Range Extension Of An Air-to-air Engagement By Offline Trajectory Optimization

Sarkar, Krishna; Kar, P.; Mukherjee, Arijit; Padhi, Radhakant

doi:10.2514/6.2011-6339

Cited by 6 publications

(5 citation statements)

References 14 publications

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“…The complete procedure is available in Betts 15 and not reported here for brevity. Recently Mukherjee 16 , et al carried out an air-to-air trajectory optimisation using NLP based direct transcription method. Present problem formulation is subset of that with fixed initial condition, terminal constraint of meeting specified evader (R d ,H d ) and in-flight path constraint of |α| ≤ α max .…”

Section: Decision Vectormentioning

confidence: 99%

“…In the second pass, N b = 2 × 5 − 1 = 9 and N c = 3× (N b − 1) has been chosen. The algorithm for choice of (N b ,N c ) for different iterations is given in Mukherjee16 , et al In the first pass, for 17 grid points (N b ,1 = 5), the NLP constraints are formed using trapezoidal discretisation. Total five passes are required for achieving convergence in present problem.…”

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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%

mentioning

confidence: 99%

CMAC Trained Optimum Mid course Guidance for Tactical Flight Vehicle

Kumar

Sarkar

2021

Def. Sc. J.

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“…Beyond visual range (BVR) combat is being the main form of future air combat [1][2][3]. Developing the missile with BVR combat capability by improving the existing medium range missile has great practical significance.…”

Section: Introductionmentioning

confidence: 99%

Trajectory Optimization Based on Multi‐Interval Mesh Refinement Method

Lei

Shao

Liu

et al. 2017

Mathematical Problems in Engineering

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In order to improve the optimization accuracy and convergence rate for trajectory optimization of the air-to-air missile, a multi-interval mesh refinement Radau pseudospectral method was introduced. This method made the mesh endpoints converge to the practical nonsmooth points and decreased the overall collocation points to improve convergence rate and computational efficiency. The trajectory was divided into four phases according to the working time of engine and handover of midcourse and terminal guidance, and then the optimization model was built. The multi-interval mesh refinement Radau pseudospectral method with different collocation points in each mesh interval was used to solve the trajectory optimization model. Moreover, this method was compared with traditional h method. Simulation results show that this method can decrease the dimensionality of nonlinear programming (NLP) problem and therefore improve the efficiency of pseudospectral methods for solving trajectory optimization problems.

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“…The propulsion burn rate of both motors are (ṁ p1 , ṁ p2 ) are (7.7, 3.9) kg/s. The present configuration has been used for range extension with single-pulse rocket motor recently 13 . The present design study is with two-pulse rocket motor.…”

Section: Case Study Of Tactical Fvmentioning

confidence: 99%

“…Menon [11][12] , et al carried out synthesis of a high performance two-pulse-motor-propelled beyond visual range (BVR) vehicle by simultaneous optimisation of trajectory and propulsion system for air-to-air application using the direct method 11,12 . Kar 13 , et al have evolved the maximum launch range pertaining to an air-to-air interception of a FV with onepulse motor using NLP. The same methodology has been used to cover a given launch range of the present FV at minimum time subject to flight path and terminal constraints.…”

Section: Introductionmentioning

confidence: 99%

Trajectory Optimisation of Long Range and Air-to-Air Tactical Flight Vehicles

Manickavasagam¹,

Sarkar²,

Vaithiyanathan³

2015

DSJ

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NomeNclature A eEngine exhaust area C A This paper presents formulation and solution of long range flight vehicle and tactical air-to-air flight vehicle trajectory optimisation. The first case study is of a long range flight vehicle. Here an optimum steering program during powered phase has been evolved as control input for achieving maximum range with available propulsions in the presence of path and terminal constraints. The second case study is of a tactical flight vehicle for air-to-air application. Here a minimum flight time trajectory has been generated for covering a specified range pertaining to a specified air-to-air engagement by evolving pitch lateral acceleration as control input. Here also, there are many path and terminal constraints consisting of launch aircraft, pursuer, and evader. The studies have been carried out as part of system design activity of both flight vehicles. Both are real-life optimisation problems under several constraints. Through it is very difficult to solve such practical problems in flight dynamics using classical optimal control theory, it has been solved successfully using direct transcription method based on nonlinear programming. Rapid convergence has been achieved in four passes with minimum grids in first pass, to start with, and increasing the grids in subsequent passes. Solving such a real-life problem with proper convergence subjected to many constraints is claimed as novelty of present research.

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Range Extension Of An Air-to-air Engagement By Offline Trajectory Optimization

Cited by 6 publications

References 14 publications

CMAC Trained Optimum Mid course Guidance for Tactical Flight Vehicle

CMAC Trained Optimum Mid course Guidance for Tactical Flight Vehicle

Trajectory Optimization Based on Multi‐Interval Mesh Refinement Method

Trajectory Optimisation of Long Range and Air-to-Air Tactical Flight Vehicles

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