My research is focused on the following areas:
- Development of High Performance Guidance Navigation and Control Algorithms for Aerospace Systems
- Multi Unmanned Aerial Vehicle Applications
- Distributed Artificial Intelligence and Machine Learning
- Air Traffic Network Modelling and Optimization
Development of High Performance Guidance Navigation and Control Algorithms for Aerospace Systems
Applied control and estimation theory has advanced to a point where we can autonomously fly unmanned aircraft and perform basic maneuvers such as landing/take-off and waypoint tracking effortlessly. However, for operations that require high precision target tracking, agile maneuvering and abilility to recover from loss of control, the current Guidance, Navigation and Control (GNC) algorithms still rely heavilty on human input. The basic objective of this research thrust is to push the state of the art in GNC design for both aircraft and spacecraft systems and hence form the baseline for the next generation of autonomous aerospace vehicles.
- Autonomous Control of Unmanned Combat Air Vehicles: Design of a Multimodal Control and Flight Planning Framework for Agile Maneuvering
- Collaborators: Gokhan Inalhan, Emre Koyuncu
- This project studies the problem of generation and control of maneuver profiles for Unmanned Combat Air Vehicles (UCAV) with aggressive maneuvering capability. A motion description framework is developed, where complex maneuvers are decomposed into series of sub-maneuvers (modes) and associated parameters. Main advantage of framework is allowance of the feasibility and control problems to be treated for each sub-maneuver individually instead of whole maneuver, thus reducing the complexity of the problem which can also be modeled in the language of Hybrid Dynamical Systems. For maneuver generation, agility metrics and mode compatibility relationships are used to create a feasible maneuver generation algorithm. For low level controls, each mode is assigned a Higher Order Sliding Mode Controller, where whole maneuver is controlled via switching between modes and controllers. Overall generation and control algorithms are tested on agile combat maneuver examples.
Multi Unmanned Aerial Vehicle Applications
Unmanned Aerial Vehicles (UAVs) have been enjoying a massive popularity in the last decade. Technologies that enable autonomous single UAV missions has been demonstrated in both academia and industry. However, many practical missions require coordination for several UAVs, due to coverage of large operational areas, constraints on robutsness and need for heterogeneous fleets with diverse set of capabilities. The main objective of this research thrust is to develop scalable planning algorithms and corresponding control and localization methods to enable autonomous multi-UAV operations for a wide variety of civil and military applications; such as persistent surveillance, security, agriculture and fire-fighting.
- Health Aware Planning for Multiagent Persistent Missions
- Partners: Boeing Research and Technology
- Collaborators: Jonathan P. How, Girish Chowdhary, Yu Fan Chen, Tuna Toksoz, Josh Redding, Matthew Vavrina, John Vian
- The goal of this work is to create methods to solve online multiagent planning problems under constraints such as, uncertain mission dynamics, communication requirements and time varying agent capabilities. The work combines techniques from approximate dynamic programming, model based reinforcement learning and learning focused adaptive control.