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.
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.
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.
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.
A solution to the H/sub infinity / mixed sensitivity problem for the SISO (single-input single-output) case is obtained using a Wiener approach to parameterize all equalizing and stabilizing controllers. The controlle...
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A solution to the H/sub infinity / mixed sensitivity problem for the SISO (single-input single-output) case is obtained using a Wiener approach to parameterize all equalizing and stabilizing controllers. The controller which incorporates the LQG (linear quadratic Gaussian) solution has a structure similar to that of D.C. Youla et al. (1976). The system of equations thus obtained is square and has some degree of advantage over previous solutions.< >
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.< >
A modified output error method (MOEM) for model reference adaptive control (MRAC) and identification (MRAI) is introduced. The regressors are properly chosen so that the open-loop system can be compactly expressed as ...
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A modified output error method (MOEM) for model reference adaptive control (MRAC) and identification (MRAI) is introduced. The regressors are properly chosen so that the open-loop system can be compactly expressed as a stable system with an input linear with respect to the unknown plant parameters. The output error satisfies a constructible stable filtered equation. The method does not require any strictly positive real or arbitrary stable filterings, and the uncertainty on the magnitude of the high-frequency gain of the plant does not result in an overparameterization of the identifier. Sufficient conditions on the reference input under which the parameter and output errors converge exponentially to zero are also given.< >
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.< >
A method of reducing overhead caused by the processor synchronization process and common memory access in finely grained tasks is described. The authors propose a hardware configuration to eliminate the synchronizatio...
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A method of reducing overhead caused by the processor synchronization process and common memory access in finely grained tasks is described. The authors propose a hardware configuration to eliminate the synchronization time and a scheduler which minimizes the redundant accesses to shared memory. The proposed scheduler algorithm is processed in parallel. The processes share the common upper bound and the lower bound function which includes the preparation time for shared memory access. The effectiveness of the proposed scheme was confirmed by applying it to the computation of Newton-Euler equations for dynamic arm control, using a multiple digital signal processing system with four TMS320C25 processors.< >
We report on recent progress in the development of a computer-aided engineering (CAE) environment for nonlinear control system analysis and design based on sinusoidal-input describing function (SIDF) methods. Several ...
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We report on recent progress in the development of a computer-aided engineering (CAE) environment for nonlinear control system analysis and design based on sinusoidal-input describing function (SIDF) methods. Several major additions have been made to our nonlinear controls CAE software: ACSL macros were developed to allow the generation of SIDF models of nonlinear plants in a manner analogous to that of the SIMNON-based software developed earlier, and MATLAB routines were developed for the analysis of these models and for the design of general nonlinear controllers based on them. This software provides an integrated tool set for treating very general nonlinear systems with no restrictions on system order, number of nonlinearities, configuration, or nonlinearity type. Based on the new software presented here, the use of SIDF-based nonlinear control system analysis and design methods is substantially easier to carry out and more powerful than before
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