A Globally Convergent Composite-Step Trust-Region Framework for
Real-Time Optimization with Plant-Model Mismatch
Abstract
Inaccurate models limit the performance of model-based real-time
optimization (RTO) and even cause system instability. Therefore, a RTO
framework that can guarantee global convergence with the presence of
plant-model mismatch is desired. In this regard, the trust-region
framework is simple to implement and guarantees globally convergent for
unconstrained problems. However, it remains to be seen if the
trust-region strategy can handle inequality constraints directly with
the common model adaptation method. This paper addresses this issue and
proposes a novel composite-step trust-region framework that guarantees
global convergence for constrained RTO problems. The trial step is
decomposed into a normal step that improves feasibility and a tangential
step that reduces the cost function. In each iteration, the model
optimization problem with relaxed constraints is solved. The proof of
the global convergence property under structural plant-model mismatch is
given.