A Neural Network-Based Closed Loop Identification of a Magnetic Bearings System

2004 ◽  
Author(s):  
Jose´ Medina ◽  
Mo´nica Parada ◽  
Victor Guzma´n ◽  
Luis Medina ◽  
Sergio Di´az
Keyword(s):  
Neural Network ◽  
Closed Loop ◽  
Magnetic Bearing ◽  
External Input ◽  
Open Loop ◽  
Active Magnetic Bearing ◽  
The Neural Network ◽  
Closed Loop Identification ◽  
Artificial Neural Network Ann ◽  
Amb System

This paper deals with the identification of a radial-type active magnetic bearing (AMB) system using Artificial Neural Network (ANN). Identification and validation experiments are performed on a laboratory magnetic bearing system. Since the electromechanical configuration is inherently unstable, the identification data is gathered while the AMB is operating in closed loop with a controller in the loop. From this data, the identification procedure generates an open-loop plant model. A NNARX (Neural network autoregressive external input model) structure is proposed and evaluated for emulating the system’s dynamic. The model is implemented by a Neural network, constructed using a multilayer perceptron (MLP) topology, and trained using as inputs the rotor’s displacements and excitation currents. Validation tests are performed under perturbation conditions (impact applied on the rotor). Results show that the neural network based model presented here is a powerful tool for dynamic plant’s identification, and that it could be also suitable for robust control application.

scholarly journals Nonlinear Control of an Active Magnetic Bearing System Achieved Using a Fuzzy Control with Radial Basis Function Neural Network

2014 ◽  
Vol 2014 ◽  
pp. 1-18 ◽  
Author(s):  
Seng-Chi Chen ◽  
Van-Sum Nguyen ◽  
Dinh-Kha Le ◽  
Nguyen Thi Hoai Nam
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Fuzzy Logic Controller ◽  
Magnetic Bearing ◽  
Operating Conditions ◽  
Active Magnetic Bearing ◽  
Highly Nonlinear ◽  
Radial Basis ◽  
Amb System

Studies on active magnetic bearing (AMB) systems are increasing in popularity and practical applications. Magnetic bearings cause less noise, friction, and vibration than the conventional mechanical bearings; however, the control of AMB systems requires further investigation. The magnetic force has a highly nonlinear relation to the control current and the air gap. This paper proposes an intelligent control method for positioning an AMB system that uses a neural fuzzy controller (NFC). The mathematical model of an AMB system comprises identification followed by collection of information from this system. A fuzzy logic controller (FLC), the parameters of which are adjusted using a radial basis function neural network (RBFNN), is applied to the unbalanced vibration in an AMB system. The AMB system exhibited a satisfactory control performance, with low overshoot, and produced improved transient and steady-state responses under various operating conditions. The NFC has been verified on a prototype AMB system. The proposed controller can be feasibly applied to AMB systems exposed to various external disturbances; demonstrating the effectiveness of the NFC with self-learning and self-improving capacities is proven.

scholarly journals Investigation of a 2-DOF Active Magnetic Bearing Actuator for Unmanned Underwater Vehicle Thruster Application

Actuators ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 79
Author(s):  
Muhammad Abdul Ahad ◽  
Sarvat M. Ahmad
Keyword(s):  
Underwater Vehicle ◽  
Magnetic Bearing ◽  
Open Loop ◽  
Active Magnetic Bearing ◽  
Linear Quadratic ◽  
Reference Tracking ◽  
Unmanned Underwater Vehicle ◽  
Performance Specifications ◽  
Amb System ◽  
Intractable Problem

In this work, a novel application of Active Magnetic Bearing (AMB) is proposed to integrate AMB in the Magnetically Coupled Thruster (MCT) assembly for underwater application. In this study, a 2-Degree-Of-Freedom (DOF) AMB is developed and investigated for the MCT of an Unmanned Underwater Vehicle (UUV). The paper presents the detailed electro-mechanical modeling of the in-house developed AMB system. The intractable problem of rotor suspension and rotation with opposing pairs of electromagnets is considered. A Linear Quadratic Gaussian (LQG) controller is designed and analyzed in the frequency domain for the stabilization of the open-loop unstable AMB for MCT. The performance specifications of the controller, such as reference tracking and disturbance rejection are achieved and evaluated through real-time implementation of the controller. The compensator also performed reasonably well during the dynamic operations, i.e., when the rotor-propeller assembly was spun at 1500 rpm. This rotor speed is needed to generate a thrust of 40–45 N and up to 1 m/s forward velocity, which is necessary to propel the UUV under consideration. By deploying AMB in MCT assembly, it is anticipated that problems associated with the conventional directly coupled thruster operating in harsh underwater environment, such as water ingress into electronics compartment, rusting, lubrication, and vibrations would be eliminated.

