Abstract
A new robust nonlinear model predictive control (RNMPC) heuristic is proposed, specifically developed to be i) easy to implement, ii) robust against constraint violations and iii) fast to solve. Our proposed heuristic samples from the disturbance distributions and performs n-steps-ahead Monte Carlo (MC) simulations to calculate the back-off where n is a small number, typically one. We show two implementations of our heuristic. The Automatic Back-off Calculation NMPC (ABC-NMPC) uses MC simulations on a process model to calculate the back-off, and explicitly states the back-off in a standard NMPC problem. Our second implementation, the MC Single-Stage NMPC (MCSS-NMPC), directly includes the disturbance distribution in the optimization problem, making it an implicit back-off method. Our methods are robust against constraint violation in the next time-step, under certain assumptions. In the presented case-study, our proposed RNMPC methods outperform the popular multi-stage NMPC in terms of robustness and/or computational cost. We suggest several further modifications to our RNMPC methods to improve performance, at the cost of increased complexity.
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 callSimilar Papers
Disclaimer: 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.
