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

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.