Neural Modeling and Control of Diesel Engine with Pollution Constraints

TL;DR

This paper presents a neural network-based approach for modeling and controlling a turbocharged Diesel engine to meet pollution constraints, demonstrating improved transient control and pollution management capabilities.

Contribution

It introduces a neural modeling and control scheme for a Diesel engine that incorporates physical equations and handles pollution constraints, extending neuro-control to multivariable systems.

Findings

Neural models accurately describe engine dynamics.

Neural control improves transient response under pollution constraints.

Simulation results show effective pollution and speed regulation.

Abstract

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.