Diesel engine indicated torque estimation based on artificial neural networks

This paper presents an artificial neural networks approach to estimate the indicated torque of a singlecylinder diesel engine from crank shaft angular position and velocity measurements. Since these variables can be measured using low-cost sensors, the estimator may be useful in the implementation of the control or diagnostics strategies that require cylinder indicated torque, a variables that are not easily measured and need expensive sensors. The approach is to design indicated torque estimators using feedback and an artificial neural networks model as feedforward. Such an approach can offer the advantage of being amenable to real-time implementation. The estimated results of the engine indicated torque are presented, which compared with experimental data indicate a good agreement.