A reduced regularization strategy for economic NMPC |
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| Affiliation: | 1. Centre for Process Systems Engineering, Department of Chemical Engineering, Imperial College London, UK;2. Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX, USA;3. School of Information Science and Technology, ShanghaiTech University, China;1. Department of Electrical Engineering, COMSATS University Islamabad, Islamabad, Pakistan;2. IAV Development GmbH, Gifhorn, Germany;3. Center for Advanced Studies in Telecommunication, COMSATS University Islamabad, Pakistan;4. Chair of Mechatronics, University of Rostock, Rostock, Germany |
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| Abstract: | Nonlinear Model Predictive Control (NMPC) enables the incorporation of detailed dynamic process models for nonlinear, multivariable control with constraints. This optimization-based framework also leads to on-line dynamic optimization with performance-based and so-called economic objectives. Nevertheless, economic NMPC (eNMPC) still requires careful formulation of the nonlinear programming (NLP) subproblem to guarantee stability. In this study, we derive a novel reduced regularization eNMPC approach with stability guarantees. Compared with full state regularization, the proposed strategy is less conservative and easier to implement. The resulting eNMPC framework is firstly demonstrated on a nonlinear continuous stirred-tank reactor (CSTR) example and a large-scale double distillation system example. Then the proposed strategy is applied to a challenging nonlinear CO2 capture model, where bubbling fluidized bed models comprise a solid-sorbent post-combustion carbon capture system. Our results indicate the benefits of this improved eNMPC approach over tracking to the setpoint, and better stability over eNMPC without regularization. |
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| Keywords: | Nonlinear model predictive control Economic NMPC Bubbling fluidized bed Nonlinear optimization |
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