Reinforcement Learning Adaptive Risk-Sensitive Fault-Tolerant IGC Method for a Class of STT Missile with Non-Affine Characteristics, Stochastic Disturbance and Unknown Uncertainties Article Swipe

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YOU? · · 2023 · Open Access · · DOI: https://doi.org/10.21203/rs.3.rs-2490827/v1

In this paper, a novel reinforcement learning adaptive risk-sensitive fault-tolerant stochastic non-affine integrated guidance and control (NAIGC) method is proposed for a class of skid-to-turn (STT) missiles. The cost standard of the risk-sensitive index we coveted can be arbitrarily small, ensured by solving a specific inequality. Firstly, an extended integration system is introduced to solve the challenging control problem posed by the non-affine form of the control signal, taking into account the non-affine nature in the missile. Secondly, for random noise and unknown non-linearities in NAIGC systems, a reinforcement learning actor-critic adaptive risk-sensitive control method is proposed to ensure the input-state stability of the system. Subsequently, hyperbolic tangent functions and adaptive boundary estimation are used to reduce disturbance-induced jitter and actuator fault-induced bias in the control system. In addition, the proposed control strategy improves the missile's interception performance against maneuvering targets and reduces the conservatism of existing adaptive robust control methods. Ultimately, not only is the stability of the NAIGC closed-loop system demonstrated using Lyapunov theory, but the effectiveness and superiority of the method is also verified through numerical simulations.

Related Topics

Missile

Affine Transformation

Reinforcement Learning

Artificial Intelligence

Computer Science

Engineering

Mathematics

Structural Engineering

Aerospace Engineering

Paleontology

Concepts

Missile Affine transformation Class (philosophy) Reinforcement learning Control theory (sociology) Disturbance (geology) Artificial intelligence Reinforcement Computer science Engineering Mathematics Structural engineering Aerospace engineering Geology Control (management) Paleontology Pure mathematics

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Type: preprint
Language: en
Landing Page: https://doi.org/10.21203/rs.3.rs-2490827/v1
PDF: https://www.researchsquare.com/article/rs-2490827/latest.pdf
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Cited By: 1
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Title: Reinforcement Learning Adaptive Risk-Sensitive Fault-Tolerant IGC Method for a Class of STT Missile with Non-Affine Characteristics, Stochastic Disturbance and Unknown Uncertainties

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Type: preprint

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Language: en

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Publication year: 2023

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Publication date: 2023-02-07

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Authors: Zheng Wang, Yuting Hao

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Landing page: https://doi.org/10.21203/rs.3.rs-2490827/v1

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Concepts: Missile, Affine transformation, Class (philosophy), Reinforcement learning, Control theory (sociology), Disturbance (geology), Artificial intelligence, Reinforcement, Computer science, Engineering, Mathematics, Structural engineering, Aerospace engineering, Geology, Control (management), Paleontology, Pure mathematics

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Cited by: 1

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abstract_inverted_index.stochastic	11
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abstract_inverted_index.closed-loop	161
abstract_inverted_index.inequality.	46
abstract_inverted_index.input-state	101
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abstract_inverted_index.disturbance-induced	118
abstract_inverted_index.<title>Abstract</title>	0
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