Missile guidance using neural networks

Lee, Hungu; Lee, Yong-In; Song, Eun-Jung; Sun, Byung-Chan; Tahk, Min-Jea

doi:10.1016/s0967-0661(97)00059-2

Cited by 7 publications

(2 citation statements)

References 5 publications

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“…In launcher it is for 0.5 s. At launch exit initial states for trajectory optimisation are (V h , V v , r, h) as (0, 25 m/s, 0 m, 300 m). Pursuer has to reach at mid-course end (r f , h f ) as (15,5) km. The path constraints are α < 15 deg and Qα < 15000 N rad/ m 2 from structural design consideration.…”

Section: Decision Vectormentioning

confidence: 99%

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CMAC Trained Optimum Mid course Guidance for Tactical Flight Vehicle

Kumar

Sarkar

2021

Def. Sc. J.

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This paper discusses design and validation of neural network based mid-course guidance law of a surface to air flight vehicle. In present study, initially different optimal trajectories have been generated off-line of different pursuer-evader engagements by ensuring minimum flight time, maximum terminal velocity and favorable handing over conditions for seeker based terminal guidance. These optimal trajectories have been evolved by nonlinear programming based direct method of optimisation. The kinematic information of both pursuer and evader, generated based on these trajectories have been used to train cerebellar model articulate controller (CMAC) neural network. Later for a given engagement scenario an on-line near optimal mid-course guidance law has been evolved based on output of trained network. Training has been carried out by CMAC type supervisory neural network. The tested engagement condition is within input/output training space of neural network. Seeker based homing guidance has been used for terminal phase. Complete methodology has been validated along pitch plane of pursuer-evader engagement. During mid-course phase, the guidance demand has been tracked by attitude hold autopilot and during terminal phase, the guidance demanded lateral acceleration has been tracked by acceleration autopilot. System robustness has been studied in presence of plant parameter variations and sensor noise under Monte Carlo Platform.

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Section: Decision Vectormentioning

confidence: 99%

“…These numbers are based on hardware characteristics. Generally inner loop gain crossover frequency , 5 3…”

Section: Guidance Demand Tracking Autopilotmentioning

confidence: 99%