Co-Design of Autonomous Systems: From Hardware Selection to Control Synthesis
This work provides a formal framework for jointly optimizing hardware and control for autonomous systems, which is important for engineers designing complex cyber-physical systems.
This paper addresses the co-design of control algorithms and robotic platforms by formalizing LQG control problems as monotone feasibility relations. This approach allows embedding control co-design into higher-level robotic platform co-design, enabling the computation of Pareto efficient design solutions for autonomous drones performing search-and-rescue tasks.
Designing cyber-physical systems is a complex task which requires insights at multiple abstraction levels. The choices of single components are deeply interconnected and need to be jointly studied. In this work, we consider the problem of co-designing the control algorithm as well as the platform around it. In particular, we leverage a monotone theory of co-design to formalize variations of the LQG control problem as monotone feasibility relations. We then show how this enables the embedding of control co-design problems in the higher level co-design problem of a robotic platform. We illustrate the properties of our formalization by analyzing the co-design of an autonomous drone performing search-and-rescue tasks and show how, given a set of desired robot behaviors, we can compute Pareto efficient design solutions.