The problem of disturbance rejection with simultaneous triangular decoupling via proportional state feedback for linear, time-invariant systems is discussed. The following two major aspects are solved: the necessary a...
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The problem of disturbance rejection with simultaneous triangular decoupling via proportional state feedback for linear, time-invariant systems is discussed. The following two major aspects are solved: the necessary and sufficient conditions for the problem to have a solution, and the general analytical expressions of the controller matrices.< >
作者:
R.A. HilhorstJ. van AmerongenP. LöhnbergH.J.A.F. TullekenControl
Systems and Computer Engineering Group Dept of Electrical Engineering University of Twente and Mechatronics Research Centre Twente P. O. Box 217 7500 AE Enschede The Netherlands Process Systems Group
Dept of Mathematics & Systems Engineering Koninklijke/Shell-Laboratorium (Shell Research B.V.) P.O. Box 3003 1003 AA Amsterdam The Netherlands
The intelligence of controllers has increased over the decades. However, the number of applications of adaptive controllers is still restricted, due to practical limits of the implemented continuous adaptation. For pr...
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The intelligence of controllers has increased over the decades. However, the number of applications of adaptive controllers is still restricted, due to practical limits of the implemented continuous adaptation. For processes which operate only in a limited number of modes (so called mode-switch processes), constant adaptation is not needed or desired. In this paper an intelligent extension of adaptive control will be presented, in which process behaviour can be stored in a memory, retrieved from it and evaluated for each mode of operation. This intelligent memory concept leads to an adaptive control structure which, after a learning phase, quickly adjusts the controller parameters based on retrieval of old information, without the need to relearn every time. This approach has been tested on a simulation model of an assembly robot, but it is directly applicable to many processes in the (petro)chemical industry.
This paper examines the system identification problem from the standpoint of control system design. Noting first that nearly all robust control design methods require explicit worst-case/deterministic bounds on the ex...
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This paper examines the system identification problem from the standpoint of control system design. Noting first that nearly all robust control design methods require explicit worst-case/deterministic bounds on the existing plant uncertainty, it is argued that the class of system identification methods which are inherently compatible with robust control design methods-or control-oriented is a subset of the class of system identification methods which yield an explicit worst-case/deterministic bound on the resulting identification error. An abstract theoretical framework for control-oriented system identification is then developed. This framework is inherently worst-case/deterministic in nature, and makes precise such notions as identification error, algorithm convergence, and algorithm optimality from a worst-case/deterministic standpoint. Finally, the abstract theoretical framework is utilized to formulate and solve two related control-oriented system identification problems for stable, linear, shift-invariant, distributed parameter plants. In each of these problems the assumed apriori information is minimal, consisting only of a lower bound on the relative stability of the plant, an upper bound on a certain gain associated with the plant, and an upper bound on the noise level. In neither case are any assumptions made concerning the structure of either the plant (i.e., dynamic order, relative order, etc.) or the noise (i.e., zero-mean, etc.). The first of these problems involves identification of a point sample of the plant frequency response from a noisy, finite, output time series obtained in response to an applied sinusoidal input with frequency corresponding to the frequency point of interest.
The authors propose a new style for three-layer channel routing. Liked the horizontal-vertical-horizontal (HVH) model, the middle layer is reserved for all vertical connections to the terminals on both sides of a chan...
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The authors propose a new style for three-layer channel routing. Liked the horizontal-vertical-horizontal (HVH) model, the middle layer is reserved for all vertical connections to the terminals on both sides of a channel. However, the authors use rectilinear wires on the top and bottom layers to complete the remaining routing. Using this model, the authors develop a channel router and show that it produces optimum solutions to all the benchmark examples tested. The algorithm runs in O(n/sup 2/w+wl) time, where n, w, and l are the number of nets, the channel width, and the sum of lengths of wires for the nets, respectively. It was implemented in C on a Sun 3/160 workstation. The experimental results are provided.< >
This paper is concerned with a Riccati type class of nonlinear differential equations called parallel D-characteristic (PD-characteristic) equations, associated with time-varying linear dynamic systems which are descr...
