Synthesis of optimal feedback guidance law for homing missiles using neural networks

Rahbar, N.; Bahrami, Mohsen

doi:10.1002/1099-1514(200005/06)21:3<137::aid-oca668>3.3.co;2-5

Cited by 2 publications

(2 citation statements)

References 6 publications

(6 reference statements)

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“…Other authors such as Ref. [14,21,22] have discussed approaches to substitute advanced classical guidance laws (OGL) using ANN. Just like has been mentioned when evaluating [16] attempting to use neural networks to represent advanced classical guidance laws has been left for the future.…”

Section: Backgroundd For Ann Based Missile Guidancementioning

confidence: 99%

Intelligent Missile using Artificial Neural Networks

Rajagopalan

Faruqi

Nandagopal

2015

AIR

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Missile guidance systems using the Proportional Navigation (PN) guidance law is limited in performance in supporting wide class of engagement scenarios with varying mission and target parameters. For surpassing this limitation, an Artificial Neural Network (ANN) to substitute the PN guidance is proposed by the authors. The ANN based system enables learning, adaptation, and faster throughput and thus equips the guidance system with capability akin to intelligent biological organisms. This improvement could remove the barrier of limitations with allowable mission scope. In this paper, a Multi-Layer Perceptron (MLP) has been selected to implement the ANN based approach for replacing PN guidance. Attempts to replace PN guidance using MLP are limited in the literature and warrant greater attention due to significant theoretical development with the MLP field in recent times. It is shown in this paper, that the MLP based guidance law can effectively substitute PN for a wide range of engagement scenarios with variations in initial conditions. A foundational argument to justify using an MLP for substituting PN is provided. Besides this, the design, training and simulation based testing approach for an MLP to replace PN has been devised and described. The potential for faster throughput is possible as the MLP nodes process information in parallel when generating PN like guidance commands. The results clearly demonstrate the potential of MLP in future applications to effectively replace and thus upgrade a wide spectrum of modern missile guidance laws.

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Section: Backgroundd For Ann Based Missile Guidancementioning

confidence: 99%

Intelligent Missile using Artificial Neural Networks

Rajagopalan

Faruqi

Nandagopal

2015

AIR

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“…Researchers have utilized a multitude of schemes in an attempt to solve this problem. Rahbar and Bahrami (2000) have used neural networks in developing a closed‐loop optimal control for pursuit guidance. Kunisch et al (2003) have proposed a non‐linear feedback law for optimal control of evolution problem by a combination of model reduction techniques and numerical solution of Hamiltonian‐Jaccobi‐Bellman (HJB) equation.…”

Section: Introductionmentioning

confidence: 99%

A neuro‐optimal approach for thrust‐insensitive trajectory planning

Pourtakdoust

Pazooki

Noushabadi

2009

Aircraft Engineering and Aerospace Technology

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PurposeThe purpose of this paper is to devise a new approach to synthesize closed‐loop feedback guidance law for online thrust‐insensitive optimal trajectory generation utilizing neural networks.Design/methodology/approachThe proposed methodology utilizes an open‐loop variational formulation that initially determines optimal launch/ascent trajectories for various scenarios of known uncertainties in the thrust profile of typical solid propellant engines. These open‐loop optimized trajectories will then provide the knowledge base needed for the subsequent training of a neural network. The trained network could eventually produce thrust‐insensitive closed‐loop optimal guidance laws and trajectories in flight.FindingsThe proposed neuro‐optimal guidance scheme is effective for online closed‐loop optimal path planning through some measurable and computable engine and flight parameters.Originality/valueDetermination of closed‐loop optimal guidance law for non‐linear dynamic systems with uncertainties in system and environment has been a challenge for researchers and engineers for many years. The problem of steering a solid propellant driven vehicle is one of these challenges. Even though a few researchers have worked in the area of non‐linear optimal control and thrust‐insensitive guidance, this paper proposes a new strategy for the determination of closed‐loop online thrust insensitive guidance laws leading to optimal flight trajectories for solid propellant launch and ascent vehicles.

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Synthesis of optimal feedback guidance law for homing missiles using neural networks

Cited by 2 publications

References 6 publications

Intelligent Missile using Artificial Neural Networks

Intelligent Missile using Artificial Neural Networks

A neuro‐optimal approach for thrust‐insensitive trajectory planning

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