A robust adaptive model predictive control framework for nonlinear uncertain systems

TL;DR

This paper introduces a robust adaptive model predictive control framework for nonlinear uncertain systems that ensures stability and constraint satisfaction while reducing conservatism through set-membership estimation and tube-based methods.

Contribution

It develops a novel tube-based RAMPC framework with monotonicity properties, providing constructive design conditions and demonstrating that existing methods are special cases.

Findings

Ensures robust recursive feasibility and constraint satisfaction.

Provides finite-gain $\\mathcal{L}_2$ stability with disturbance.

Shows improved performance over existing approaches.

Abstract

In this paper, we present a tube-based framework for robust adaptive model predictive control (RAMPC) for nonlinear systems subject to parametric uncertainty and additive disturbances. Set-membership estimation is used to provide accurate bounds on the parametric uncertainty, which are employed for the construction of the tube in a robust MPC scheme. The resulting RAMPC framework ensures robust recursive feasibility and robust constraint satisfaction, while allowing for less conservative operation compared to robust MPC schemes without model/parameter adaptation. Furthermore, by using an additional mean-squared point estimate in the objective function the framework ensures finite-gain $\mathcal{L}_2$ stability w.r.t. additive disturbances. As a first contribution we derive suitable monotonicity and non-increasing properties on general parameter estimation algorithms and tube/set based RAMPC schemes that ensure robust recursive feasibility and robust constraint satisfaction under recursive model updates. Then, as the main contribution of this paper, we provide similar conditions for a tube based formulation that is parametrized using an incremental Lyapunov function, a scalar contraction rate and a function bounding the uncertainty. With this result, we can provide simple constructive designs for different RAMPC schemes with varying computational complexity and conservatism. As a corollary, we can demonstrate that state of the art formulations for nonlinear RAMPC are a special case of the proposed framework. We provide a numerical example that demonstrates the flexibility of the proposed framework and showcase improvements compared to state of the art approaches.