Application of the certainty equivalence principle in the dynamic control problem of an unstable random access (RA) channel results in a nonlinear separation control rule which can be implemented in a decentralized fa...
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Application of the certainty equivalence principle in the dynamic control problem of an unstable random access (RA) channel results in a nonlinear separation control rule which can be implemented in a decentralized fashion and yields maximum stable throughout e-1. Stability of the controlled RA channel is proven by showing that the channel backlog and its least squares estimate from an ergodic double Markov chain. Tight upper and lower bounds on the invariant probability distribution are also derived when the backlog is precisely known. The bounds are loglinear functions of the channel backlog and can be readily used to compute bounds on the average backlog and delay in the RA channel. An adaptive realization of the control is introduced for an RA channel with unknown traffic rates. Extensive simulation results are provided and the performance of our control is compared with other existing distributed controls.
A method of reference model decomposition, an extension of model reference adaptive control, is presented. The decomposition method can be regarded as a way of including knowledge about the structure and parameters of...
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A method of reference model decomposition, an extension of model reference adaptive control, is presented. The decomposition method can be regarded as a way of including knowledge about the structure and parameters of unmodeled dynamics in the adaptive system, making it possible to choose a lower order controller which is only equipped for the nominal process part. To illustrate the decomposition method, an adaptive controller for a scale model of a gantry crane is presented. Simplifying and linearizing the mathematical equations describing the crane yields a fourth-order model, of which the dynamics of the load swing take two. A standard adaptive control algorithm needs eight parameters, which in practice yields unacceptable behavior. The decomposition method allows the use of only two adjustable parameters, and real-time experiments showing practical results obtained with the method are described.< >
作者：
P. LöhnbergB. FrankeControl Laboratory
Control Systems and Computer Engineering Group Department of Electrical Engineering University of Twente Enschede The Netherlands
Optimal parameter estimation and experiment design requires a weighting between the cost of parameter errors and that of identification itself. Having identified this need, the general principle to obtain both costs f...
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Optimal parameter estimation and experiment design requires a weighting between the cost of parameter errors and that of identification itself. Having identified this need, the general principle to obtain both costs for using the model in control design is described. Fast experiment design is achieved by using analytic expressions to calculate the optimal experiment parameters. This is illustrated by a simple example. It is shown that the use of the resulting model criterion in optimal identification is equivalent to cautious stochastic control. A more realistic example is illustrated by simulation.
This paper presents a foundation for an integrated approach to the design of controls and diagnostics in reliable controlsystems. In this approach the control module and diagnostic module of the control system are de...
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Some fundamental results related to binary and multi-level decision logic distributed decision (evidence) fusion (DD(E)F) are presented. New asymptotic performance results for binary and multi-level Neyman-Pearson (N-...
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Some fundamental results related to binary and multi-level decision logic distributed decision (evidence) fusion (DD(E)F) are presented. New asymptotic performance results for binary and multi-level Neyman-Pearson (N-P) DD Farederived. Acomparative resultbetween N-P DDF and Dempster-Shafer DEF in the framework of the Generalized Evidence Processing (GEP) theory is presented under a specific decision rule at the fusion.
In this paper a new method for the computation of the optimal step in gradient algorithms is presented. This method improves the convergence of the gradient algorithms and outperforms any other suboptimal scheme on li...
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In this paper a new method for the computation of the optimal step in gradient algorithms is presented. This method improves the convergence of the gradient algorithms and outperforms any other suboptimal scheme on linear problems while it does not require any additional storage. The method may be also applied to problems with state or control constraints, linear time varying systems and, via linearization, to nonlinear systems as well.
The dual relation between the Model Reference Adaptive control (MRAC) and Identification (MRAI) problems is discussed. A common framework for both problems is established.
The dual relation between the Model Reference Adaptive control (MRAC) and Identification (MRAI) problems is discussed. A common framework for both problems is established.
The objective of this paper is to develop a systematic procedure for deriving control-relevant parameter estimation algorithms for linear models represented via the prediction-error model structure. The key element in...
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The objective of this paper is to develop a systematic procedure for deriving control-relevant parameter estimation algorithms for linear models represented via the prediction-error model structure. The key element in the design procedure is the prefiltering of the input and output time series obtained from the plant. The prefiltering step insures that the estimated model retains those plant characteristics that are most significant with regards to the user's control requirements. In this paper we employ linear fractional representations of the closed-loop system to obtain a general statement of the control-relevant parameter estimation problem which applies to different types of models and control structures. The prefilters obtained via this technique incorporate explicitly the model structure, the desired closed-loop transfer functions, and the setpoint/disturbance characteristics of the control problem. The proposed procedure is then applied to obtain prefilters for models to be used to design feedback, feedforward, and decentralized controllers.
A common framework for model reference adaptive identification and control (MRAI and MRAC) is established. The key idea for this common framework is to maintain the same set of equations for describing the parameteriz...
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A common framework for model reference adaptive identification and control (MRAI and MRAC) is established. The key idea for this common framework is to maintain the same set of equations for describing the parameterizations of the plant and the model and to solve the control equation properly for each case (identification or control) for the corresponding tuned system, i.e. the model (identification) or the plant (control). Within this framework, two methods, the MOEM (modified output error method) and the IEM (input error method), for the MRAI and MRAC problems are studied, and global asymptotic stability properties are established.< >
An effective method for neural network based visual pattern recognition is presented. It is shown that it can be successfully used for visual recognition of deformed letters. The main advantages of the presented metho...
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An effective method for neural network based visual pattern recognition is presented. It is shown that it can be successfully used for visual recognition of deformed letters. The main advantages of the presented method are its intuitive appeal, simple implementation and analytical justification.< >
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