I develop novel engineering methods and advanced mathematical tools for investigating large-scale multi-agent systems and ensemble systems. Applications include autonomous and self-driving vehicles, unmanned aircrafts, sensor-actuator networks, social networks, and energy networks. I address questions about designing control laws that organize the multi-agent systems for achieving global objectives, questions about controlling and stabilizing multi-agent systems under the constraint that agents can only access local informations, questions about improving/fixing the robustness issues under perturbations and/or malicious attacks, and questions about optimal allocation of resources—such as power supply, sensor bandwidth, computational capacity—for maximizing performance measures and/or minimizing costs.
geometric control theory, stochastic control theory, optimization, decision theory, networked control, distributed control, ensemble control and estimation
ECEN 3810 - Introduction to Probability Theory
Fall 2019 / Spring 2021
Covers the fundamentals of probability theory, and treats the random variables and random processes of greatest importance in electrical engineering. Provides a foundation for study of communication theory, control theory, reliability theory, and optics. Credit not granted for this course and MATH 4510 or APPM 3570.
ECEN 5008 - Special Topics
Spring 2019 / Spring 2020 / Fall 2020
Examines a special topic in Electrical, Computer and Energy Engineering. May be repeated up to 9 total credits.
ECEN 5448 - Advanced Linear Systems
Fall 2018 / Fall 2019 / Fall 2020 / Fall 2021
Offers a state space approach to analysis and synthesis of linear systems, state transition matrix, controllability and observability, system transformation, minimal realization, and analysis and synthesis of multi-input and multi-output systems. Recommended prerequisites: ECEN 3300 and ECEN 4138.