Title: Model Predictive Control Architecture for Optimizing Earth Science Data Collection (PCAES)
Presenting Author: Mike Lieber
Organization: Ball Aerospace

Abstract:
The increasing importance of distributed space-based sensor systems has led to exciting developments in large-scale data extraction software and synthesis of complex data products. In particular, this has pushed the development of sensor web software to coordinate the data collection process. However, many systems operate inefficiently due to limited freedom to adaptively interrogate the atmospheric or ground scene. This is particularly true with lidar systems with their characteristic small angular footprints and signal-to-noise requirements (clouds effects for example). We propose a local, multi-layered control system architecture which communicates with the higher level software layers. The local control is based upon an architecture known as Model Predictive Control (MPC). MPC has found use in many different complex systems where the controlled system is characterized as multivariable, with multiple constraints and possibly nonlinear. MPC optimizes the data collection at each time step from higher level constraints and commands and is enabled by the increased computational power now available in FPGA implementations. We propose to demonstrate the MPC architecture for electronically steerable flash lidar (ESFL) and use models to verify it's capability from synthetic scenes and fusion with other sensors. ESFL has potentially hundreds of individually steerable laser beamlets and when combined with other sensor poses a large optimization problem well suited to the MPC approach. Although tested for a flash lidar system, the development has much wider applicability and can be considered as a fast lower level software environment for interface with already developed adaptive queuing software.