Simultaneous state estimation and parameter tracking of time varying input-output ARMAX model with known noise parameters was described recently. Optimal control strategies of such model are developed in this contribu...
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(纸本)9783952426906
Simultaneous state estimation and parameter tracking of time varying input-output ARMAX model with known noise parameters was described recently. Optimal control strategies of such model are developed in this contribution. Certainty equivalent and cautious LQ strategies (Linear system and Quadratic criterion) are obtained. Certainty equivalent strategies operate with state and parameter means only and so neglect the uncertainty while cautious strategies take parameter and state estimation uncertainty into the consideration.
Object-oriented database systems aim at meeting the data modelling, performance, cooperative design and version management requirements of current advanced applications, such as CAD (computer-aided design), CAM (compu...
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Object-oriented database systems aim at meeting the data modelling, performance, cooperative design and version management requirements of current advanced applications, such as CAD (computer-aided design), CAM (computer-aided manufacturing), CASE (computer-aided software engineering), CIM (computer integrated manufacturing), hypermedia and expert systems. This paper presents the ALEX Object Manager, which is a part of the ALEX object-oriented database management system that is being developed based on the ODMG-93 standard. The system decomposition and process layout are presented, some implementation problems are discussed and the current status of the system is reported.
The aim of the work is to present the state-of-the-art in this filed of adaptive control. Some development trends based especially on the general stability theory are analyzed. The characteristics of basic model refer...
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The aim of the work is to present the state-of-the-art in this filed of adaptive control. Some development trends based especially on the general stability theory are analyzed. The characteristics of basic model reference adaptive control (MRAC) structures and algorithms are given in order to provide an insight into the development of the theory and to indicate the possibilities of their practical application. As the paper makes only a survey of the subject area, the stability proofs of the presented algorithms are omitted. The plant to be controlled is assumed to be described by a linear model.
In this paper an application of the H ∞ -optimal position control of a hydraulic drive is presented. The treatment of model uncertainties in the field of robust control is demonstrated and an analysis for the two mos...
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In this paper an application of the H ∞ -optimal position control of a hydraulic drive is presented. The treatment of model uncertainties in the field of robust control is demonstrated and an analysis for the two most important kinds of uncertainties is presented. Main goal is to portray the relation between the weighting functions and the performance and robustness of the controlled system. On the basis of this detailed analysis, a set of simple tuning rules for the appropriate selection of the weighting functions is derived. A software package for the analysis of the plant and the selection of the weighting functions has been developed and successfully applied to a hydraulic drive.
The paper brings an overview of methods of modeling, control and design of distributed parameter systems with lumped input and distributed output. Selected results will be demonstrated using high-performance simulatio...
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The paper brings an overview of methods of modeling, control and design of distributed parameter systems with lumped input and distributed output. Selected results will be demonstrated using high-performance simulation programming tools offered by environment of MATLAB.
In the paper a predictive control scheme of distributed parameter systems is outlined. The scheme of predictive control is based on spatial and time decomposition of system dynamics. Distributed parameter system is co...
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In the paper a predictive control scheme of distributed parameter systems is outlined. The scheme of predictive control is based on spatial and time decomposition of system dynamics. Distributed parameter system is considered as a system with lumped input and distributed output. GPC and LQG synthesis are designed for this system in the paper. Result algorithms are verified on physical model of distributed parameter system.
Model reference adaptive control (MRAC) has been developed in many modifications. The stability proofs have been given under the assumption that the plant model is linear. However, MRAC approaches give often convergen...
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Model reference adaptive control (MRAC) has been developed in many modifications. The stability proofs have been given under the assumption that the plant model is linear. However, MRAC approaches give often convergent solutions even for nonlinear systems. The aim of this paper is to show that for nonlinear systems with the model in canonical form, the standard MRAC with state feedback structure can be used to obtain the perfect model matching. The stability proof is derived using the general Laypunov stability theory. At the end of paper, an example of adaptive control of third order nonlinear system is presented, where a considerable improvement of control system dynamical behavior has been obtained.
Deals with identification of nonlinear processes and model-based fault detection/isolation (FDI). The applicability of the proposed methods is illustrated on a three-tank laboratory setup. The process identification i...
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Deals with identification of nonlinear processes and model-based fault detection/isolation (FDI). The applicability of the proposed methods is illustrated on a three-tank laboratory setup. The process identification is based on the local linear model tree (LOLIMOT) algorithm and leads to local linear models. The parameters of the local models are used for generation of structured residual equations, similar to the well-known parity space approach. This enables detection and isolation of five different sensor faults of the three-tank process, continously over all ranges of operation.
This paper deals with nonlinear predictive control based on higher order Takagi-Sugeno fuzzy systems which can also be interpreted as generalized radial basis function networks. We investigate how the fuzzy models can...
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This paper deals with nonlinear predictive control based on higher order Takagi-Sugeno fuzzy systems which can also be interpreted as generalized radial basis function networks. We investigate how the fuzzy models can be linked to a special type of model based predictive control algorithm, namely the dynamic matrix control (DMC). Previously, purely linear step response models were used for long-range prediction. Here, the method is extended to nonlinear processes. Therefore, various step responses for different operating points are extracted from the fuzzy model. For performance evaluation, a heat exchanger is identified by means of the local linear model tree algorithm and controlled by the modified DMC.
In this paper is presented a new approach to robust non-linear control design, which can guarantee a prescribed decay rate of exponential stability for known system uncertainties. The proposed approach doesn’t employ...
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In this paper is presented a new approach to robust non-linear control design, which can guarantee a prescribed decay rate of exponential stability for known system uncertainties. The proposed approach doesn’t employ matching conditions. The non-linear power system with deterministic uncertainties is chosen as demonstration example.
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