Neural network guidance based on pursuit-evasion games with enhanced performance

Choi, Han‐Lim; Tahk, Min-Jea; Bang, Hyochoong

doi:10.1016/j.conengprac.2005.03.001

Cited by 18 publications

(1 citation statement)

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“…In their proposed hybrid guidance scheme, γ-correction guidance law steers the vehicle to track the optimal trajectory generated from the nominal launch conditions and feedback guidance law reduces terminal miss distance caused by initial launch errors. Choi 9 , et al proposed a differential game guidance law using ANN for an air-to-air engagement.…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%