On adaptive control processes

Bellman, Richard; Kalaba, Robert E.

doi:10.1109/tac.1959.1104847

Cited by 359 publications

(254 citation statements)

References 4 publications

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“…And the angular variation in Equation (13) produces phase modulation to the leakage flux. Correspondingly, the magnetic flux in motor stator can be expressed as (14) This shows that the flux wave contains nonlinear effects because of the fault in the rotor system. This nonlinear interaction of linkage flux will produce corresponding electromagnetic force and hence induce a nonlinear current signal in the stator [4].…”

Section: Phase Current Signal From Reciprocating Compressorsmentioning

confidence: 99%

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Fault diagnosis of motor drives using stator current signal analysis based on dynamic time warping

Zhen

Wang

et al. 2013

Mechanical Systems and Signal Processing

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Electrical motor stator current signals have been widely used to monitor the condition of induction machines and their downstream mechanical equipment. The key technique used for current signal analysis is based on Fourier transform (FT) to extract weak fault sideband components from signals predominated with supply frequency component and its higher order harmonics. However, the FT based method has limitations such as spectral leakage and aliasing, leading to significant errors in estimating the sideband components. Therefore, this paper presents the use of dynamic time warping (DTW) to process the motor current signals for detecting and quantifying common faults in a downstream two-stage reciprocating compressor. DTW is a time domain based method and its algorithm is simple and easy to be embedded into real-time devices. In this study DTW is used to suppress the supply frequency component and highlight the sideband components based on the introduction of a reference signal which has the same frequency components with the supply power. Moreover, a sliding window is designed to process the raw signal using DTW frame by frame for effective calculation. Based on the proposed method, the stator current signals measured from the compressor induced with different common faults and under different loads are analysed for fault diagnosis. Results show that DTW based on residual signal analysis through the introduction of a reference signal, allows the supply components to be suppressed well so that the fault related sideband components are highlighted for obtaining accurate fault detection and diagnosis results. In particular, the root mean square (RMS) values of the residual signal can indicate the differences between the healthy case and different faults under varying discharge pressures. It provides an effective and easy approach to the analysis of motor current signals for better fault diagnosis of the downstream mechanical equipment of motor drives in the time domain in comparison with conventional FT based methods.

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Section: Phase Current Signal From Reciprocating Compressorsmentioning

confidence: 99%

“…Bellman and Kalaba [14] first introduced it on adaptive control processes. It was popularised in the '70s, when it was mainly applied to isolated word recognition and speech recognition [11,[15][16][17] to account for differences in speaking rates between speakers and utterances.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis of motor drives using stator current signal analysis based on dynamic time warping

Zhen

Wang

et al. 2013

Mechanical Systems and Signal Processing

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“…real-time) for adapting the management of dynamic systems is the essence of many dynamic/adaptive control strategies [55,56]. Some strategies allow varying uncertainties -even related to datadriven information -to be explicitely modelled.…”

Section: Uncertainty and Complexity: Semantic Array Programmingmentioning

confidence: 99%

An Architecture for Adaptive Robust Modelling of Wildfire Behaviour under Deep Uncertainty

Rigo

Rodriguez-Aseretto

Bosco

et al. 2013

IFIP Advances in Information and Communication Technology

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Abstract. Wildfires in Europe -especially in the Mediterranean region -are one of the major treats at landscape scale. While their immediate impact ranges from endangering human life to the destruction of economic assets, other damages exceed the spatio-temporal scale of a fire event. Wildfires involving forest resources are associated with intense carbon emissions and alteration of surrounding ecosystems. The induced land cover degradation has also a potential role in exacerbating soil erosion and shallow landslides. A component of the complexity in assessing fire impacts resides in the difference between uncontrolled wildfires and those for which a control strategy is applied. Robust modelling of wildfire behaviour requires dynamic simulations under an array of multiple fuel models, meteorological disturbances and control strategies for mitigating fire damages. Uncertainty is associated to meteorological forecast and fuel model estimation. Software uncertainty also derives from the datatransformation models needed for predicting the wildfire behaviour and its consequences. The complex and dynamic interactions of these factors define a context of deep uncertainty. Here an architecture for adaptive and robust modelling of wildfire behaviour is proposed, following the semantic array programming paradigm. The mathematical conceptualisation focuses on the dynamic exploitation of updated meteorological information and the design flexibility in adapting to the heterogeneous European conditions. Also, the modelling architecture proposes a multicriteria approach for assessing the potential impact with qualitative rapid assessment methods and more accurate a-posteriori assessment.

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“…First, any system that learns from examples must find some way to overcome the "curse of dimensionality" (Bellman 1961;Edelman 2008) i.e. exponential increase in the number of samples required with dimensionality.…”

Section: What Is Dynamic Coordination?mentioning

confidence: 99%

Dynamic Coordination in Brain and Mind

Phillips¹,

von²,

Singer³

2010

Dynamic Coordination in the Brain

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Our goal here is to clarify the concept of "dynamic coordination", and to note major issues that it raises for the cognitive neurosciences. In general, coordinating interactions are those that produce coherent and relevant overall patterns of activity, while preserving the essential individual identities and functions of the activities coordinated. "Dynamic coordination" is the coordination that is created on a moment-by-moment basis so as to deal effectively with unpredictable aspects of the current situation. We distinguish different computational goals for dynamic coordination, and outline issues that arise concerning local cortical circuits, brain systems, cognition, and evolution. Our focus here is on dynamic coordination by widely distributed processes of self-organisation, but we also discuss the role of central executive processes.2

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On adaptive control processes

Cited by 359 publications

References 4 publications

Fault diagnosis of motor drives using stator current signal analysis based on dynamic time warping

Fault diagnosis of motor drives using stator current signal analysis based on dynamic time warping

An Architecture for Adaptive Robust Modelling of Wildfire Behaviour under Deep Uncertainty

Dynamic Coordination in Brain and Mind

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