Dynamic state feedback controller and observer design for dynamic artificial neural network models

Artificial neural networks are black-box models that can be used to model nonlinear dynamical systems. This article presents a synthesis method for full dynamic state feedback controllers and state and output observers that have guaranteed properties for systems approximated by dynamic artificial neural networks. The resulting control designs are applicable to the practical situation in which the steady-state values for the control input are not known. Dynamic artificial neural networks are written in the standard nonlinear operator form, also known in the literature as the Lure formulation. A generalized form of the Lure formulation is adopted to allow for the representation of deep l-layer networks, l >= 1. Sufficient conditions for controller synthesis and observer design are derived in the form of linear matrix inequalities, using a quadratic Lyapunov function. The synthesis method is demonstrated for the control of pH in two tanks in series and a numerical example.(c) 2022 Elsevier Ltd. All rights reserved.

Automated summary

This article presents a synthesis method for full dynamic state feedback controllers and state and output observers that have guaranteed properties for systems approximated by dynamic artificial neural networks. The method uses linear matrix inequalities and quadratic Lyapunov function to derive sufficient conditions for controller synthesis and observer design. It is applicable to the practical situation where the steady-state values for the control input are not known.