Reduced Echo State Networks for Model Predictive Control of a Quadruple-Tank Process

The use of Recurrent Neural Networks (RNNs) is interesting to obtain prediction models for control. Among them, Echo State Networks (ESNs) display advantages such as simplified training and implementation. However, its high dimensionality might become an obstacle for optimization and interpretability. For that reason, this paper presents a comparative study of Model Predictive Control (MPC) implementations using Echo State Networks and their reduced models. The original ESN model and a reduced model obtained through Proper Orthogonal Decomposition (POD) are evaluated in terms of model precision, control performance and computational cost, in the task of controlling a quadruple-tank process. The results indicate that, despite a slight decrease in precision due to model reduction, the reduced model provides a significant reduction in execution time with almost the same control performance, making it a viable alternative for real-time applications.