ASE-8016 Advanced Topics in Automation Science and Engineering , 1-10 cr

Implementation ASE-8016 2019-01

Description

This advanced topics course will provide students with an introduction to cooperative control of networked dynamical systems (also known as cyber-physical systems or internet of things), a very active research area in control theory and robotics for the last 15 years. We will cover a variety of topics including the classic consensus (randezvous) problem, formation control problem, distributed optimization, and coverage control for robotic network. We will also discuss different applications such as robotic network and intelligent transportation systems. The course will be a combination of lectures and student's final report. Students will be evaluated based on the assignments and final report. It is expected that the students could learn new topics related to automation and develop their writing skills which will be useful for their postgraduate studies.

Tentative list of topics (subject to change):
1. Consensus problems (how to make a swarm of robots gather at a common location?)
2. Formation control (how to make a swarm of robots move in a desired formation?)
3. Coverage control (how to optimally deploy a group of robots for performing search and rescue mission?)
4. Network topology design and control (who should communicate with whom?)
5. Distributed optimization (how to solve an optimization problem in a scalable manner and by respecting the privacy of each subsystem?)
6. Resilient consensus against cyber attacks (can a group of robots still gather at a common location in the presence of adversary?)
7. Event-triggered control (when should one (robot) communicate with the others?)

Lessons

Assessment scale

Numerical evaluation scale (0-5)

Requirements

Students need to complete assignments and write a report about how to apply the idea of cooperative control into their own research.

Additional information of course implementation

The course is intended for postgraduate students (both master and PhD students).
The students are expected to be familiar with (at least, have interest in) control theory. The topics have broad applications such as in power system (smart grid), robotic network, smart mobility, machine learning.