International Journal of Adaptive Control and Signal Processing (Int J Adapt Contr Signal Process)

Publications in this journal

• Article: State estimation for short-time switched linear systems under asynchronous switching

  Weiming Xiang, Jian Xiao, Muhammad Naveed Iqbal
  International Journal of Adaptive Control and Signal Processing 01/2013;
• Source

  Available from: archives-ouvertes.fr

  Article: Fault diagnosis for a class of descriptor linear parameter‐varying systems

  C. M. Astorga‐Zaragoza, D. Theilliol, J. C. Ponsart, M. Rodrigues
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  ABSTRACT: In this paper, a model-based fault estimation method for a particular class of discrete-time descriptor linear parameter-varying systems is developed. The main contribution of this work consists in the design of an observer that performs simultaneously both, the states estimation and the fault magnitude vectors, considered as unknown inputs. The conditions for the existence of such observer are given. Such conditions guarantee the observer stability and they are proved through a Lyapunov analysis combined with a linear matrix inequalities formulation. The fault estimation scheme is evaluated through numerical simulations. Copyright © 2011 John Wiley & Sons, Ltd.
  International Journal of Adaptive Control and Signal Processing 02/2012; 26(3):208 - 223.
• Source

  Available from: hal.archives-ouvertes.fr

  Article: Fault detection and isolation in linear parameter‐varying descriptor systems via proportional integral observer

  H. Hamdi, Mickael Rodrigues, C. Mechmeche, D. Theilliol, N. Benhadj Braiek
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  ABSTRACT: The main contribution of this paper is the design of a polytopic unknown inputs proportional integral observer (UIPIO) for linear parameter-varying (LPV) descriptor systems. This observer is used for actuator fault detection and isolation. The proposed method is based on the representation of the LPV descriptor systems in a polytopic form. Its parameters evolve in an hypercube domain. The designed polytopic UIPIO is also able to estimate both the states and the unknown inputs of the LPV descriptor system. Stability conditions of such observer are expressed in terms of linear matrix inequalities. An example illustrates the performances of such polytopic UIPIO. Copyright © 2011 John Wiley & Sons, Ltd.
  International Journal of Adaptive Control and Signal Processing 02/2012; 26(3):224 - 240.
• Source

  Available from: cas.cz

  Article: Parameter tracking with partial forgetting method

  K. Dedecius, I. Nagy, M. Kárný
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  ABSTRACT: This paper concerns the Bayesian tracking of slowly varying parameters of a linear stochastic regression model. The modelled and predicted system output is assumed to possess time-varying mean value, whereas its dynamics are relatively stable. The proposed estimation method models the system output mean value by time-varying offset. It formulates three extreme hypotheses on model parameters' variability: (i) no parameter varies; (ii) all parameters vary; and (iii) the offset varies. The Bayesian paradigm then provides a mixture as posterior distribution, which is appropriately projected to a feasible class. Exponential forgetting at ‘second’ hypotheses level allows tracking of slow variations of respective hypotheses.The developed technique is an example of a general procedure called partial forgetting. Focus on a simple example allows to demonstrate essence of the approach. Moreover, it is important per se as it corresponds with a varying load of otherwise (almost) time-invariant dynamic system. Copyright © 2011 John Wiley & Sons, Ltd.
  International Journal of Adaptive Control and Signal Processing 12/2011; 26(1):1 - 12.
• Article: Bias compensation‐based parameter estimation for output error moving average systems

  Jie Ding, Feng Ding
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  ABSTRACT: Identification problems of output error models with moving average noises are considered in this paper. The least-squares-based parameter estimation is biased under the colored noises in outputs. Firstly, a bias compensation term is formulated to achieve the bias-eliminated estimates of the system parameters. Secondly, the bias compensation term is determined by the unknown variance of the noise and the unknown noise model, thus based on the hierarchical identification principle, an unbiased parameter estimation is obtained by interactively estimating noise variance and noise parameters. Finally, the estimated bias compensation term is added to the biased parameter estimates. The simulation examples confirm the effectiveness of the proposed algorithm. Copyright © 2011 John Wiley & Sons, Ltd.
  International Journal of Adaptive Control and Signal Processing 11/2011; 25(12):1100 - 1111.
• Source

  Available from: ucsd.edu

  Article: The internal model principle for periodic disturbances with rapidly time‐varying frequencies

