Three‐Dimensional Impact Angle Constrained Guidance Laws with Fixed‐Time Convergence

Gao, Jiwei; Cai, Yuanli

doi:10.1002/asjc.1571

Cited by 15 publications

(11 citation statements)

References 33 publications

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“…Then the three-dimensional relative kinematic dynamics between missile and target can be expressed as follows [27]:…”

Section: Missile-target Interception Geometrymentioning

confidence: 99%

“…The objective of the guidance law is to achieve the interception of missile and target, with desired impact angle and . The impact angle is defined to be the LOS angles as in [27] [28]. As nullifying the LOS angular rates ̇ and ̇ can lead to the interception, the solution to this problem is to design the missile accelerations to guarantee the following equations: ̇ = ̇ = 0, = , = .…”

Section: Missile-target Interception Geometrymentioning

confidence: 99%

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Range-Aware Impact Angle Guidance Law With Deep Reinforcement Meta-Learning

et al. 2020

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In this paper, a new guidance law is proposed for impact angle constrained missile with timevarying velocity against a maneuvering target. The proposed guidance law is based on model-based deep reinforcement learning (RL) technique where a deep neural network is trained to be a predictive model used in model predictive path integral (MPPI) control. Tube-MPPI, a robust approach utilizing ancillary controller for disturbance rejection, is introduced in guidance law design in this work to deal with the MPPI degradation of robustness when the deep predictive model differs with actual environment. To further improve the performance, meta-learning is utilized to enable the deep neural dynamics adapt to environment changes online. With this approach the model mismatch of the nominal controller is reduced to improve tube-MPPI performance. Furthermore, a range-aware hyperbolic function is proposed as an adaptive function in the MPPI performance index design. Thus, reduced initial acceleration command and increased terminal velocity benefit guidance performance. Numerical simulations under various conditions demonstrate the effectiveness of proposed guidance law. INDEX TERMS Missile guidance, tube model predictive control, meta-learning, deep reinforcement learning, impact angle constraint

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“…Then the three-dimensional relative kinematic dynamics between missile and target can be expressed as follows [27]:…”

Section: Missile-target Interception Geometrymentioning

confidence: 99%

Section: Missile-target Interception Geometrymentioning

confidence: 99%

Range-Aware Impact Angle Guidance Law With Deep Reinforcement Meta-Learning

et al. 2020

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“…Taking the longitudinal plane as an example, the missile impact angle θ d is defined as the included angle between the missile and the target velocity vectors at the engagement time, and the impact angle corresponds to the terminal LOS angle as follows [20,21]:…”

Section: Problem Formulationmentioning

confidence: 99%

Integrated Guidance and Control with Input Saturation and Impact Angle Constraint

Zhang

Gan

et al. 2020

Discrete Dynamics in Nature and Society

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Aiming at the problem of impact angle constraint and input saturation, an integrated guidance and control (IGC) algorithm with impact angle constraint and input saturation is proposed. A three-channel independent design model of missile IGC with impact angle constraint is established, and an extended state observer with fast finite-time convergence is designed to estimate and compensate model errors and coupling relationship between channels. Based on the nonsingular terminal sliding mode control and backstepping control, the IGC three-channel independent design is completed. Nussbaum function and an auxiliary system are introduced to deal with the input saturation. The Lyapunov function is constructed to prove the finite-time convergence of the IGC algorithm. The missile six-degree-of-freedom simulation results show the effectiveness and superiority of the IGC algorithm.

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“…Line-of-sight (LOS) angles are denoted by θ L and φ L . Then the three-dimensional engagement dynamics can be expressed as follows [41]:…”

Section: In This Figure O I X I Y I Z I O M X M Y M Z M and O T mentioning

confidence: 99%

Learning to Guide: Guidance Law Based on Deep Meta-Learning and Model Predictive Path Integral Control

et al. 2019

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In this paper, we present a novel guidance scheme based on model-based deep reinforcement learning (RL) technique. With model-based deep RL method, a deep neural network is trained as a predictive model of guidance dynamics which is incorporated into a model predictive path integral (MPPI) control framework. However, the traditional MPPI framework assumes the actual environment similar to the training dataset for the deep neural network which is impractical in practice with different maneuvering of the target, other perturbations, and actuator failures. To address this problem, our method utilizes meta-learning technique to make the deep neural dynamics model adapt to such changes online. With this approach, we can alleviate the performance deterioration of standard MPPI control caused by the difference between the actual environment and training data. Then, a novel guidance law for a varying velocity interceptor intercepting maneuvering target with desired terminal impact angle under actuator failure is constructed based on the aforementioned techniques. The simulation and experiment results under different cases show the effectiveness and robustness of the proposed guidance law in achieving successful interceptions of maneuvering target. INDEX TERMS Missile guidance, model predictive control, meta-learning, deep reinforcement learning, impact angle constraint.

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Three‐Dimensional Impact Angle Constrained Guidance Laws with Fixed‐Time Convergence

Cited by 15 publications

References 33 publications

Range-Aware Impact Angle Guidance Law With Deep Reinforcement Meta-Learning

Range-Aware Impact Angle Guidance Law With Deep Reinforcement Meta-Learning

Integrated Guidance and Control with Input Saturation and Impact Angle Constraint

Learning to Guide: Guidance Law Based on Deep Meta-Learning and Model Predictive Path Integral Control

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