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This paper is concerned with a Riccati type class of nonlinear differential equations called parallel D-characteristic (PD-characteristic) equations, associated with time-varying linear dynamic systems which are described by differential equations. The solutions of PD-characteristic equations are known to possess singularities (finite-time escapes), which greatly hampers their use in the analysis and control of time-varying linear systems. A theorem is developed to show that the solutions of PD-characteristic equations are well-behaved near the singularities This result allows the classical notion of continuability of solutions to be extended through such singularities. A number of coordinate transformations are also discussed which facilitate numerical integration through such singularities.< >
The authors present a self-organizing artificial neural network (ANN) that exhibits deterministically reliable behavior to noise interference when the noise does not exceed a specified level of tolerance. The complexi...
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The authors present a self-organizing artificial neural network (ANN) that exhibits deterministically reliable behavior to noise interference when the noise does not exceed a specified level of tolerance. The complexity of the proposed ANN, called DIGNET, in terms of neuron requirements versus stored patterns, increases linearly with the number of stored patterns and their dimensionality. The self-organization of DIGNET is based on the idea of competitive generation and elimination of attraction wells in the pattern space. The same neural network can be used for both pattern recognition and classification.< >
This paper analyzes worst case identification of linear shift invariant systems with an l 1 error criterion. A characterization of optimal algorithms and intrinsic errors of identification is given. The application o...
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This paper analyzes worst case identification of linear shift invariant systems with an l 1 error criterion. A characterization of optimal algorithms and intrinsic errors of identification is given. The application of this characterization is made to identification in l 1 . The assumed apriori information on the plant are bounds on the system gain and decay rate of the impulse response of the unknown system and the experimental data is assumed to be a finite set of corrupted impulse or step response samples of the systems. Comparisons are made with the H ∞ system identification problem. Comments on the l 1 identification of small signal models of smooth nonlinear discrete time systems are made.
作者:
Igel, M.Koglin, H.‐J.Schegner, P.Dr.-lng. Michael Igel (1961). VDE
received his Dip1.-lng. degree in Electrical Engineering at the University of SaarlandGermany. in 1987 and became scientific assistant at the Lehrstuhl fur Energieversorgung of this University. After receiving his Dr.-Ing. degree in 1990. he presently works as development engineer of the department of protection systems in the power transmission and distribution division of AEG at Frankfurt a. M./Germany. (AEG Aktiengesellschaft Lyoner Str. 44-48. D-6000 Frankfurt 7 I T + 49 69/66 40 16 I 7) Prof. Dr.-lng. Hans-Jurgen Koglin (1937). VDE
holds the Lehrstuhl fur Energieversorgung of the University of SaarlandGermany since 1983. After the study of Electrical Engineering (power engineering) at the Technische Hochschule DarmstadtIGermany. he worked as a scientific assistant at the “Institut fur Elektrische Enerpieversorgung” of this Unive:rsity. After receiving his Dr.-lng. degree he tirst worked as assistant professor later on as full Professor at the Technische Hochschule Darmstadt (Department “Systemfuhrung in Energieversorgungsnetzen”). He is member i n several national and international committees. His most important research activities are: reliability evaluation computer-optimized network development planning digital network protection systems substation control system state estimation. corrective switching visibility of overhead lines. (Universitat des Saarlandes Lehrstuhl fur Energieversorgung Im Stadtwald D-6600 Saarbriicken I I T + 4 9 6 8 113 023979) Dr.-Ing. Peter Schegner (1955)
VDEis head of the development department of protection systems in .the power transmission and distribution division of AEG at Frankfurt a. M./Gerrnany. He studied Electrical Power Engineering at the Technische Hochschule Darmstadt/Genany and worked as system engineer in the field of power system control. He became a member of the scientific staff at the Universitiit des Saarlandes receiving his degree with a thesis on the earthfault distance protection. (AEG Aktiengesellschaft
Through the analysis of transient signals after the occurrence of an earthfault it is possible to evaluate fault direction and fault distance. The faulty line is simulated by using a three phase Tl‐model. The result ...
A method is given to estimate the geometry and motion of a moving body surface from image sequences. To this aim a parametric model of the surface is used, in order to reformulate the problem to one of parameter estim...
A method is given to estimate the geometry and motion of a moving body surface from image sequences. To this aim a parametric model of the surface is used, in order to reformulate the problem to one of parameter estimation. After linearization of the model standard linear estimation methods can be used to estimate the parameters. The main contribution of this paper is that a method is provided to perform the linearization without specifying the model. Therefore structure from motion estimation and nonrigid body motion estimation can be performed regardless of the model.
Task-dependent response - eg, in instrument calibration, spatial positioning and the like - is a long nursed requirement: transition to the end state very fast, albeit imprecise, followed by slower but very accurate f...
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