<p> Control structure design is an essential step in control system synthesis and has big impact on achievable closed-loop performance. This thesis develops a systematic approach of selecting optimal control structures based on closed-loop dynamic performance and other criteria, such as integrity.</p> <p> The main contribution of this thesis is a rigorous mathematical formulation for control structure design problem that includes full closed-loop transient analysis with additional integrity requirement. The multi-objective framework is extendable so that different control performance objectives can be easily added. Unique process requirements and engineer inputs can be taken into account as additional constraints. The proposed formulation is a Mixed Integer Nonlinear Programming (MINLP) with complementarity constraints. The research scope is limited to linear process models and linear controller algorithms.</p> <p> The tailored solving strategy that makes this challenging problem computationally tractable is introduced in this thesis. The modified Branch and Bound algorithm takes advantage of the special problem structure by using control knowledge to generate valid lower bound efficiently. Prior knowledge can be cooperated as heuristic tuning parameters to guide the solving process so that a reasonably good solution can be found early in the solving process. The complexity study shows the solving strategy can attack design problem size up to 8x8. Considering the percentage of good structures needing evaluation will decrease with problem size even larger problems will be tractable.</p> <p> The common control structures in process industries, such as square and nonsquare Single-Input-Single-Output (SISO) loop pairing using PID controller and block-centralized structure using Model Predictive Controller (MPC), are addressed in this thesis. The usefulness of this research has been demonstrated by several case studies, include Tennessee Eastman problem. The proposed methodology finds a physically sound pairing with good performance for Tennessee Eastman problem in less than one hour, while several off-the-shelf NLP, MINLP and global solvers cannot find a solution in five days.</p> / Thesis / Doctor of Philosophy (PhD)

京都大学 / 新制・課程博士 / 博士(情報学) / 甲第24938号 / 情博第849号 / 新制||情||142(附属図書館) / 京都大学大学院情報学研究科数理工学専攻 / (主査)准教授 原口 和也, 教授 山下 信雄, 教授 阿久津 達也 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

The digital transformation of engineering design processes is essential for the aerospace industry to remain competitive in the global market. Multidisciplinary design optimization (MDO) frameworks play a crucial role in this transformation by integrating various engineering disciplines and enabling the optimization of complex spacecraft structures. Since the design team consists of multiple entities from different domains working together to build the final product, the design and analysis tools must be readily available and compatible. An integrated approach is required to handle the problem's complexity efficiently. Additionally, most aerospace structures are made from composite panels. It is challenging to optimize such panels as they require the satisfaction of constraints where the design ply thicknesses and orientations can only take discrete values prescribed by the manufacturers. Heuristics such as particle swarm or genetic algorithms are inefficient because they provide sub-optimal solutions when the number of design variables is large. They also are computationally expensive in handling the combinatorial nature of the problem. To overcome these challenges, this work proposes a two-fold solution that integrates multiple disciplines and efficiently optimizes composite spacecraft structures by building a rapid design framework. The proposed model-based design framework for spacecraft structures integrates commercially available software from Siemens packages such as NX and HEEDS and open-source Python libraries. The framework can handle multiple objectives, constraint non-linearities, and discrete design variables efficiently using a combination of black-box global optimization algorithms and Mixed Integer Programming (MIP)-based optimization techniques developed in this work. Lamination parameters and MIP are adopted to optimize composite panels efficiently. The framework integrates structural, thermal and acoustic analysis to optimize the spacecraft's overall performance while satisfying multiple design constraints. Its capabilities are demonstrated in optimizing a small spacecraft structure for required structural performance under various static and dynamic loading conditions when the spacecraft is inside the launch vehicle or operating in orbit. / Doctor of Philosophy / The design of new spacecraft takes several years and requires significant resources. The primary design objective is to minimize spacecraft mass/cost while satisfying the mission requirements. This is done by altering the structure's geometric and material properties. Most spacecraft panels are made from composite materials where the orientations of fiber paths and the thickness of the panel determine its strength and stiffness. Finding the best values for these parameters cannot be done efficiently using existing optimization algorithms, as several combinations of orientations can give a similar performance which can be subpar. In this dissertation, mathematical programming is adopted for fast evaluation of optimum panel properties, thereby saving a significant amount of resources compared to conventional techniques. Moreover, the requirements that govern the design process are handled one at a time in an organization. This leads to discrepancies in the various teams' designs that satisfy all requirements. A framework is built to integrate all requirements to account for their conflicting nature and quickly give the best possible spacecraft structural design configuration.

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