Constrained Performance Boosting Control for Nonlinear Systems via ADMM

Abstract

We present the Alternating Direction Method of Multipliers for Performance Boosting (ADMM-PB), an approach to design performance boosting controllers for stable or pre-stabilized nonlinear systems, while explicitly seeking input and state constraint satisfaction. Rooted on a recently proposed approach for designing neural-network controllers that guarantees closed-loop stability by design while minimizing generic cost functions, our strategy integrates it within an alternating direction method of multipliers routine to seek constraint handling without modifying the controller structure of the aforementioned seminal strategy. Our numerical results showcase the advantages of the proposed approach over a baseline penalizing constraint violation through barrier-like terms in the cost, indicating that ADMM-PB can lead to considerably lower constraint violations at the price of inducing slightly more cautious closed-loop behaviors.