We introduce the concept of D -differentiability of matrices over the (max,+) algebra. Specifically, we view the stochastic (max,+)-linear system x ( k + 1) = A ( k )⊗ x ( k ), for k ≥ 0 with x (0) = x 0 , as a Marko...
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We introduce the concept of D -differentiability of matrices over the (max,+) algebra. Specifically, we view the stochastic (max,+)-linear system x ( k + 1) = A ( k )⊗ x ( k ), for k ≥ 0 with x (0) = x 0 , as a Markov chain the transition dynamic of which is given through the matrices A ( k ). Elaborating on the product rule of D -differentiability for Markov kernels, we obtain results on differentiability of (max,+)-linear systems and unbiased gradient estimators as well. Moreover, we establish sufficient conditions for deducing the differentiability of a (max,+)-linear system from that of the firing time distributions of the corresponding stochastic event graph. The results hold uniformly on a predefined class of performance functions. We illustrate our approach with an analysis of joint characteristics of waiting times in a (max,+)-linear queueing network.
In this paper we apply a method for diagnosing faults on hybrid systems to a model of a camera mounted on a mobile robot. A hybrid system is a system mixing continuous and discrete behaviors that cannot be faithfully ...
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In this paper we apply a method for diagnosing faults on hybrid systems to a model of a camera mounted on a mobile robot. A hybrid system is a system mixing continuous and discrete behaviors that cannot be faithfully modeled neither by using a formalism with continuous dynamics only nor by a formalism including only discrete dynamics. We use the well known framework of hybrid automata for modeling hybrid systems, and try do detect faults exploiting a Fault Diagnosis Game on them, with two players: the environment and the diagnoser. The environment controls the evolution of the system and chooses whether and when a fault occurs. The diagnoser observes the external behavior of the system and announces whether a fault has occurred or not. The case study we introduce here is a simplified model of a camera mounted on a mobile rover that has to take pictures of some defined locations of the environment. We add the possibility of a stuck fault on the camera motor to apply the theory and show its effectiveness.
The paper deals with a problem of state estimation for nonlinear continuous stochastic systems with discrete-time measurements. A general recursive solution of the estimation problem given by the Bayesian rule and by ...
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The paper deals with a problem of state estimation for nonlinear continuous stochastic systems with discrete-time measurements. A general recursive solution of the estimation problem given by the Bayesian rule and by the Fokker-Planck equation is described. Local estimation methods employing analytical approach to solution and global estimation methods employing analytical, numerical and simulation approaches are discussed. A software package for state estimation of continuous stochastic systems with discrete-time measurements is developed and described. It serves for system design, system simulation, estimator setup and state estimation. The package is designed to embody easily user defined estimators and thus it is suitable for estimator testing and quality comparison of different estimators. Usage of the nonlinear filtering software package is illustrated in a numerical example.
Abstract Three approaches of iterative learning control (ILC) applied to a Gantry-Tau parallel kinematic robot are studied; ILC algorithms using 1) measured motor angles, 2) tool-position estimates, and for evaluation...
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Abstract Three approaches of iterative learning control (ILC) applied to a Gantry-Tau parallel kinematic robot are studied; ILC algorithms using 1) measured motor angles, 2) tool-position estimates, and for evaluation purposes, 3) measured tool position. The approaches are compared experimentally, with the tool performance evaluated using external sensors. It is concluded that the tool performance can be improved using tool-position estimates in the ILC algorithm, compared to when using motor-angle measurements. Applying ILC algorithms to a system following trajectories with so-called lead-in/lead-out is also considered in the paper.
This paper establishes that when using a least squares criterion to estimate an output error type model structure, then the measurement noise induced variability of the frequency response estimate depends on the estim...
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This paper establishes that when using a least squares criterion to estimate an output error type model structure, then the measurement noise induced variability of the frequency response estimate depends on the estimated (and hence also on the true) pole positions. This dependence on pole position is perhaps counter to prevailing wisdom that for any 'shift invariant' model structure, the variability depends only on model order, data length, and input and noise spectral densities. That is, it is counter to the belief that variance error is model-structure independent.
The paper reports on the on-going development of an interactive computer package for assisrmg in the introduction and in the reinforcement of concepts of discrete time adaptive/self-tuning control to undergraduates in...
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The paper reports on the on-going development of an interactive computer package for assisrmg in the introduction and in the reinforcement of concepts of discrete time adaptive/self-tuning control to undergraduates in the area of electronics, computer systems and control engineering. The project is being carried out jointly by staff of the Control Theory and Applications Centre, Coventry University, UK, and the Institute of Control and Systems Engineering, Technical University of Wroclaw, Poland.
In the paper sound signal compression algorithm in real-time systems is considered. The main consideration is devoted to streaming bits allocation control module and various methods of its implementation. Adaptive Kal...
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In the paper sound signal compression algorithm in real-time systems is considered. The main consideration is devoted to streaming bits allocation control module and various methods of its implementation. Adaptive Kalman filter has been proposed as an alternative for existing ways. Quality of reconstructed signals obtained by different bit control methods was estimated and compared.
In recent yeas, long-range accuracy cooperative navigation(CN) for multiple UUVs under complex marine environment is getting more and more attention. In this paper, firstly, the state-of-the-art of multiple UUV cooper...
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In recent yeas, long-range accuracy cooperative navigation(CN) for multiple UUVs under complex marine environment is getting more and more attention. In this paper, firstly, the state-of-the-art of multiple UUV cooperation system is introduced. Then, the characteristic and system structure of multiple UUVs CN are analyzed, as well as the CN methods. Finally, based on the current development of CN system, the prospect of CN for multiple UUVs is brought forward in terms of the requirement, hardware, communication, and so on.
In this paper, Artificial Neural Networks (ANN) have been applied in thermal processes related to liquid immersed distribution transformers. This technique allows to monitor and to estimate the heating of transformers...
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In this paper, Artificial Neural Networks (ANN) have been applied in thermal processes related to liquid immersed distribution transformers. This technique allows to monitor and to estimate the heating of transformers in order to improve its performance and to reduce the insulation degradation. The insulation degradation in transformers is usually computed taking into account the hot spot temperature. However, the characteristics and properties related with this temperature are not very well known and its identification has been a difficult task. On the other hand, the ability of neural networks in to solve complex and diversified problems make them attractive for estimation of thermal processes associated with transformers.
Stochastic volatility models aie a well-known framework for the analysis of financial time series data, together with the other important class of ARCH-type models. The main difference between them, at least from a st...
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Stochastic volatility models aie a well-known framework for the analysis of financial time series data, together with the other important class of ARCH-type models. The main difference between them, at least from a statistical point of view, relies on the possibility of obtaining exact inference, in particular with regard to the estimation issue. Whereas for ARCH-type models the standard results apply, in the sense that maximum likelihood estimates for the parameters of interest can be computed, for stochastic volatility models there are more complications and usually only approximate results can be obtained, unless two particular estimation strategies are employed: exact non-Gaussian filtering methods or simulation techniques. This paper stresses the importance of "only" approximate and therefore suboptimal estimation methods for special models whose complexity makes it difficult to find exact solutions. The setup where the analysis is conducted is the state-space formulation and this suggests enclosing the cases here considered in a class of so-called stochastic volatility systems.
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