PhD Defense: Optimal Control and Coordination of Autonomous Intelligent Systems by Edge Computing

TECoSA PhD student Kaige Tan will defend his thesis at Gladan, Brinellvägen 85 at the Dept. of Engineering design, KTH Campus.. Contact Lei Feng (lfeng@kth.se) if you are interested in attending.

Abstract: Autonomous Intelligent Systems (AIS) are transforming various sectors by integrating advanced control theories, artificial intelligence, and cyber-physical systems. However, AIS control development faces significant challenges, including ensuring real-time responsiveness, designing adaptive controllers for dynamic environments, and coordinating multi-agent systems under uncertainties. These issues are exacerbated in resource-constrained settings, where balancing computational demands and real-time performance is critical.

To mitigate these challenges, this thesis leverages edge computing to enhance system performance, so that data-driven methods and optimal control technologies become feasible for complex AIS applications. Edge computing is a scheme that brings computation, communication, and storage resources closer to data sources, to achieve low-latency processing, real-time adaptability, and scalable solutions for AIS applications. It provides two key benefits: (1) offloading computationally intensive tasks to nearby edge servers, so as to ensure responsive and efficient operations despite constrained resources onboard, and (2) facilitating decentralized coordination among multiple agents by exploiting the edge server as a trustworthy node, so as to improve system scalability, reliability, and collaborative decision-making.

Building on the advantages of the offloading and coordinatio The dissertation will be at March 31st, 13:00. The venue is Gladan, Brinellvägen 85 at the Dept. of Engineering design, KTH Campus.

The title of my thesis is: “Optimal Control and Coordination of Autonomous Intelligent Systems by Edge Computing”, and the abstract is:

Autonomous Intelligent Systems (AIS) are transforming various sectors by integrating advanced control theories, artificial intelligence, and cyber-physical systems. However, AIS control development faces significant challenges, including ensuring real-time responsiveness, designing adaptive controllers for dynamic environments, and coordinating multi-agent systems under uncertainties. These issues are exacerbated in resource-constrained settings, where balancing computational demands and real-time performance is critical.

To mitigate these challenges, this thesis leverages edge computing to enhance system performance, so that data-driven methods and optimal control technologies become feasible for complex AIS applications. Edge computing is a scheme that brings computation, communication, and storage resources closer to data sources, to achieve low-latency processing, real-time adaptability, and scalable solutions for AIS applications. It provides two key benefits: (1) offloading computationally intensive tasks to nearby edge servers, so as to ensure responsive and efficient operations despite constrained resources onboard, and (2) facilitating decentralized coordination among multiple agents by exploiting the edge server as a trustworthy node, so as to improve system scalability, reliability, and collaborative decision-making.

Building on the advantages of the offloading and coordination capabilities inherent in edge computing, this thesis investigates how these features can be harnessed to overcome the limitations of AIS in achieving optimal control and coordination. Primary contributions of this thesis include: (1) the development of state estimation and data-driven optimal control algorithms, which enables more precise estimation and control in nonlinear, time-variant systems; (2) the design of edge-based computational task offloading algorithms to achieve real-time adaptive control and learning by distributing computationally intensive tasks, which effectively balances latency and resource constraints; and (3) the introduction of decentralized optimization frameworks for multi-agent systems, which enhances scalability, robustness, and coordination under communication constraints by leveraging edge servers as trustworthy nodes for efficient collaboration and decision-making. All contributions have been validated through case studies in soft robotics and connected autonomous vehicles, demonstrating their effectiveness and advancements over existing methods.

In summary, this thesis advances AIS capabilities by addressing real-time computational challenges and enabling optimal, data-driven control and decentralized coordination. The integration of edge computing improves the efficiency, scalability, and adaptability of AIS, offering promising opportunities for applications in autonomous mobility and other dynamic domains.

Details of the panel are shown below:

Principal supervisor: Assoc. prof. Lei Feng
Co-supervisor: Assoc. Prof. Fredrik Asplund
Chair at the defense: Assoc. Prof. Andreas Cronhjort
Opponent: Assoc. Prof. Quanyan Zhu

Members of the grading committee:

Prof. Nicolce Murgovski
Assoc. Prof. Ci Liang
Assoc. Prof. Mohammad Ashjaei

Substitute: Prof. Jonas Mårtensson