Thesis
Robust Steady-State-Aware Model Predictive Control for Systems with Limited Computational Resources and External Disturbances
Washington State University
Master of Science (MS), Washington State University
12/2024
DOI:
https://doi.org/10.7273/000007243
Abstract
Model Predictive Control (MPC) is a powerful strategy for managing complex dynamical systems, but its reliance on online optimization creates challenges in systems with limited computational resources. A common approach to mitigate this issue is to shorten the prediction horizon, which can enlarge the region of attraction but often increases computational load. Recently, steady-state-aware MPC has emerged as a promising alternative, ensuring output tracking and convergence to a desired steady-state configuration while maintaining constraint satisfaction without incurring additional computational demands. However, this method does not adequately address external disturbances, limiting its applicability. This study enhances the robustness of steady-state-aware MPC against external disturbances by adopting a tube-based design framework. This approach decouples the optimization of the nominal trajectory from robust control synthesis, requiring no extra online computational resources. Theoretical guarantees for the proposed methodology are provided, and its effectiveness is assessed through simulations and experimental studies on a Parrot Bebop 2 drone. The results demonstrate that the enhanced control scheme maintains robust performance in real-world scenarios, highlighting its potential for applications in dynamic environments.
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Details
- Title
- Robust Steady-State-Aware Model Predictive Control for Systems with Limited Computational Resources and External Disturbances
- Creators
- Hassan Jafari Ozoumchelooei
- Contributors
- Mehdi Hosseinzadeh (Chair)Ming Luo (Committee Member)John Swensen (Committee Member)
- Awarding Institution
- Washington State University
- Academic Unit
- Voiland College of Engineering and Architecture
- Theses and Dissertations
- Master of Science (MS), Washington State University
- Publisher
- Washington State University
- Number of pages
- 42
- Identifiers
- 99901195539801842
- Language
- English
- Resource Type
- Thesis