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.
In recent years reliable methods for designing multivariable feedback controlsystems have been intensively studied. System problems such as sensitivity, minimalization, robustnes to model uncertainties and systems fa...
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In recent years reliable methods for designing multivariable feedback controlsystems have been intensively studied. System problems such as sensitivity, minimalization, robustnes to model uncertainties and systems fault tolerance have been the subject of particular investigations aiming to improve MIMO systems reliability. The purpose of this paper is to present some recent results in the area of linear multivariable controlsystems reliability improvement, which have been obtained using robustness theory. The research reported here is less than ten years old and is in continuous progress.
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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The siandard H ∞ control problems has now been solved, in which some rank conditions are imposed on the plant data. These conditions are sometimes not satisfied in practical applications, and it is desirable to obtai...
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The siandard H ∞ control problems has now been solved, in which some rank conditions are imposed on the plant data. These conditions are sometimes not satisfied in practical applications, and it is desirable to obtain general solutions to “non-siandard” problems which do not require these conditions. In this paper, we shall derive a parameterization of the H ∞ controller for a non-standard two-block case which include the state feedback control law as a speial case.
An on-line scheme to failure diagnosis is proposed for dynamic systems under adaptive control, which is designed based on a direct approach to self-tuning regulator. Failure modes occurred in the system are assumed to...
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An on-line scheme to failure diagnosis is proposed for dynamic systems under adaptive control, which is designed based on a direct approach to self-tuning regulator. Failure modes occurred in the system are assumed to be described by unexpected changes in physical parameters of the system. The parameter changes in the controlled system can effectively be detected by using Kullback Discrimination Information (KDI) as an index for model discrimination. In order to decide whether the detected system parameter change is caused by a failure or not, a fuzzy inference approach to failure decision is considered. Some appropriate membership functions which describe fuzzy events of failures are constructed to perform the fuzzy inference. In this way, useful knowledge about failure modes which is available from, e.g., experts can be introduced into the model- based diagnosis technique. Simulation studies of a second-order damped oscillator have been carried out to demonstrate the effectiveness of the method.
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.
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