Application of model-based fault detection to a brushless DC motor

Moseler, Olaf; Isermann, Rolf

doi:10.1109/41.873209

Cited by 197 publications

(83 citation statements)

References 11 publications

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“…In stator winding, back Electro Magnetic Field (EMF) is generated when the rotating magnet interacts with the stator pole. The model of BLDC motor is taken from [39].…”

Section: Design Of the Control Algorithmmentioning

confidence: 99%

Trajectory Tracking of a Tri-Rotor Aerial Vehicle Using an MRAC-Based Robust Hybrid Control Algorithm

et al. 2017

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Abstract:In this paper, a novel Model Reference Adaptive Control (MRAC)-based hybrid control algorithm is presented for the trajectory tracking of a tri-rotor Unmanned Aerial Vehicle (UAV). The mathematical model of the tri-rotor is based on the Newton-Euler formula, whereas the MRAC-based hybrid controller consists of Fuzzy Proportional Integral Derivative (F-PID) and Fuzzy Proportional Derivative (F-PD) controllers. MRAC is used as the main controller for the dynamics, while the parameters of the adaptive controller are fine-tuned by the F-PD controller for the altitude control subsystem and the F-PID controller for the attitude control subsystem of the UAV. The stability of the system is ensured and proven by Lyapunov stability analysis. The proposed control algorithm is tested and verified using computer simulations for the trajectory tracking of the desired path as an input. The effectiveness of our proposed algorithm is compared with F-PID and the Fuzzy Logic Controller (FLC). Our proposed controller exhibits much less steady state error, quick error convergence in the presence of disturbance or noise, and model uncertainties.

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“…In stator winding, back Electro Magnetic Field (EMF) is generated when the rotating magnet interacts with the stator pole. The model of BLDC motor is taken from [39].…”

Section: Design Of the Control Algorithmmentioning

confidence: 99%

Trajectory Tracking of a Tri-Rotor Aerial Vehicle Using an MRAC-Based Robust Hybrid Control Algorithm

et al. 2017

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“…Parameter estimation methods are based on the principle that accepts sudden changes in parameters like friction, mass, viscosity, resistance etc. using system identification methods as a sign of faults (Isermann & Ballé, 1997;Moseler & Isermann, 2000). In addition to proposing new methods, all these methods are investigated for the robustness against disturbances and uncertainties.…”

Section: Literature Overview Of Model-based Fdi For Nonlinear Systemsmentioning

confidence: 99%

Model-Based FDI Schemes For Robot Manipulators Using Soft Computing Techniques

Yüksel¹,

Sezgin²

2010

Fault Detection

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“…Unfortunately, their main drawback is the mechanical collector, which has only a limited life span. In addition, brush sparking can destroy the rotor coil, generate electromagnetic compatibility problems and reduce insulation resistance to an unacceptable limit (Moseler and Isermann, 2000). Moreover, in many cases, electrical motors operate in closed-loop control and small faults often remain hidden by the control loop.…”

Section: Neural Network Based Fault Diagnosis Of a DC Motormentioning

confidence: 99%

“…Therefore, there is a need to detect and isolate faults as early as possible. Recently, a great deal of attention has been paid to electrical motor fault diagnosis (Nandi et al, 2005;Li et al, 2004;Moseler and Isermann, 2000;Xiang-Qun and Zhang, 2000;Fuessel and Isermann, 2000). In general, the elaborated solutions can be splitted into three categories: signal analysis methods, knowledge based methods and model based approaches (Xiang-Qun and Zhang, 2000;Korbicz et al, 2004).…”

Section: Neural Network Based Fault Diagnosis Of a DC Motormentioning

confidence: 99%

Towards Robustness in Neural Network Based Fault Diagnosis

Patan¹,

Witczak²,

Korbicz³

2008

International Journal of Applied Mathematics and Computer Science

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Challenging design problems arise regularly in modern fault diagnosis systems. Unfortunately, classical analytical techniques often cannot provide acceptable solutions to such difficult tasks. This explains why soft computing techniques such as neural networks become more and more popular in industrial applications of fault diagnosis. Taking into account the two crucial aspects, i.e., the nonlinear behaviour of the system being diagnosed as well as the robustness of a fault diagnosis scheme with respect to modelling uncertainty, two different neural network based schemes are described and carefully discussed. The final part of the paper presents an illustrative example regarding the modelling and fault diagnosis of a DC motor, which shows the performance of the proposed strategy.

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Application of model-based fault detection to a brushless DC motor

Cited by 197 publications

References 11 publications

Trajectory Tracking of a Tri-Rotor Aerial Vehicle Using an MRAC-Based Robust Hybrid Control Algorithm

Trajectory Tracking of a Tri-Rotor Aerial Vehicle Using an MRAC-Based Robust Hybrid Control Algorithm

Model-Based FDI Schemes For Robot Manipulators Using Soft Computing Techniques

Towards Robustness in Neural Network Based Fault Diagnosis

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