Reservoir computing for system identification and predictive control with limited data
This work addresses the need for efficient data-driven models in control systems, but it is incremental as it compares existing architectures rather than introducing a new method.
The paper tackled the problem of using neural networks as surrogate models for model predictive control (MPC) by comparing recurrent neural network variants, finding that echo state networks (ESNs) offer benefits such as reduced computational complexity, longer valid prediction times, and lower MPC objective function costs.
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.