  C. E. Kinney, R. A. de Callafon
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  ABSTRACT: In this paper, we study the limitations of scheduling an internal model to reject disturbances with a time-varying frequency. Hence, any adaptive method that uses scheduling and frequency estimation is also limited in the same manner. The limitations of scheduling are to investigate by posing and solving the problem of rejecting periodic disturbances from a multichannel system when the frequencies of the periodic disturbances are changing rapidly in time by designing an scheduled controller that satisfies the internal model principle. The periodic disturbances are modeled by a sum of sinusoids and the frequencies of the disturbances are used for scheduling the controller. It is shown that a controller that regulates input additive disturbances may not regulate the same disturbances added to the output of the system. This is in contrast to the classical case where the frequency of the disturbances is constant. Copyright © 2011 John Wiley & Sons, Ltd.
  International Journal of Adaptive Control and Signal Processing 10/2011; 25(11):1006 - 1022.
• Article: Q(λ)‐learning adaptive fuzzy logic controllers for pursuit–evasion differential games

  Sameh F. Desouky, Howard M. Schwartz
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  ABSTRACT: This paper addresses the problem of tuning the input and the output parameters of a fuzzy logic controller. A novel technique that combines Q(λ)-learning with function approximation (fuzzy inference system) is proposed. The system learns autonomously without supervision or a priori training data. The proposed technique is applied to three different pursuit–evasion differential games. The proposed technique is compared with the classical control strategy, Q(λ)-learning only, and the technique proposed by Dai et al. (2005) in which a neural network is used as a function approximation for Q-learning. Computer simulations show the usefulness of the proposed technique. Copyright © 2011 John Wiley & Sons, Ltd.
  International Journal of Adaptive Control and Signal Processing 09/2011; 25(10):910 - 927.
• Article: Nonlinear system modeling and identification using Volterra‐PARAFAC models

  Gérard Favier, Alain Y. Kibangou, Thomas Bouilloc
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  ABSTRACT: Discrete-time Volterra models are widely used in various application areas. Their usefulness is mainly because of their ability to approximate to an arbitrary precision any fading memory nonlinear system and to their property of linearity with respect to parameters, the kernels coefficients. The main drawback of these models is their parametric complexity implying the need to estimate a huge number of parameters. Considering Volterra kernels of order higher than two as symmetric tensors, we use a parallel factor (PARAFAC) decomposition of the kernels to derive Volterra-PARAFAC models that induce a substantial parametric complexity reduction. We show that these models are equivalent to a set of Wiener models in parallel. We also show that Volterra kernel expansions onto orthonormal basis functions (OBF) can be viewed as Tucker models that we shall call Volterra-OBF-Tucker models. Finally, we propose three adaptive algorithms for identifying Volterra-PARAFAC models when input–output signals are complex-valued: the extended complex Kalman filter, the complex least mean square (CLMS) algorithm and the normalized CLMS algorithm. Some simulation results illustrate the effectiveness of the proposed identification methods. Copyright © 2011 John Wiley & Sons, Ltd.
  International Journal of Adaptive Control and Signal Processing 09/2011; 26(1):30 - 53.
• Article: Transient analysis of diffusion least‐mean squares adaptive networks with noisy channels

  Azam Khalili, Mohammad Ali Tinati, Amir Rastegarnia, Jonathon A. Chambers
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  ABSTRACT: In this paper, we study the effect of noisy channels on the transient performance of diffusion adaptive network with least-mean squares (LMS) learning rule. We first drive the update equation of diffusion LMS which incorporates the effects of noisy channels. Then, using the framework of fundamental weighted energy conservation relation, we derive closed-form expressions for learning curves in terms of mean-square deviation and excess mean-square error. We also find the mean and mean-square stability bounds of step-size for diffusion LMS with noisy channels. We show that although noisy channels affect the performance of the diffusion LMS network, the stability bounds of the step-size are the same form as in the ideal channels case. The derived closed-form expressions are shown to provide a good match with values found by simulation. Copyright © 2011 John Wiley & Sons, Ltd.
  International Journal of Adaptive Control and Signal Processing 09/2011; 26(2):171 - 180.
• Source

  Available from: soton.ac.uk

  Article: Process fault prognosis using a fuzzy‐adaptive unscented Kalman predictor

