Intellectual Merit. 

The outcomes of this project research contribute to new approaches that guarantee the safety of cyber-physical systems such as connected vehicles, accounting for the uncertainty of wireless networks.  Traditional techniques for analyzing and developing control laws in safety-critical applications usually require a precise mathematical model of the system. However, there are many control applications where such precise, analytical models cannot be derived or are not readily available. Increasingly, data-driven approaches from machine learning are used in conjunction with sensor or simulation data in order to address these cases. Such approaches can be used to identify unmodeled dynamics with high accuracy. However, an objective that is increasingly prevalent in the literature involves merging or complementing the analytical approaches from control theory with techniques from machine learning. Cyber-physical systems such as self-driving vehicles, distributed sensor networks, aerial drones, and agile robots, need to interact with their environments that are ever-changing and difficult to model. These and many other applications motivate the use of data-driven decision-making and control together. However, if data-driven systems are to be applied in these new settings, it is critical that they be accompanied by guarantees of safety and reliability, as failures could be catastrophic. This project addresses problems in which there are interactions between model-based and data-driven systems and develops learning-based control strategies for the entire system that guarantees safety and optimality. Applications of these systems can be sought in autonomous networked mobile systems that are quickly making their way into the marketplace and are soon expected to serve a wide range of new tasks including package delivery, cooperatively fighting wildfires, and search and rescue after a natural disaster. As the number of these systems increases, their performance and capabilities can be greatly enhanced through wireless coordination. Wireless channel extremely contributes to the optimality and safety of the whole system, but it is a data-driven factor and there is no explicit mathematical model for it to be involved in the model-based part, that is mostly model predictive controller. This project develops novel approaches that integrates data-driven and model-driven control, while accounting for the uncertainty of wireless communication. In the end, the developed algorithms are applied to the motion planning of two connected autonomous vehicles with linear and nonlinear dynamics. The results illustrate that the controller can create a safe trajectory that not only is optimal in terms of control effort and highway capacity usage but also results in a more stable wireless channel with maximum packet delivery rate.

Broader Impacts.

This project outcomes are likely to have a societal impact on the future development of connected vehicles, enabling the performance benefits promised by wirelessly coordinated mobility while providing safety guarantees. The project team has presented the technical results widely to scientific and general audiences at venues such as ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS), IEEE International Intelligent Transportation Systems Conference (ITSC), Conference on Neural Information Processing Systems (NeurIPS). In addition, this project has provided research training opportunities for multiple PhD students at the University of Virginia. The PIs have also integrated the project findings into new educational materials for graduate courses, which have been shared with faculty at other institutions to use.

Last Modified: 11/30/2021
Modified by: Lu Feng