Thesis
Nonlinear model predictive control of processes with incomplete state measurements
Master of Science (M.S.), Drexel University
Aug 2018
DOI:
https://doi.org/10.17918/aysb-hs98
Abstract
This dissertation consists of two main parts. The first part provides a comprehensive review of the tuning guidelines that have been introduced for model predictive controllers (MPCs) since 2010. The second part deals with nonlinear control of single-input single-output processes with manipulated-input-saturation constraints, incomplete state measurements, and an unstable steady-state operating point. A nonlinear controller is proposed. It includes an input-output linearizing state feedback controller, which is also an analytical solution to a shortest prediction horizon, continuous-time MPC optimization problem. It uses a closed-loop nonlinear reduced-order state observer to estimate unmeasured state variables. As it handles input constraints optimally, it exhibits no integrator windup. Given the closed-loop stability of the control system, it guarantees zero steady-state error (offset) in the presence of constant process disturbances and process-model mismatch. Its application and performance are illustrated by applying the controller to two nonlinear chemical process examples, a chemical reactor and a bioreactor, via numerical simulations.
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Details
- Title
- Nonlinear model predictive control of processes with incomplete state measurements
- Creators
- Mohammed Alhajeri - DU
- Contributors
- Masoud Soroush (Advisor) - Drexel University (1970-)
- Awarding Institution
- Drexel University
- Degree Awarded
- Master of Science (M.S.)
- Publisher
- Drexel University; Philadelphia, Pennsylvania
- Number of pages
- ix, 85 pages
- Resource Type
- Thesis
- Language
- English
- Academic Unit
- Chemical (and Biological) Engineering (1970-2026); College of Engineering (1970-2026); Drexel University
- Other Identifier
- 10998; 991014632251304721