Reconciling Deep Learning and Control Theory: Recurrent Neural Networks for Indirect Data-Driven Control

Abstract

AbstractThis Brief aims to discuss the potential of Recurrent Neural Networks (RNNs) for indirect data-driven control. Indeed, while RNNs have long been known to be universal approximators of dynamical systems, their adoption for system identification and control has been limited by the lack of solid theoretical foundations. We here intend to summarize a novel approach to address this gap, which is structured in two contributions. First, a framework for learning safe and robust RNN models is devised, relying on the Incremental Input-to-State Stability ($$\delta $$ δ ISS) notion. Then, after a $$\delta $$ δ ISS black-box model of the plant is identified, its use for the design of model-based control laws (such as Nonlinear MPC) with closed-loop performance guarantees is illustrated. Finally, the main open problems and future research directions are outlined.