Implementation of Predictors Based on Evolutionary Algorithms Using Regression Neural Networks—Application to Receding Horizon Control

Embedding an evolutionary algorithm (EA) into control structures offers an effective solution for specific control problems. Often, it predicts the best control values using a process model (PM). The primary limitation is its high computational time. Our work addresses optimal control problems (OCPs) with a final cost, using receding horizon control (RHC) with an EA as a predictor. This work is a continuation of a previous article, in which the EA predictor was replaced with a multilinear regression-based predictor. Our objective is to propose a predictor based on regression neural networks (RNNs) that emulates the behavior of the (EA, PM) couple. A number of closed-loop simulations using the existing EA controller produce sequences of optimal control values and corresponding state values, which are stored in a data structure. Datasets for each sampling period are derived from these data and are used to train RNN objects employing a unique RNN model. The model, which is an “optimizable” RNN plus the list of hyperparameters preset before optimization, is determined after a thorough analysis of possible candidates using a MATLAB R2025b application. The presented method of constructing an RNN predictor is the main contribution. Algorithms for (a) constructing the sequence of RNN objects and (b) simulating the closed loop are also proposed. A case study illustrates our method. The RNN predictor successfully emulated the (EA, PM) couple: (a) the control-loop dynamics were nearly identical; (b) the performance indices were essentially the same; and (c) the execution time of the controller significantly decreased from 38 to 0.054 s, demonstrating that RHC can be applied more broadly.