CAREER: Co-Design of Networking and Decentralized Control to Enable Aerial Networking in an Uncertain Airspace

ABSTRACT

Airborne networking utilizes direct flight-to-to-flight communication for flexible information sharing, safe maneuvering, and coordination of time-critical missions. It is challenging because of the high mobility, stringent safety requirements, and uncertain airspace environment. This project uses a co-design approach that exploits the mutual benefits of networking and decentralized mobility control in an uncertain heterogeneous environment. The approach departs from the usual perspective that views physical mobility as communication constraints, communication as constraints for decentralized mobility control, and uncertain environment as constraints for both. Instead, we proactively exploits the constraints, uncertainty, and new structures with information to enable high-performance designs. The features of the co-design such as scalability, fast response, trackability, and robustness to uncertainty advance the core CPS science on decision-making for large-scale networks under uncertainty.

During the period of 2016-2017, the following results are achieved.

1. With respect to the scalable control for high-dimensional uncertainties, we developed a stochastic optimal control framework that is scalable to the dimension of high-dimensional uncertainties. The framework builds on two multi-dimensional uncertainty evaluation approaches, the multivariate probabilistic collocation method (M-PCM) and its extension called M-PCM- OFFD which integrates M-PCM with the orthogonal fractional factorial designs (OFFDs) to break the curse of dimensionality. We explored the capabilities of M-PCM and M-PCM-OFFD based stochastic optimal control, reinforcement learning based control, and Q-learning based control.
   
2. With respect to the effectiveness of layered structures and the equipment of additional memories to UAV networking, we showed that layered structures are more effective than equivalent egalitarian structures in terms of the data transmission load required to reach consensus. We took a structural approach by establishing explicit relationships between simple structural characteristics and the performance of quantized consensus (e.g., consensus condition, consensus value, and transmission load to reach consensus) for broad classes of layered structures. We also provided analytical asymptotic and transient performance results on the use of additional local memories to further reduce data transmission load to reach consensus.
   
3. With respect to the use of communication performance measurements to inform decentralize control, we developed a decentralized control algorithm for directional antennas mounted on two moving UAVs to achieve a robust broad-band long-distance communication channel. In particular, the self- alignment of UAV-mounted directional antennas over a long distance is achieved through fusing GPS and communication channel characteristic measured by received signal strength indicator (RSSI), using unscented Kalman filter (UKF) and fuzzy logic. The solution significantly enhances the performance of wireless communication channel in imperfect environment subject to the unavailability of GPS signals and unstable strength of wireless signals. Simulations were performed to validate the decentralized directional antenna control approach.
   
4. With respect to the implementation and testbed development, we chose begalbone and FPGA as the control algorithm implementation. We also explored the use of ROS as the middleware. We implemented both communication schemes: 1) high-bandwidth communication signal goes through the directional communication channel, and GPS and control signals transmit through a separate omni communication channel, and 2) all signals go through the directional communication channel.