Neural networks for control systems: a survey
Automatica (Journal of IFAC)
Neural Networks for Modelling and Control of Dynamic Systems: A Practitioner's Handbook
Neural Networks for Modelling and Control of Dynamic Systems: A Practitioner's Handbook
Neural Networks for Identification, Prediction, and Control
Neural Networks for Identification, Prediction, and Control
Editorial: Introduction to the special issue on neural network feedback control
Automatica (Journal of IFAC)
Predictive neuro-control of uncertain systems: design and use of a neuro-optimizer
Automatica (Journal of IFAC)
Optimal control of terminal processes using neural networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Diesel engine emissions prediction using parallel neural networks
ACC'09 Proceedings of the 2009 conference on American Control Conference
Neural Predictive Controller Based Diesel Injection Management System for Emission Minimisation
International Journal of Green Computing
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The paper describes a neural approach for modelling and control of a turbocharged Diesel engine. A neural model, whose structure is mainly based on some physical equations describing the engine behaviour, is built for the rotation speed and the exhaust gas opacity. The model is composed of three interconnected neural submodels, each of them constituting a nonlinear multi-input single-output error model. The structural identification and the parameter estimation from data gathered on a real engine are described. The neural direct model is then used to determine a neural controller of the engine, in a specialized training scheme minimising a multivariable criterion. Simulations show the effect of the pollution constraint weighting on a trajectory tracking of the engine speed. Neural networks, which are flexible and parsimonious nonlinear black-box models, with universal approximation capabilities, can accurately describe or control complex nonlinear systems, with little a priori theoretical knowledge. The presented work extends optimal neuro-control to the multivariable case and shows the flexibility of neural optimisers. Considering the preliminary results, it appears that neural networks can be used as embedded models for engine control, to satisfy the more and more restricting pollutant emission legislation. Particularly, they are able to model nonlinear dynamics and outperform during transients the control schemes based on static mappings.