Advances in sensitivity-based nonlinear model predictive control and dynamic real-time optimization

作者：Biegler L T^*; Yang X; Fischer G A G

来源：Journal of Process Control, 2015, 30: 104-116.

DOI：10.1016/j.jprocont.2015.02.001

• Nonlinear programming
• Nonlinear model predictive control
• Dynamic real-time optimization

Recent results in the development of efficient large-scale nonlinear programming (NLP) algorithms have led to fast, on-line realizations of optimization-based methods for nonlinear model predictive control (NMPC) and dynamic real-time optimization (D-RTO), with predictive nonlinear dynamic (e.g., first principle) models. For NMPC, optimization-based controllers are developed that lead to well-understood stability and robustness properties, even for large, complex plant models. The realization of NMPC requires the application of a fast NLP solver for time-critical, on-line optimization, as well as efficient NLP sensitivity tools that require 2-3 orders of magnitude less computation than the NLP solution. This leads to advanced step NMPC (asNMPC), which essentially eliminates computational delay. We also extend these capabilities to dynamic real-time optimization (D-RTO) with more general stage costs that are economically based. This overview also extends input to state stability (ISS) properties for asNMPC to handle active set changes, and also for D-RTO through convex regularizations. Two large scale distillation case studies, based on nonlinear first principle models, are presented that demonstrate the effectiveness of these approaches.

• 出版日期2015-6