Intelligent Kalman Filtering for Fault Detection on an Active Magnetic Bearing System

2008 ◽  
Author(s):  
Nana K. Noel ◽  
Kari Tammi ◽  
Gregory D. Buckner ◽  
Nathan S. Gibson
Keyword(s):  
Neural Network ◽  
Kalman Filter ◽  
Fault Detection ◽  
Magnetic Bearing ◽  
Active Magnetic Bearing ◽  
Estimation Accuracy ◽  
False Alarms ◽  
Single Input Single Output ◽  
Single Output ◽  
Modeling Uncertainties

One of the challenges of condition monitoring and fault detection is to develop techniques that are sufficiently sensitive to faults without triggering false alarms. In this paper we develop and experimentally demonstrate an intelligent approach for detecting faults in a single-input, single-output active magnetic bearing. This technique uses an augmented linear model of the plant dynamics together with a Kalman filter to estimate fault states. A neural network is introduced to enhance the estimation accuracy and eliminate false alarms. This approach is validated experimentally for two types of fabricated faults: changes in suspended mass and coil resistance. The Kalman filter alone is shown to be incapable of identifying all fault cases due to modeling uncertainties. When an artificial neural network is trained to compensate for these uncertainties, however, all fault conditions are identified uniquely.

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.

A hybrid method for lithium-ion batteries state-of-charge estimation based on gated recurrent unit neural network and an adaptive unscented Kalman filter

2021 ◽  
pp. 1-36
Author(s):  
Shuai Xu ◽  
Fei Zhou ◽  
Yucheng Liu
Keyword(s):  
Neural Network ◽  
Kalman Filter ◽  
Hybrid Method ◽  
Unscented Kalman Filter ◽  
Absolute Error ◽  
State Of Charge ◽  
Estimation Accuracy ◽  
Adaptive Unscented Kalman Filter ◽  
Battery State Of Charge ◽  
Gated Recurrent Unit

Abstract Among the battery state of charge estimation methods, the Kalman-based filter algorithms are sensitive to the battery model while the neural network-based algorithms are decided by hyperparameters. In this paper, a hybrid approach composed of a gated recurrent unit neural network and an adaptive unscented Kalman filter method is proposed. A gated recurrent unit neural network is first used to acquire the nonlinear relationship between the battery state of charge and battery measurement signals, and then an adaptive unscented Kalman filter is utilized to filter out the output noise of the neural network to further improve estimation accuracy. The hybrid method avoids the establishment of accurate battery models and the search for optimal hyperparameters. The data of dynamical street test and US06 test are used as training dataset and validation dataset, respectively, while the data collected from the tests under federal urban driving schedules and Beijing driving cycle conditions are taken as testing dataset. As compared with some hybrid methods proposed in other literature, the hybrid method has the best estimation accuracy and generalization for various driving cycles at different ambient temperatures. The root mean square error and the mean absolute error all are less than 1.5%, and the maximum absolute error are less than 2%. In addition, it also exhibits powerful robustness against the abnormal values of the battery signals and can converge to the true value in just 5 seconds.

Sliding mode control with deep learning method for rotor trajectory control of active magnetic bearing system

2018 ◽  
Vol 41 (5) ◽  
pp. 1383-1394 ◽  
Author(s):  
Xuan Yao ◽  
Zhaobo Chen
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sliding Mode Control ◽  
Convolutional Neural Network ◽  
Sliding Mode ◽  
Magnetic Bearing ◽  
Trajectory Control ◽  
Active Magnetic Bearing ◽  
Learning Method ◽  
Mode Control

Active magnetic bearing (AMB) is competent in rotor trajectory control for potential applications such as mechanical processing and spindle attitude control, while the highly nonlinear and coupled dynamic characteristics especially in the condition of rotor large motion are obstacles in controller design. In this paper, a controller of AMB is proposed to achieve rotor 3D trajectory control. First, the dynamic model of the AMB-rotor system containing a nonlinear electromagnetic force model is introduced. Then the DCNN-SMC (deep convolutional neural network - sliding mode control) controller is proposed. Sliding mode control is used to achieve the tracking control with high robustness and responsiveness, and a deep convolutional neural network based on deep learning method is designed to compensate the uncertainties of the system. Finally, simulation of a 5-degree of freedom (DOF) system on various trajectories demonstrates evident control effect of the proposed controller in precision and significant effect of DCNN based on deep learning method in compensation control.

