A Neural Network-based Contraction Control with Online Parameter Identification for Uncertain Nonlinear Systems

Abstract

Motivated by the trend of flexible manufacturing in the process control industry and the uncertain nature of chemical process models, this article aims to achieve offset-free tracking for a family of uncertain nonlinear systems. The proposed control approach employs two main modules: a neural network embedded contraction-based controller to ensure convergence to time-varying references; and an online identification module coupled with a reference generator to provide convergency of the modelled parameters to that of the physical system. The first step in the proposed approach is to provide a guaranteed contraction condition for nonlinear systems, subject to time-varying parametric uncertainty, that are driven by neural network embedded controllers and modelled parameter estimates. The second step is to provide unknown system parameter identification online. By ensuring that uncertain parameter estimates converge to the corresponding physical values, offset-free tracking can be achieved. An illustrative example is included to demonstrate the overall approach.