Advanced Control and Intelligent Systems
Autonomous systems must operate reliably in complex, fast-changing environments where human operators rely on timely, accurate system performance. This research advances intelligent control architectures that enable autonomous platforms to adapt, respond, and perform effectively in real-world operational conditions. By integrating real-time hardware and software systems with advanced algorithms, researchers design and validate control strategies that improve system resilience, enhance mission effectiveness, and support safer, more reliable autonomous operations for the warfighter.
Key capabilities include:
Real-time hardware and software integration for autonomous systems
Algorithm development for adaptive and responsive autonomous control
Embedded systems and digital controller prototyping
Control architectures supporting energy and complex dynamic systems
Rigid-body PID control and advanced flight control methods
Hardware-in-the-Loop (HITL) simulation for system testing and validation
Adaptive Interception in Dynamic Domains: Exploration of Hybrid Reinforcement Learning in Pursuit-Evasion Tasks
Akinmolayan, M., Josyula, D., Casbeer, D. Adaptive Interception in Dynamic Domains: Exploration of Hybrid Reinforcement Learning in Pursuit-Evasion Tasks, AAAI Spring Symposium, April 2026. Affiliation: Bowie State University (To appear).
A Unified Naming and Addressing Scheme for Hybrid DTN/NDN Communication Protocols
Langrin, R., & Blackstone, J. (2026). A Unified Naming and Addressing Scheme for Hybrid DTN/NDN Communication Protocols. Proceedings of the 2026 AAAI Symposium Series, San Francisco, CA. Affiliation: Howard University, Research Institute For Tactical Autonomy (To appear).
