Computationally Aware Cyber-Physical Systems
Predicting how a system will behave in the future requires more computing power if that system is complex. Navigating through environments with many obstacles could require significant computing time, which may delay the issue of decisions that have to be made by the on-board algorithms. Fortunately, systems do not always need the most accurate model to predict their behavior.
This project seeks to develop new theory for controller design which incorporates selecting the best model to use when making a decision in real time. The project advances the knowledge on modeling, analysis, and design of CPSs that utilize predictive methods for trajectory synthesis under constraints in real-time cyber-physical systems.
- Design tools capable of accounting for computational capabilities in real-time
- Design hybrid feedback algorithms that include more accurate prediction schemes exploiting computational capabilities, within the time constraints.
- Design methods for controllers that switch between different predictive models of the system, depending on the computational burden of the associated, and the accuracy that the predictive model provides. The approach utilizes hybrid control to switch between subsystems.
- Develop new framework to characterize hardware/software interactions and uncertainty.
Consider explicitly hardware/computational constraints when defining operation regions for safer implementation.
- To enable more accurate and faster trajectory synthesis for controllers with nonlinear plants, or nonlinear constraints that encode obstacles.
- Methods for the design of algorithms that adapt to the computational limitations of autonomous and semi-autonomous systems while satisfying stringent timing and safety requirements.
These tools will pave the way for more kinds of aircraft to navigate closely and safely with one another through the National Air Space (NAS), including Unmanned Air Systems (UAS).
University of California Santa Cruz.
PI: Dr. Ricardo Sanfelice
This work is supported by the National Science Foundation under award CNS-1544395. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. Autonomous Vehicles CACPS