A novel Reference Governor approach for constraint management of nonlinear systems

This paper presents a new approach to enforce state, output, and control constraints in closed-loop nonlinear systems. The approach is based on the Reference Governor (RG) scheme and leverages set-theoretic methods in control, as well as data-driven offline optimization. Specifically, the approach decomposes the design of the constraint management strategy into two parts: enforcement at steady-state, and during transient. The former is achieved by using the forward and inverse steady-state characterization of the nonlinear system, which is available, for example, in many applications in the automotive industry. The latter is achieved by implementing an RG-based approach, which employs a novel Robust Output Admissible Set (ROAS), which is obtained using data from the nonlinear system. This scheme is systematically studied, and algorithms for its implementation are provided. The scheme is validated using simulations, as well as real experiments on a turbocharged gasoline engine.