Dual adaptive MPC using an exact set-membership reformulation

TL;DR

This paper introduces a dual adaptive MPC method that employs an exact set-membership reformulation, enhancing robustness and performance in control tasks through a novel optimization approach.

Contribution

It presents a new dual adaptive MPC framework using strong duality for exact set-membership reformulation, improving robustness and exploration capabilities.

Findings

Guarantees robust constraint satisfaction

Ensures recursive feasibility

Demonstrates performance improvement in simulations

Abstract

Adaptive model predictive control (MPC) methods using set-membership identification to reduce parameter uncertainty are considered in this work. Strong duality is used to reformulate the set-membership equations exactly within the MPC optimization. A predicted worst-case cost is then used to enable performance-oriented exploration. The proposed approach guarantees robust constraint satisfaction and recursive feasibility. It is shown that method can be implemented using homothetic tube and flexible tube parameterizations of state tubes, and a simulation study demonstrates performance improvement over state-of-the-art controllers.