摘要：AbstractThe steady-state performance of a parametrically or structurally uncertain system can be optimized using iterative real-time optimization methods such asmodifier adaptation(MA). Here, we extend a recently proposed MA scheme in two important and novel directions. First, weaccelerateits convergence, i.e., we reduce the number of potentially time-consuming and suboptimal transitions to intermediate steady states by appropriate filtering. Second, we propose anadaptationstrategy to reduce conservatism related to the unknown curvature of the system’s steady-state performance curve. Moreover, we combine these two innovations and demonstrate their benefits on two numerical examples.
