Learning stabilising policies for constrained nonlinear systems

TL;DR

This paper introduces a two-layered neural network-based control scheme for constrained nonlinear systems, ensuring stability and constraint satisfaction while improving performance through a trainable stable operator.

Contribution

It presents a novel control architecture combining a stabilising base controller with a trainable neural network operator for enhanced system performance.

Findings

Ensures global or regional p-stability of the closed-loop system.

Maintains constraint satisfaction within a robustly positive invariant set.

Demonstrates effectiveness through simulation on a pH-neutralisation benchmark.

Abstract

This work proposes a two-layered control scheme for constrained nonlinear systems represented by a class of recurrent neural networks and affected by additive disturbances. In particular, a base controller ensures global or regional closed-loop l_p-stability of the error in tracking a desired equilibrium and the satisfaction of input and output constraints within a robustly positive invariant set. An additional control contribution, derived by combining the internal model control principle with a stable operator, is introduced to improve system performance. This operator, implemented as a stable neural network, can be trained via unconstrained optimisation on a chosen performance metric, without compromising closed-loop equilibrium tracking or constraint satisfaction, even if the optimisation is stopped prematurely. In addition, we characterise the class of closed-loop stable behaviours that can be achieved with the proposed architecture. Simulation results on a pH-neutralisation benchmark demonstrate the effectiveness of the proposed approach.