Optimal control of autonomous systems is a fundamental and challenging problem, especially when many stringent safety constraints and tight control limitations are involved such that solutions are hard to determine. It has been shown that optimizing quadratic costs while stabilizing affine control systems to desired (sets of) states subject to state and control constraints can be reduced to a sequence of Quadratic Programs (QPs) by using Control Barrier Functions (CBFs) and Control Lyapunov Functions (CLFs). Although computationally efficient, this method is limited by several factors which are addressed in this dissertation. The first contribution of this dissertation is to extend CBFs to high order CBFs (HOCBFs) that can accommodate arbitrary relative degree systems and constraints. The satisfaction of Lyapunov-like conditions in the HOCBF method implies the forward invariance of the intersection of a sequence of sets, which can then guarantee the satisfaction of the original safety constraint. Second, under tight control bounds, this dissertation proposes an analytical method to find sufficient conditions that guarantee the QP feasibility. The sufficient conditions are captured by a single state constraint that is enforced by a CBF and then added to the QP. Third, for complex safety constraints and systems in which it is hard to find sufficient conditions for feasibility, machine learning techniques are employed to learn the definitions of HOCBFs or feasibility constraints. Fourth, when time-varying control bounds and noisy dynamics are involved, adaptive CBFs (AdaCBFs) are proposed, which can guarantee the feasibility of the QPs if the original optimization problem itself is feasible. Finally, for systems with unknown dynamics, adaptive affine control dynamics are proposed to approximate the real unmodelled system dynamics which are updated based on the error states obtained by real-time sensor measurements. A set of events required to trigger a solution of the QP in order to guarantee safety is defined, and a condition that guarantees the satisfaction of the HOCBF constraint between events is derived. In order to address the myopic nature of the CBF method, a real-time control framework that combines optimal trajectories and the computationally efficient HOCBF method providing safety guarantees is also proposed. The HOCBFs and CLFs are used to account for constraints with arbitrary relative degrees and to track the optimal state, respectively. Eventually, an optimal control problem based on the proposed framework is always reduced to a sequence of QPs regardless of the formulation of the original cost function. Another contribution of the dissertation is to apply the above proposed methods to solve complex safety-critical optimal control problems, such as those arising in rule-based autonomous driving and optimal traffic merging control for Connected and Automated Vehicles (CAVs).

京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第21916号 / 情博第699号 / 新制||情||120(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 加嶋 健司, 教授 太田 快人, 教授 山下 信雄 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第21918号 / 情博第701号 / 新制||情||120(附属図書館) / 京都大学大学院情報学研究科システム科学専攻 / (主査)准教授 西原 修, 教授 大塚 敏之, 教授 加納 学 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

付記する学位プログラム名: デザイン学大学院連携プログラム / 京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第21761号 / 工博第4578号 / 新制||工||1713(附属図書館) / 京都大学大学院工学研究科航空宇宙工学専攻 / (主査)教授 藤本 健治, 教授 泉田 啓, 教授 太田 快人 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DGAM

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Recent rapid advances in computing, communication, sensing, and actuation, together with miniaturization technologies, have offered unprecedented opportunities to employ large numbers of autonomous vehicles (air, ground, and water) working cooperatively to accomplish a mission. Cooperative control of such multi-agent dynamical systems has potential impact on numerous civilian, homeland security, and military applications. Compared with single-agent control problems, new theoretical and practical challenges emerge and need to be addressed in cooperative control of multiagent dynamical systems, including but not limited to problem size, task coupling, limited computational resources at individual agent level, communication constraints, and the need for real-time obstacle/collision avoidance. In order to address these challenges, a unified optimal multi-agent cooperative control strategy is proposed to formulate the multi-objective cooperative control problem into one unified optimal control framework. Many cooperative behaviors, such as consensus, cooperative tracking, formation, obstacle/collision avoidance, or flocking with cohesion and repulsion, can be treated in one optimization process. An innovative inverse optimal control approach is utilized to include these cooperative objectives in derived cost functions such that a closedorm cooperative control law can be obtained. In addition, the control law is distributed and only depends on the local neighboring agents’ information. Therefore, this new method does not demand intensive computational load and is easy for real-time onboard implementation. Furthermore, it is very scalable to large multi-agent cooperative dynamical systems. The closed-loop asymptotic stability and optimality are theoretically proved. Simulations based on MATLAB are conducted to validate the cooperative behaviors including consensus, Rendezvous, formation flying, and flocking, as well as the obstacle avoidance performance.

京都大学 / 新制・課程博士 / 博士(情報学) / 甲第24743号 / 情博第831号 / 新制||情||139(附属図書館) / 京都大学大学院情報学研究科システム科学専攻 / (主査)教授 大塚 敏之, 教授 加納 学, 教授 東 俊一 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

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