Cancelling Gyroscopic Effects in AMB Systems With Flexible Rotors via Modal Feedback

2019 ◽  
Author(s):  
Alican Sahinkaya ◽  
Jerzy T. Sawicki
Keyword(s):  
Feedback Control ◽  
Controller Design ◽  
Sensory Information ◽  
Magnetic Bearing ◽  
Open Loop ◽  
State Feedback Control ◽  
Active Magnetic Bearing ◽  
Flexible Rotors ◽  
Amb System ◽  
Gyroscopic Effects

Abstract For active magnetic bearing (AMB) systems with rotors having significant polar to transverse moments of inertia ratio, the influence of gyroscopic effects needs to be considered in controller design procedures to prevent excessive vibrations and potential instability during operation. This consideration leads to conservative controllers due to large uncertainties caused by the rotational speed range of the rotor, or gain-scheduled controllers that require larger computational power, both of which are not desirable. A cross-feedback control has been applied in the literature to compensate for the gyroscopic effects of AMB systems with rigid rotors. However, the method is not applicable to AMB systems with flexible rotors due to lack of full-state sensory information and under actuation. This paper proposes a novel modal state feedback control as an addon controller for AMB systems with flexible rotors to compensate for the gyroscopic effects of selected modes. The aim of the add-on controller is to alter the open loop AMB system such that the open loop dynamics presents reduced gyroscopic effects of the selected modes from a controller point of view, reducing the uncertainties in the model for robust controller design. The proposed approach is demonstrated on an AMB rotor test rig with a rotor configuration featuring noticeable gyroscopic effects. The comparison of the frequency response data of the open loop AMB system with and without the proposed add-on controller shows the feasibility of the approach.

scholarly journals APPLICATION OF ARTIFICIAL NEURAL NETWORKS IN THE CONTEXT OF ACTIVE MAGNETIC BEARING CONTROL SYSTEMS

2021 ◽  
Vol 3 ◽  
pp. 159-162
Author(s):  
Alexander Kravtsov ◽  
Konstantin Vukolov ◽  
Igor Plokhov ◽  
Igor Savraev ◽  
Sergei Loginov
Keyword(s):  
Neural Network ◽  
Magnetic Bearing ◽  
Magnetic Suspension ◽  
Active Magnetic Bearing ◽  
Rolling Moment ◽  
Neural Network Controller ◽  
The Neural Network ◽  
Network Controller ◽  
Network Methods ◽  
Single Mass

The article is devoted to the application of neural network methods and genetic algorithms in solving problems of controlling an electric drive of an active magnetic suspension. The method of rolling moment for eliminating an imbalance is considered. The scheme of the neural network controller and the curves of the transients in the open single-mass electromechanical system and in the system c of the neurocontrollers are presented.

scholarly journals Control of Magnetic Bearings for Rotor Unbalance With Plug-In Time-Varying Resonators

2015 ◽  
Vol 138 (1) ◽  
Author(s):  
Christopher Kang ◽  
Tsu-Chin Tsao
Keyword(s):  
Disturbance Rejection ◽  
Internal Model ◽  
Closed Loop ◽  
Electromagnetic Levitation ◽  
Magnetic Bearing ◽  
Open Loop ◽  
Active Magnetic Bearing ◽  
Time Varying ◽  
Periodic Disturbance ◽  
Rotational Systems

Rotor unbalance, common phenomenon of rotational systems, manifests itself as a periodic disturbance synchronized with the rotor's angular velocity. In active magnetic bearing (AMB) systems, feedback control is required to stabilize the open-loop unstable electromagnetic levitation. Further, feedback action can be added to suppress the repeatable runout but maintain closed-loop stability. In this paper, a plug-in time-varying resonator is designed by inverting cascaded notch filters. This formulation allows flexibility in designing the internal model for appropriate disturbance rejection. The plug-in structure ensures that stability can be maintained for varying rotor speeds. Experimental results of an AMB–rotor system are presented.

Some Nonlinear Effects of Magnetic Bearings

1999 ◽  
Author(s):  
Norbert Steinschaden ◽  
Helmut Springer
Keyword(s):  
Equations Of Motion ◽  
Period Doubling ◽  
Path Following ◽  
Nonlinear Effects ◽  
Magnetic Bearing ◽  
Operating Conditions ◽  
Active Magnetic Bearing ◽  
Nonlinear Phenomena ◽  
Amb System ◽  
Large Rotor

Abstract In order to get a better understanding of the dynamics of active magnetic bearing (AMB) systems under extreme operating conditions a simple, nonlinear model for a radial AMB system is investigated. Instead of the common way of linearizing the magnetic forces at the center position of the rotor with respect to rotor displacement and coil current, the fully nonlinear force to displacement and the force to current characteristics are used. The AMB system is excited by unbalance forces of the rotor. Especially for the case of large rotor eccentricities, causing large rotor displacements, the behaviour of the system is discussed. A path-following analysis of the equations of motion shows that for some combinations of parameters well-known nonlinear phenomena may occur, as, for example, symmetry breaking, period doubling and even regions of global instability can be observed.

