Abstract
A novel state-space-based multivariable constrained nonlinear model predictive control (NMPC) strategy is described. The NMPC strategy uses enhanced Decoupled A-B Net (DABNet) based dynamic reduced models (D-RMs) as core predictive models. The control objective cost-function and its analytical derivatives, explicitly utilize the decoupled nature of the DABNet model, which provides computational advantages over automatic differentiation (AD) methodologies. The output-node parallelization strategy described in this paper is demonstrated to provide comparable computational advantages as that found in linear multi-rate MPC algorithms, while furnishing superior disturbance rejection and setpoint tracking characteristics of the NMPC. The approach has been presented and compared on a multi-time-scale solid-sorbent-based bubbling fluidized bed (BFB) carbon capture process.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
