Course description (ASE 396 & CS 395T, Fall 2015)

Synopsis: Autonomous robots and vehicles, smart medical devices and transplants, and advanced manufacturing platforms are only a few of the emerging systems that have been enabled by the advances in sensor, computation, communication, and control technologies. Systems that leverage the co-existence of these technologies are often called "cyberphysical systems". Over the last two decades, the growth in our ability to build cyberphysical systems has outpaced the growth in our ability to systematically specify, model and design them. This mismatch is now widely accepted a bottleneck in the affordable development of trustworthy cyberpyhsical systems. It also has motivated extensive recent research for creating methods and tools for the formal specification and automated verification and synthesis of cyberphysical systems.

This course provides an exposition to cyberphysical systems, theory and methods for developing these systems, and their applications in a range of domains, including autonomy, human-machine interaction, robotics, and networked systems. It primarily distills relevant principles from controls and formal methods, and highlights connections with optimization, networking, and learning.

The course will balance between establishing a working knowledge of the computational tools for verification and synthesis of cyberphysical systems and an understanding of the theory behind them. The lectures will be punctured by computational demonstrations of the tools and assignments will include computational elements.

The objective of the course is to prepare the students for research in cyberpyhsical systems. To this end, the course includes a project component. Typical project topics include tackling (pieces of) an open theoretical problem, software implementation and benchmarking of existing algorithms, and application of existing tools in a new area.

Expected audience and prerequisites: Graduate students and senior undergraduate students from engineering and computer science with related interests.

The topics listed above will be covered with no expectation on prior knowledge. Some familiarity in basic ideas in controls or automata theory (e.g., being able to recognize differential equations or automata) is desirable.

Course projects: Every student will be required to work on a course project throughout the semester. The type of project work may be application of existing methods/tools to a new problem in a new application domain, software implementation and benchmarking of existing algorithms, extension of an existing algorithm, and theoretical study of an "open" problem. The students are encouraged to make connections between the course project and their own research. Alternatively, the instructor will present a list of potential project topics.

Grading: The final grade will be based on the following elements:

• Homework -- 45% (7-9 assignments)
• Project -- 45% (15% for the mid-term report and 30% for the final report)
• Participation -- 10%
  

Course material: No specific textbook. Material relevant for each lecture and general references will be made available through a course wiki.