A Machine Learning Algorithm That Experiences the Evolutionary Algorithm’s Predictions—An Application to Optimal Control

Abstract

Using metaheuristics such as the Evolutionary Algorithm (EA) within control structures is a realistic approach for certain optimal control problems. They often predict the optimal control values over a prediction horizon using a process model (PM). The computational effort sometimes causes the execution time to exceed the sampling period. Our work addresses a new issue: whether a machine learning (ML) algorithm could “learn” the optimal behaviour of the couple (EA and PM). A positive answer is given by proposing datasets apprehending this couple’s optimal behaviour and appropriate ML models. Following a design procedure, a number of closed-loop simulations will provide the sequences of optimal control and state values, which are collected and aggregated in a data structure. For each sampling period, datasets are extracted from the aggregated data. The ML algorithm experiencing these datasets will produce a set of regression functions. Replacing the EA predictor with the ML model, new simulations are carried out, proving that the state evolution is almost identical. The execution time decreases drastically because the PM’s numerical integrations are totally avoided. The performance index equals the best-known value. In different case studies, the ML models succeeded in capturing the optimal behaviour of the couple (EA and PM) and yielded efficient controllers.

Machine Learning Algorithms That Emulate Controllers Based on Particle Swarm Optimization—An Application to a Photobioreactor for Algal Growth

“…This work goes in the same direction but involves a new technique: using machine learning (ML) to emulate predictors based on MAs. Recently, we have proposed linear regression (LR) predictors that are "equivalent" in a certain sense to predictors based on Evolutionary Algorithms (Eas) [19]. This paper deals with OCPs having final costs and solutions involving PSO predictions.…”

“…To continue the work presented in [19], we shall consider the equivalence mentioned above and implement Regression Neural Network (RNN) predictors besides the LR ones.…”