaptive state feedback control of a magnetic levitation system

Zhao, Shi-tie; Gao, Xianwen

doi:10.1109/ccdc.2014.6852423

Cited by 10 publications

(7 citation statements)

References 10 publications

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“…The measured value was 0.27 A, and thus (i eq , z eq ) = (0.27 A, 2 cm). Substituting the system parameters in (13), the resulting transfer function is:…”

Section: Sensors Parameterizationmentioning

confidence: 99%

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Experiments with Neural Networks in the Identification and Control of a Magnetic Levitation System Using a Low-Cost Platform

Silva

Barbosa

2021

Applied Sciences

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In this article, we designed and implemented neural controllers to control a nonlinear and unstable magnetic levitation system composed of an electromagnet and a magnetic disk. The objective was to evaluate the implementation and performance of neural control algorithms in a low-cost hardware. In a first phase, we designed two classical controllers with the objective to provide the training data for the neural controllers. After, we identified several neural models of the levitation system using Nonlinear AutoRegressive eXogenous (NARX)-type neural networks that were used to emulate the forward dynamics of the system. Finally, we designed and implemented three neural control structures: the inverse controller, the internal model controller, and the model reference controller for the control of the levitation system. The neural controllers were tested on a low-cost Arduino control platform through MATLAB/Simulink. The experimental results proved the good performance of the neural controllers.

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“…The measured value was 0.27 A, and thus (i eq , z eq ) = (0.27 A, 2 cm). Substituting the system parameters in (13), the resulting transfer function is:…”

Section: Sensors Parameterizationmentioning

confidence: 99%

“…For that, there are several architectures that use neural networks that can be employed [11,12]. This type of control can be applied to several types of systems; however, its performance stands out compared to other techniques in the control of nonlinear and unstable systems [13,14].…”

Section: Introductionmentioning

confidence: 99%

Experiments with Neural Networks in the Identification and Control of a Magnetic Levitation System Using a Low-Cost Platform

Silva

Barbosa

2021

Applied Sciences

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“…The paper with title "neural network adaptive state feedback control of a magnetic levitation system ", proposes combine neural network adaptive control and state feedback control based on RBFNN. In simulation show that adaptive state feedback controller based on RBF has better stability than conventional PID [7]. However, it is know that neural networks need learning that can affect the controller system to become slower.…”

Section: Introductionmentioning

confidence: 96%

“…However, this paper did not consider disturbance in magnetic levitation. In addition, there are also other methods such as fuzzy logic controller [4], feedback linearization [5], LQR [6], neural network [7][8] [9]. Fuzzy logic controller for magnetic levitation system shows better performance than PID controller when adding mass as disturbance [4].…”

Section: Introductionmentioning

confidence: 99%

Sliding Mode Control with Gain Scheduled for Magnetic Levitation System

Uswarman

Istiqphara

Nugroho

2019

Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika

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This paper presents a study of controlling magnetic levitation object using conventional sliding mode control (CSMC) and sliding mode control (SMC) with gain-scheduled. SMC with gain-scheduled aims to improve the robustness of the control system from disturbance. The CSMC simulation results show that output can follow the set point if there is no interference, but if the disturbance happens then there is overshoot and undershoot of 0.034 mm and 0.07 mm for disturbance 10 * sin( ) and 20 * sin( ). Then SMC with gain-scheduled shows excellent performance because the output can follow the reference even if it got disturbance of 10 * sin( ) and 20 * sin( ).

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“…Thus, to improve control schemes, a stricter adherence to the complex nonlinear nature of the Maglev Systems is needed. In the last several years, neural network control systems have received significant attention due to their ability to capture complex nonlinear dynamics and model nonlinear unknown parameters [46].…”

Section: Brief Introductionmentioning

confidence: 99%

Reachable Set Estimation and Verification for Neural Network Models of Nonlinear Dynamic Systems

Xiang

Lopez

Musau

et al. 2018

Safe, Autonomous and Intelligent Vehicles

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Neural networks have been widely used to solve complex realworld problems. Due to the complicate, nonlinear, non-convex nature of neural networks, formal safety guarantees for the behaviors of neural network systems will be crucial for their applications in safety-critical systems. In this paper, the reachable set estimation and verification problems for Nonlinear Autoregressive-Moving Average (NARMA) models in the forms of neural networks are addressed. The neural network involved in the model is a class of feed-forward neural networks called Multi-Layer Perceptron (MLP). By partitioning the input set of an MLP into a finite number of cells, a layer-by-layer computation algorithm is developed for reachable set estimation for each individual cell. The union of estimated reachable sets of all cells forms an over-approximation of reachable set of the MLP. Furthermore, an iterative reachable set estimation algorithm based on reachable set estimation for MLPs is developed for NARMA models. The safety verification can be performed by checking the existence of intersections of unsafe regions and estimated reachable set.Several numerical examples are provided to illustrate our approach.

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Neural network adaptive state feedback control of a magnetic levitation system

Cited by 10 publications

References 10 publications

Experiments with Neural Networks in the Identification and Control of a Magnetic Levitation System Using a Low-Cost Platform

Experiments with Neural Networks in the Identification and Control of a Magnetic Levitation System Using a Low-Cost Platform

Sliding Mode Control with Gain Scheduled for Magnetic Levitation System

Reachable Set Estimation and Verification for Neural Network Models of Nonlinear Dynamic Systems

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