Estimating Random Delays in Modbus Network Using Experiments and General Linear Regression Neural Networks with Genetic Algorithm Smoothing
This work addresses the need for accurate delay estimation in networked control systems, which is crucial for stability and performance, but it is incremental as it applies existing methods (GRNN and genetic algorithm) to a specific domain problem.
The paper tackled the problem of modeling time-varying delays in networked control systems, which are challenging due to their randomness and non-linearity, and achieved a prediction error of less than 3% using a general regression neural network with genetic algorithm smoothing.
Time-varying delays adversely affect the performance of networked control sys-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 on multiple pa-rameters such as length, contention, connected devices, protocol employed, and channel loading. Further, these multiple parameters are inherently random and de-lays vary in a non-linear fashion with respect to time. This makes estimating ran-dom delays challenging. This investigation presents a methodology to model de-lays in NCS using experiments and general regression neural network (GRNN) due to their ability to capture non-linear relationship. To compute the optimal smoothing parameter that computes the best estimates, genetic algorithm is used. The objective of the genetic algorithm is to compute the optimal smoothing pa-rameter that minimizes the mean absolute percentage error (MAPE). Our results illustrate that the resulting GRNN is able to predict the delays with less than 3% error. The proposed delay model gives a framework to design compensation schemes for NCS subjected to time-varying delays.