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Time-varying delays adversely affect the performance of networked controlsys-tems (NCS) and in the worst-case can destabilize the entire system.Therefore, modelling network delays is important for designing NCS. However,modelling time-varying delays is challenging because of their dependence onmultiple pa-rameters such as length, contention, connected devices, protocolemployed, and channel loading. Further, these multiple parameters areinherently random and de-lays vary in a non-linear fashion with respect totime. This makes estimating ran-dom delays challenging. This investigationpresents a methodology to model de-lays in NCS using experiments and generalregression neural network (GRNN) due to their ability to capture non-linearrelationship. To compute the optimal smoothing parameter that computes the bestestimates, genetic algorithm is used. The objective of the genetic algorithm isto compute the optimal smoothing pa-rameter that minimizes the mean absolutepercentage error (MAPE). Our results illustrate that the resulting GRNN is ableto predict the delays with less than 3% error. The proposed delay model gives aframework to design compensation schemes for NCS subjected to time-varyingdelays.

Estimating Random Delays in Modbus Network Using Experiments and General Linear Regression Neural Networks with Genetic Algorithm Smoothing