A novel approach to reachability analysis of aerodynamic interceptors

Date

2023-9

Author

Bayoğlu Akalın, Tuğba

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An algorithm for reachability analysis has been developed to address the evolving challenges faced by air defense systems due to the increasing diversity of potential threats. The expansion of target sets that pose threats necessitates the development of enhanced defense mechanisms to effectively counter numerous targets, some of which are challenging to detect and track. To address these challenges, the coordinated use of interceptor kinematic capabilities to create a defended airspace shows promise. To implement this strategy effectively, accurate forecasting of interceptor kinematic capabilities, referred to as reachable sets, is crucial. This study presents a novel reachability analysis algorithm with a specific focus on aerodynamic interceptors. The algorithm employs a directional search technique to determine reachable set boundaries along predefined search directions under various flight conditions and durations, while accounting for input constraints. Key factors considered include energy dissipation during maneuvers, energy augmentation from thrust profiles, variable acceleration limits due to dynamic flight conditions, and autopilot dynamics. Leveraging Model Predictive Static Programming, the algorithm offers input-constrained suboptimal midcourse guidance law. This law assesses reachability, generates energy efficient trajectories, and addresses physical limits. The resulting boundaries represent achievable minimum and maximum ranges under different conditions, aiding interceptor capability estimation. Furthermore, the research conducts a comparative analysis, highlighting the significance of input constraints in control system design. Comparing reachable sets with and without input constraints provides insights into system behavior, emphasizing the role of input constraints in guidance algorithm design.

Subject Keywords

Reachable Set Computation, Reachability Analyses, Model Predictive Control, Optimal Control

URI

https://hdl.handle.net/11511/105419

Collections

Graduate School of Natural and Applied Sciences, Thesis