Reservoir computing for system identification and predictive control with limited data

TL;DR

This paper evaluates the effectiveness of echo state networks in system identification and predictive control, demonstrating their advantages over traditional RNNs in terms of computational efficiency and prediction accuracy.

Contribution

It introduces the use of echo state networks as surrogate models for MPC, highlighting their benefits over other RNN variants in control tasks.

Findings

ESNs reduce computational complexity

ESNs enable longer prediction horizons

ESNs lower the cost of MPC objective function

Abstract

Model predictive control (MPC) is an industry standard control technique that iteratively solves an open-loop optimization problem to guide a system towards a desired state or trajectory. Consequently, an accurate forward model of system dynamics is critical for the efficacy of MPC and much recent work has been aimed at the use of neural networks to act as data-driven surrogate models to enable MPC. Perhaps the most common network architecture applied to this task is the recurrent neural network (RNN) due to its natural interpretation as a dynamical system. In this work, we assess the ability of RNN variants to both learn the dynamics of benchmark control systems and serve as surrogate models for MPC. We find that echo state networks (ESNs) have a variety of benefits over competing architectures, namely reductions in computational complexity, longer valid prediction times, and reductions in cost of the MPC objective function.