Adaptive feedback suppression of unknown periodic components of acoustic noises with time-varying characteristics

2016 ◽  
Vol 23 (4) ◽  
pp. 526-538 ◽  
Author(s):  
Saeid Jafari ◽  
Petros Ioannou ◽  
Lael Rudd
Keyword(s):  
Random Noise ◽  
Output Feedback Control ◽  
Design Parameters ◽  
Single Input Single Output ◽  
Feedback Suppression ◽  
Single Output ◽  
Modeling Uncertainties ◽  
Single Input ◽  
Robust Adaptive ◽  
Periodic Components

Effective attenuation of the noise level is an important problem in acoustic systems. In this paper, we propose a robust adaptive output feedback control scheme that can considerably attenuate narrow-band noises made up of periodic signals mixed with random noise in the presence of modeling uncertainties. The amplitude, phase and frequencies as well as the number of periodic terms are unknown and could vary with time. The performance and robustness of the proposed scheme with respect to unstructured modeling uncertainties are analyzed for continuous-time single-input, single-output systems; the results, however, are extendable to multi-channel systems. The successful attenuation of the unknown periodic components of the disturbance despite the time variations, modeling errors, and random noise is demonstrated using simulations. In addition, guidelines how to choose certain design parameters for performance improvement have been presented.

scholarly journals A New Scheduling Quantitative Feedback Theory-Based Controller Integrated with Fault Detection for Effective Vibration Control

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
R. Jeyasenthil ◽  
Yang-Sup Lee ◽  
Seung-Bok Choi
Keyword(s):  
Fault Detection ◽  
Control Method ◽  
Point Of View ◽  
Smart Structure ◽  
Time Interval ◽  
Time Varying ◽  
Quantitative Feedback Theory ◽  
Control Effort ◽  
Single Input Single Output ◽  
Single Output

In this work, a new integrated fault detection and control (IFDC) method is presented for single-input/single-output systems (SISOs). The idea is centered on comparing the closed-loop output between the faulty system and fault-free one to schedule/switch the feedback control once the fault occurs. The problem addressed in this work is the output disturbance rejection. The set of feedback controllers are designed using quantitative feedback theory (QFT) for fault-free and faulty systems. In the context of QFT-based IFDC, the proposed active approach is novel, simple, and easy to implement from an engineering point of view. The efficiency of the proposed method is assessed on a flexible smart structure system featuring a piezoelectric actuator. The actuator and sensor faults considered are the multiplicative type with both fixed and time-varying magnitudes. In the fixed magnitude fault case, the actuator/sensor output delivering capability is reduced by 50% (multiplying a factor of 0.5 to its actual output), while in the time-varying magnitude case, it becomes 60% to 50% for a particular time interval. In both cases, the proposed control method identifies the fault and activates the required controller to satisfy the specification with less control effort as opposed to the passive QFT design featured by faulty system design alone.

scholarly journals Coupling Analysis and Cross-Feedback Control of Three-Axis Inertially Stabilized Platform with an Active Magnetic Bearing System

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Tong Wen ◽  
Biao Xiang ◽  
Waion Wong
Keyword(s):  
Feedback Control ◽  
Vibration Isolation ◽  
Base Plate ◽  
Magnetic Bearing ◽  
Active Magnetic Bearing ◽  
Single Input Single Output ◽  
Control Scheme ◽  
Amb System ◽  
Stabilized Platform ◽  
Inertially Stabilized Platform

An active magnetic bearing (AMB) system is used to suspend the yaw gimbal of three-axis inertially stabilized platform (ISP) to minimize the friction. The dynamic functions of three gimbals in ISP are developed. The base coupling at dynamic base plate is stronger than that at static base plate, and the gimbal coupling among three gimbals increases with the number of unlocked gimbals. Therefore, a cross-feedback control scheme is designed to minimize the base coupling and the gimbal coupling, and then the multi-input multioutput system of three-axis ISP with coupling terms is simplified into three decoupled single-input single-output systems. Experimental results verify that the yaw gimbal suspended by AMB system has better vibration isolation ability than the roll gimbal supported by mechanical bearing, and the gimbal coupling and the base coupling are effectively suppressed by the cross-feedback control scheme.

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