McGrew et al., 2010 - Google Patents
Air-combat strategy using approximate dynamic programmingMcGrew et al., 2010
View PDF- Document ID
- 8724384446196721872
- Author
- McGrew J
- How J
- Williams B
- Roy N
- Publication year
- Publication venue
- Journal of guidance, control, and dynamics
External Links
Snippet
DESPITE long-range radar and missile technology improve-ments, modernfighter aircraft (eg, F/A-22, F-35, and F-15) are still designed for close combat, and military pilots are still trained in air-combat basic fighter maneuvering (BFM). Unmanned aircraft systems (UASs) …
- 238000000034 method 0 abstract description 35
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/10—Simultaneous control of position or course in three dimensions
- G05D1/101—Simultaneous control of position or course in three dimensions specially adapted for aircraft
- G05D1/104—Simultaneous control of position or course in three dimensions specially adapted for aircraft involving a plurality of aircrafts, e.g. formation flying
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/0011—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot associated with a remote control arrangement
- G05D1/0044—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot associated with a remote control arrangement by providing the operator with a computer generated representation of the environment of the vehicle, e.g. virtual reality, maps
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/0011—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot associated with a remote control arrangement
- G05D1/0027—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot associated with a remote control arrangement involving a plurality of vehicles, e.g. fleet or convoy travelling
Similar Documents
Publication | Publication Date | Title |
---|---|---|
McGrew et al. | Air-combat strategy using approximate dynamic programming | |
Shin et al. | An autonomous aerial combat framework for two-on-two engagements based on basic fighter maneuvers | |
Ernest et al. | Genetic fuzzy based artificial intelligence for unmanned combat aerial vehicle control in simulated air combat missions | |
Beard et al. | Decentralized cooperative aerial surveillance using fixed-wing miniature UAVs | |
McGrew | Real-time maneuvering decisions for autonomous air combat | |
Eklund et al. | Implementing and testing a nonlinear model predictive tracking controller for aerial pursuit/evasion games on a fixed wing aircraft | |
US8924069B1 (en) | Artificial immune system approach for airborne vehicle maneuvering | |
Ernest | Genetic fuzzy trees for intelligent control of unmanned combat aerial vehicles | |
Sprinkle et al. | Encoding aerial pursuit/evasion games with fixed wing aircraft into a nonlinear model predictive tracking controller | |
Dong et al. | Guidance and control for own aircraft in the autonomous air combat: A historical review and future prospects | |
Teng et al. | Adaptive computer-generated forces for simulator-based training | |
Ramírez López et al. | Effectiveness of autonomous decision making for unmanned combat aerial vehicles in dogfight engagements | |
Junell et al. | Reinforcement learning applied to a quadrotor guidance law in autonomous flight | |
Yoo et al. | Deep reinforcement learning-based intelligent agent for autonomous air combat | |
Li et al. | Basic flight maneuver generation of fixed-wing plane based on proximal policy optimization | |
Duan et al. | Autonomous maneuver decision for unmanned aerial vehicle via improved pigeon-inspired optimization | |
Dong et al. | Trial input method and own-aircraft state prediction in autonomous air combat | |
Kaneshige et al. | Artificial immune system approach for air combat maneuvering | |
Yoo et al. | Deep reinforcement learning based autonomous air-to-air combat using target trajectory prediction | |
Han et al. | Unmanned aerial vehicle swarm control using potential functions and sliding mode control | |
Yang et al. | Manual-based automated maneuvering decisions for air-to-air combat | |
Sprinkle et al. | Deciding to land a UAV safely in real time | |
Fang et al. | Approximate dynamic programming for CGF air combat maneuvering decision | |
Park et al. | An expert data-driven air combat maneuver model learning approach | |
Sharma | Fuzzy Q learning based UAV autopilot |