  Xuemin Tian, Yu Ping Cao, Sheng Chen
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  ABSTRACT: By monitoring the future process status via information prediction, process fault prognosis is able to give an early alarm and therefore prevent faults, when the faults are still in their early stages. A fuzzy-adaptive unscented Kalman filter (FAUKF)-based predictor is proposed to improve the tracking and forecasting capability for process fault prognosis. The predictor combines the strong tracking concept and fuzzy logic idea. Similar to the standard adaptive unscented Kalman filter (AUKF) that employs an adaptive parameter to correct the estimation error covariance, a Takagi–Sugeno fuzzy logic system is designed to provide a better adaptive parameter for smoothing this regulation. Compared with the standard AUKF, the proposed FAUKF has the same strong tracking ability but does not suffer from the drawback of serious tracking fluctuation. Two simulation examples demonstrate the effectiveness of the proposed predictor. Copyright © 2011 John Wiley & Sons, Ltd.
  International Journal of Adaptive Control and Signal Processing 08/2011; 25(9):813 - 830.
• Article: Fault tolerance evaluation based on the lattice of system configurations

  Marcel Staroswiecki, Christian Commault, Jean‐Michel Dion
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  ABSTRACT: The lattice of component subsets is a very useful structure for addressing fault tolerance and architecture design problems for systems described as a set of components. This paper presents a number of concepts and techniques that are associated with this lattice to evaluate the degree of fault tolerance of a given property and to classify components with respect to their usefulness for this property. Being very general, the approach needs no assumption on the system, nor on the properties to be satisfied, and allows both deterministic and probabilistic measures to be used. A sensor selection example illustrates the practical use of the proposed tools. Copyright © 2011 John Wiley & Sons, Ltd.
  International Journal of Adaptive Control and Signal Processing 08/2011; 26(1):54 - 72.
• Article: Signal quantization by variation

  Thomas Kämpke
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  ABSTRACT: Real-valued data are quantized according to the magnitude of their fluctuations around regional mean values or around a given, real-valued signal. Quantization does not require any parameter or threshold value except the desired number of regions. By the introduction of suitable fluctuation measures and a so-called change point graph, the determination of a minimum quantization is transformed to the computation of the shortest path with a prescribed number of intermediate nodes. Such shortest paths are shown to be computable by dynamic programming and by a variation of the Dijkstra algorithm. Evaluations and extensions of the approach are included. Copyright © 2011 John Wiley & Sons, Ltd.
  International Journal of Adaptive Control and Signal Processing 07/2011; 25(12):1061 - 1073.
• Article: Structured fault detection filters for LPV systems modeled in an LFR manner

  D. Henry
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  ABSTRACT: This paper investigates the design of robust‡ fault detection and isolation filters for linear parameter-varying systems modeled in a linear fractional representation fashion. The goal is to obtain structured fault detection filters with enhanced fault transmission H− gain and large H∞ nuisance attenuation. It is shown by means of the scaling matrices technique and the projection lemma that the synthesis of the residual structuring and the filter state-space matrices can be performed simultaneously using linear matrix inequality optimization techniques. Computational aspects are discussed and it is shown that the proposed solution is structurally well defined. Closed-loop time simulations demonstrate the efficiency of the proposed method. Copyright © 2011 John Wiley & Sons, Ltd.
  International Journal of Adaptive Control and Signal Processing 06/2011; 26(3):190 - 207.
• Source

  Available from: gatech.edu

  Article: Neuroadaptive output feedback control for nonlinear nonnegative dynamical systems with actuator amplitude and integral constraints

  Kostyantyn Y. Volyanskyy, Wassim M. Haddad, James M. Bailey
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  ABSTRACT: A neuroadaptive output feedback control architecture for nonlinear nonnegative dynamical systems with input amplitude and integral constraints is developed. Specifically, the neuroadaptive controller guarantees that the control amplitude as well as the integral of the control input over a given time interval are constrained, and the physical system states remain in the nonnegative orthant of the state space. The proposed approach is used to control the infusion of the anesthetic drug propofol for maintaining a desired constant level of depth of anesthesia for noncardiac surgery in the face of infusion rate constraints and an integral drug dosing constraint over a specified time period. Copyright © 2010 John Wiley & Sons, Ltd.
  International Journal of Adaptive Control and Signal Processing 05/2011; 25(6):483 - 501.