In this note, the control of standard linear, time-invariant, multi-input multi-output feedback system is investigated in the presence of actuators failures. The authors studied one of the dangerous situation in contr...
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In this note, the control of standard linear, time-invariant, multi-input multi-output feedback system is investigated in the presence of actuators failures. The authors studied one of the dangerous situation in controlsystems: the blocking of the actuators. Started from this situation the authors recommend usefully attitude in technological plants exploatation for preserving the stability and the minimal quality conditions. Stabilizing controllers are synthesized for at most one failure at a time.
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.
In this paper an alternative approach to model - based predictive control of a class of continuous - time linear systems is formulated. The approach is based on a spline approximation of given continuous - time cost f...
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In this paper an alternative approach to model - based predictive control of a class of continuous - time linear systems is formulated. The approach is based on a spline approximation of given continuous - time cost function and spline approximation of convolution type system description with structured and unstructured parts. Proposed predictive controller produces the control input signal in the form of a polynomial spline of given order. The various physical constraints on the signal can be easily set.
Incipient faults and changes in the structure of any industrial process may be detected and known by their effects: vibration and/or acoustic signals. We consider some methods for pre-processing the acoustic signal, g...
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Incipient faults and changes in the structure of any industrial process may be detected and known by their effects: vibration and/or acoustic signals. We consider some methods for pre-processing the acoustic signal, generated by a rolling mill process, for fault detection and structural classification. The pre-processing methods are based on artificial neural networks. The methods refer to: signal decomposition algorithms, a distances measure for spectral amplitude classification and neural network structures for spectrum compression. For the signal decomposition problem an adaptive neural network algorithm is proposed in which the number of inputs is adapted to the imposed error. When the training error for two successive steps is very little, then the number of inputs in network is increased. If the spectral components are zero for sufficient time, then the number of inputs is decreased. The Hausdorff distance is proposed for spectrum classification as the distance measure for the frequency domain in a pattern recognition context. It shown that the Hausdorff distance has a monotone relationship with the signal-to-noise-ratio. Finally, the possibility of decreasing the number of spectrum components as patterns is presented, by compression with neural networks. Spectral representations of the acoustic source show that signatures collected at rolling mill sensor locations can be successfully used to identify process and structural changes in the rolling mill monitoring system. The results obtained by simulation is encouraging for real-time implementation.
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.
This paper presents some real-time laboratory experiments with a multivariable predictive control law based on the Generalized Predictive control (GPC) and Constrained Receding Horizon Predictive control (CRHPC) to de...
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This paper presents some real-time laboratory experiments with a multivariable predictive control law based on the Generalized Predictive control (GPC) and Constrained Receding Horizon Predictive control (CRHPC) to demonstrate their performance under various conditions. The proposed controllers rests upon a set of MISO models of multivariable system. The control signal is generated subject to amplitude and increment constraints.
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 controlsystem dynamical behavior has been obtained.
This paper presents a model-based approach to fault detection of dynamic systems, which is robust to unmodeled dynamics. A “Quasi-ARMAX model᾿ is first proposed for describing nonlinear systems by incorporating a gro...
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This paper presents a model-based approach to fault detection of dynamic systems, which is robust to unmodeled dynamics. A “Quasi-ARMAX model᾿ is first proposed for describing nonlinear systems by incorporating a group of certain nonlinear structures into a linear ARMAX structure. The model can be used for a best linear approximation of the system, as well as for the estimation of resulting unmodeled dynamics, by a hierarchical implementation of recursive identification. Then robust fault detection is performed based on thresholding approach using Kullback discrimination information as fault detection index, in which the estimated unmodeled dynamics is incorporated.
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