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Economic Model Predictive Control: Model Approximation and Robustness Analysis
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- Author / Creator
- Huang, Zhiyinan
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Advanced process control has been considered as a promising tool for addressing various control objectives for complex industrial applications, including ensuring safe operation, reducing operational cost, improving process efficiency, achieving more environmentally friendly practices, etc. Advanced process control strategies often suffer from two major challenges. First, the computational costs for advanced control applications are often very significant. Second, the control performance depends highly on the quality of the embedded model and can be significantly affected by the presence of plant-model-mismatch.
Based on the two major challenges, this thesis can be partitioned into two parts.
The first part focuses on the investigation of advanced-control-oriented model approximation methods. Specifically, Chapter~\ref{1stWork} presents a study on three types of representative model approximation methods applied to economic model predictive control (EMPC), including model reduction based on available first-principle models (e.g., proper orthogonal decomposition), system identification based on input-output data (e.g., subspace identification) that results in an explicitly expressed mathematical model, and neural networks based on input-output data. Two processes that are very different in dynamic nature and complexity were selected as benchmark processes for computational complexity and economic performance comparison, namely an alkylation process and a wastewater treatment plant (WWTP). The strengths and drawbacks of each method are summarized according to the simulation results. In Chapter~\ref{2ndWork}, the observation obtained in Chapter~\ref{1stWork} is extended and a two-layer neural network (NN) framework to approximate the dynamics of the agro-hydrological system is proposed. The model is employed by a EMPC with zone-tracking (ZMPC) that aims to keep the root zone soil moisture in the target zone while minimizing the total amount of irrigation.
The performance of the proposed approximation model framework is shown to be better compared to a benchmark long-short-term-memory (LSTM) model for both open-loop and closed-loop applications. Significant computational cost reduction of the ZMPC is achieved with the proposed framework.
The second part of the thesis focuses on the development of generalized ZMPC with guaranteed robustness. In Chapter~\ref{3rdWork}, we propose a generalized robust ZMPC that has guaranteed convergence into the target zone in the presence of bounded disturbance. The proposed approach achieves this by modifying the actual target zone such that the effect of disturbances is rejected. A control invariant set (CIS) inside the modified target zone is used as the terminal set, which ensures the closed-loop stability of the proposed controller. Detailed closed-loop stability analysis is presented. Simulation studies based on a continuous stirred tank reactor (CSTR) are performed to validate the effectiveness of the proposed ZMPC. In Chapter~\ref{4thWork}, the robust ZMPC proposed in Chapter~\ref{3rdWork} is improved and applied to an amine-based post-combustion carbon capture plant and is shown to have better performance and enhanced robustness compared to a benchmark ZMPC design. -
- Subjects / Keywords
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- Graduation date
- Spring 2024
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- Type of Item
- Thesis
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- Degree
- Doctor of Philosophy
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- License
- This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.