UC Santa Barbara

In our rapidly evolving technological landscape, the proliferation of enabling technologies for autonomous systems has given rise to a burgeoning realm of societal-scale smart systems. One noteworthy category within this domain is Human-Cyber-Physical Systems (H-CPS), which encompass physical systems controlled by a blend of computer-based algorithms and human inputs. Examples of H-CPS include the smart grid and autonomous transportation systems. These systems harness the potential of distributed computing units, fast communication channels, and real-time data collection, offering efficient mechanisms for their management. This requires a synthesis of tools from distributed optimization, machine learning, game theory, and stochastic control.

However, the advent of H-CPS also presents novel challenges. Human decisions, often stochastic and beyond direct control, must be factored into the developed mechanisms. Moreover, the dependable operation of H-CPS hinges on secure communication between physical systems and computing units, raising concerns regarding user data privacy and system security. The growing number of humans and devices generates copious amounts of data from sensing units, necessitating computationally efficient data processing to ensure seamless H-CPS operation.

This thesis aims to design network control, optimization, and learning frameworks that enhance safety, robustness, and efficiency in H-CPS, with practical applications in smart infrastructure systems like the power grid and transportation networks. Additionally, its relevance extends to diverse Internet of Things applications, emphasizing user data privacy, such as the development of language models from text data. The thesis unfolds in three interconnected chapters. In the first chapter, we introduce provably efficient and adversarially robust multi-agent optimization algorithms tailored for distributed resource allocation and distributed learning scenarios in the presence of malicious agents. Moving forward to the second chapter, we aim to design prices for shared resources that do not violate hard (mainly physical) constraints of the system, without any two-way communications with the users as common in distributed optimization based methods. The third chapter focuses on crafting and analyzing joint ride pricing and fleet management policies for the control of autonomous urban mobility fleets. Throughout these chapters, we not only analyze the theoretical performance of our proposed mechanisms but also substantiate their effectiveness through extensive simulations on real-world problems.