Recently, a comprehensive dynamic mathematical model named Copernicus has been established to discover the mechanism of the vascular bubble formation and growth during and after decompression from a dive. The model us...
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Recently, a comprehensive dynamic mathematical model named Copernicus has been established to discover the mechanism of the vascular bubble formation and growth during and after decompression from a dive. The model uses Venous Gas Emboli (VGE) as a measurement and connects it to the risk of severe Decompression Sickness (DCS). Being validated by a series of diving tests, Copernicus model is believed to be suitable and efficient to predict DCS hence generate optimal decompression profiles for the divers. This paper is based on the Copernicus model and presents a nonlinear model predictive control approach, where multi-parametric nonlinear programming is used to construct an explicit solution for the ease of implementation on a typical low-cost diving computer.
nonlinear model predictive control (NMPC) algorithms are based on various nonlinear models. A number of on-line optimization approaches for output-feedback NMPC based on various black-box models can be found in the li...
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nonlinear model predictive control (NMPC) algorithms are based on various nonlinear models. A number of on-line optimization approaches for output-feedback NMPC based on various black-box models can be found in the literature. However. NMPC involving on-line optimization is computationally very demanding. On the other hand, an explicit solution to the NMPC problem would allow efficient online computations as well as verifiability of the implementation. This paper applies an approximate multi-parametric nonlinear programming approach to explicitly solve output-feedback NMPC problems for constrained nonlinear systems described by black-box models. In particular, neural network models are used and the optimal regulation problem is considered. A dual-mode control strategy is employed in order to achieve an offset-free closed-loop response in the presence of bounded disturbances and/or model errors. The approach is applied to design an explicit NMPC for regulation of a pH maintaining system. The verification of the NMPC controller performance is based on simulation experiments. (C) 2010 Elsevier Ltd. All rights reserved.
In this work we present a novel multi-parametric nonlinear programming (mp-NLP) algorithm for explicit multi-parametricnonlinear model predictive control (mp-NMPC). The algorithm is based on (i) local sensitivity ana...
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In this work we present a novel multi-parametric nonlinear programming (mp-NLP) algorithm for explicit multi-parametricnonlinear model predictive control (mp-NMPC). The algorithm is based on (i) local sensitivity analysis of nonlinear programs (NLP), and (ii) an exploration procedure which makes use of successive linearizations of the dynamic system and the nonlinear constraints. The algorithm is illustrated with an example problem drawn from the open literature.
Energy production is one of the largest sources of air pollution. A feasible method to reduce the harmful flue gases emissions and to increase the efficiency is to improve the control strategies of the existing thermo...
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Energy production is one of the largest sources of air pollution. A feasible method to reduce the harmful flue gases emissions and to increase the efficiency is to improve the control strategies of the existing thermoelectric power plants. This makes the nonlinear Model Predictive Control (NMPC) method very suitable for achieving an efficient combustion control. Recently, an explicit approximate approach for stochastic NMPC based on a Gaussian process model was proposed. The benefits of an explicit solution, in addition to the efficient on-line computations, include also verifiability of the implementation, which is an essential issue in safety-critical applications. This paper considers the application of an explicit approximate approach for stochastic NMPC to the design of an explicit reference tracking NMPC controller for a combustion plant based on its Gaussian process model. The controller brings the air factor (respectively the concentration of oxygen in the flue gases) on its optimal value with every change of the load factor and thus an optimal operation of the combustion plant is achieved. (c) 2008 Elsevier Ltd. All rights reserved.
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