Adaptive Output-Feedback Model Predictive Control of Hammerstein Systems with Unknown Linear Dynamics

TL;DR

This paper develops an adaptive output-feedback model predictive control method for Hammerstein systems with unknown linear dynamics, utilizing recursive least squares and quadratic programming to achieve output stabilization.

Contribution

It introduces a novel MPC approach that handles unknown linear dynamics and known nonlinearities in Hammerstein systems through recursive identification and iterative optimization.

Findings

Successfully stabilizes output of systems with unknown dynamics

Demonstrates effectiveness under input saturation and deadzone nonlinearities

Provides a framework for adaptive control in nonlinear systems

Abstract

This paper considers model predictive control of Hammerstein systems, where the linear dynamics are a priori unknown and the input nonlinearity is known. Predictive cost adaptive control (PCAC) is applied to this system using recursive least squares for online, closed-loop system identification with optimization over a receding horizon performed by quadratic programming (QP). In order to account for the input nonlinearity, the input matrix is defined to be control dependent, and the optimization is performed iteratively. This technique is applied to output stabilization of a chain of integrators with unknown dynamics under control saturation and deadzone input nonlinearity.