This paper deals with a mixed continuous-batch process, a pilot plant of our Lab, where both continuous decisions and scheduling take place. The interest of this contribution comes not only from the fact that it is a ...
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Many processes, even being of a continuous nature, involve in its operation signals or rules different from the classical continuous variables represented by real variables and modelled by DAE. In practice they includ...
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This paper describes the application of a nonlinear model-based control strategy in a real challenging process. A predictive controller based on a nonlinear mode! derived from physical relationships, mainly heat and m...
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Many processes, even being of a continuous nature, involve in its operation signals or rules different from the classical continuous variables represented by real variables and modelled by DAE. In practice they includ...
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Many processes, even being of a continuous nature, involve in its operation signals or rules different from the classical continuous variables represented by real variables and modelled by DAE. In practice they include on/off valves or other binary actuators, are subjected to logical operational rules, or are mixed with sequential operations. As a result, classical control does not fit very well with the overall operation of the plant. In this paper we consider the problem of hybrid control from a predictive control perspective, showing in a practical non trivial example with changing process structure, how the problem can be stated and solved.
This paper describes the application of a nonlinear model-based control strategy in a real challenging process. A predictive controller based on a non linear model derived from physical relationships, mainly heat and ...
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This paper describes the application of a nonlinear model-based control strategy in a real challenging process. A predictive controller based on a non linear model derived from physical relationships, mainly heat and mass balances, has been developed and commissioned in the Inner Triplet Heat Exchanger Unit (IT -HXTU) prototype of the LHC particle accelerator being built at CERN, operating at a temperature of about 1.9 K. The development includes a state estimator with a receding horizon estimation procedure to improve the regulator predictions.
Distributed controlsystems (DCS) are based on assignments of control loops and supervision points to different units. In a first approach, these assignments are made statically and following given distribution criter...
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This paper describes a neural predictive control toolbox developed in Matlab/Simulink environment. The application permits all phases of the system design: simulation of the plant by means of any Simulink model, loadi...
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Modelling and simulation are inseparable procedures that include the complex activities associated with the construction of models representing real processes, and experimentation with the models to obtain data on the...
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Distributed controlsystems (DCS) are based on assignments of control loops and supervision points to different units. In a first approach, these assignments are made statically and following given distribution criter...
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Distributed controlsystems (DCS) are based on assignments of control loops and supervision points to different units. In a first approach, these assignments are made statically and following given distribution criteria. This paper presents a proposal of communications system over a DCS which allows the interchanging of assignments between the control units. The objective is to provide a frame for dynamical load balancing in the system with reactivity against meaningful variables in the system such as changes in the process, traffic demands or computing load in every processor.
This paper describes a neural predictive control toolbox developed in Matlab/Simulink environment. The application permits all phases of the system design: simulation of the plant by means of any Simulink model, loadi...
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This paper describes a neural predictive control toolbox developed in Matlab/Simulink environment. The application permits all phases of the system design: simulation of the plant by means of any Simulink model, loading of input/output data, definition of the neural network architecture, training, and, finally, application of the predictive control strategy based on the neural network model.
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