A Model on the Correlation between Composition and Mechanical Properties of Mg-Al-Zn Alloys by Using Artificial Neural Network

2005 ◽  
Vol 488-489 ◽  
pp. 793-796 ◽  
Author(s):  
Hai Ding Liu ◽  
Ai Tao Tang ◽  
Fu Sheng Pan ◽  
Ru Lin Zuo ◽  
Ling Yun Wang
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Artificial Neural Network ◽  
Magnesium Alloys ◽  
Tensile Yield Strength ◽  
Data Set ◽  
The Neural Network ◽  
Artificial Neural ◽  
Prediction Of Mechanical Properties ◽  
Artificial Neural Network Ann

A model was developed for the analysis and prediction of correlation between composition and mechanical properties of Mg-Al-Zn (AZ) magnesium alloys by applying artificial neural network (ANN). The input parameters of the neural network (NN) are alloy composition. The outputs of the NN model are important mechanical properties, including ultimate tensile strength, tensile yield strength and elongation. The model is based on multilayer feedforward neural network. The NN was trained with comprehensive data set collected from domestic and foreign literature. A very good performance of the neural network was achieved. The model can be used for the simulation and prediction of mechanical properties of AZ system magnesium alloys as functions of composition.

Neuron Optimization Based PID Approach for Cutting Force Control

2006 ◽  
Vol 315-316 ◽  
pp. 85-89
Author(s):  
S. Jiang ◽  
Yan Shen Xu ◽  
J. Wu
Keyword(s):  
Neural Network ◽  
Controller Design ◽  
Open Loop ◽  
Network Control ◽  
Neural Network Control ◽  
System A ◽  
The Neural Network ◽  
Nc Milling ◽  
Hidden Layer ◽  
Control Principle

To improve the cutting efficiency, one of key approaches is to control with constant force in the full depth working condition. And the controller design is vital to realize the real-time feasibility and robustness of the system. A neuron optimization based PID approach is proposed in this paper and adopted in the NC cutting process. This approach optimizes the parameters of PID controller real-timely with the neural network control principle. It not only overcomes the mismatch of the open-loop system model which occurred in constant PID control, but also solves the contradiction between the calculation speed and precision in the neural network which caused by the node choosing of the hidden layer. At last, the simulation has been carried out on a NC milling machine to prove the validity and effectiveness of the proposed approach.

scholarly journals Neural Networks Applied to Estimating Subglacial Topography and Glacier Volume

2009 ◽  
Vol 22 (8) ◽  
pp. 2146-2160 ◽  
Author(s):  
Garry K. C. Clarke ◽  
Etienne Berthier ◽  
Christian G. Schoof ◽  
Alexander H. Jarosch
Keyword(s):  
Neural Network ◽  
Regional Scale ◽  
Ice Thickness ◽  
Ice Dynamics ◽  
Ice Caps ◽  
Mountain Glaciers ◽  
The Neural Network ◽  
Glacier Ice ◽  
Subglacial Topography ◽  
Artificial Neural Network Ann

Abstract To predict the rate and consequences of shrinkage of the earth’s mountain glaciers and ice caps, it is necessary to have improved regional-scale models of mountain glaciation and better knowledge of the subglacial topography upon which these models must operate. The problem of estimating glacier ice thickness is addressed by developing an artificial neural network (ANN) approach that uses calculations performed on a digital elevation model (DEM) and on a mask of the present-day ice cover. Because suitable data from real glaciers are lacking, the ANN is trained by substituting the known topography of ice-denuded regions adjacent to the ice-covered regions of interest, and this known topography is hidden by imagining it to be ice-covered. For this training it is assumed that the topography is flooded to various levels by horizontal lake-like glaciers. The validity of this assumption and the estimation skill of the trained ANN is tested by predicting ice thickness for four 50 km × 50 km regions that are currently ice free but that have been partially glaciated using a numerical ice dynamics model. In this manner, predictions of ice thickness based on the neural network can be compared to the modeled ice thickness and the performance of the neural network can be evaluated and improved. From the results, thus far, it is found that ANN depth estimates can yield plausible subglacial topography with a representative rms elevation error of ±70 m and remarkably good estimates of ice volume.

Dynamic Modeling and Analysis of Active Magnetic Bearings

2014 ◽  
Vol 494-495 ◽  
pp. 685-688
Author(s):  
Rong Gao ◽  
Gang Luo ◽  
Cong Xun Yan
Keyword(s):  
Dynamic Characteristic ◽  
Dynamic Modeling ◽  
Magnetic Bearing ◽  
Integrated System ◽  
Magnetic Bearings ◽  
Active Magnetic Bearing ◽  
Active Magnetic Bearings ◽  
Modeling And Analysis ◽  
Amb System ◽  
Mathematics Method

Active magnetic bearing (AMB) system is a complex integrated system including mechanics, electronic and magnetism. In order to research for the basic dynamic characteristic of rotor supported by AMB, it is necessary to present mathematics method. The dynamics formula of AMB is established using theory means of dynamics of rotator and mechanics of vibrations. At the same tine, the running stability of rotor is analyzed and the example is presented in detail.

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