Three-dimensional suboptimal guidance law based on <i>θ</i> – <i>D</i> technique and nonlinear disturbance observer

Man, Chaoyuan; Liu, Rongjie; Li, Shihua

doi:10.1177/0954410019837123

Cited by 5 publications

(3 citation statements)

References 28 publications

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“…Equation ( 7) can be further simplified into Equation (9). The expressions of f 1 , f 2 and b 1 , b 2 are respectively demonstrated by Equations ( 10) and (11).…”

Section: Formulation Of Guidance Modementioning

confidence: 99%

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Three-dimensional finite-time guidance law based on sliding mode adaptive RBF neural network against a highly manoeuvering target

Zhang

Han

2022

Aeronaut. j.

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In order to intercept a highly manoeuvering target with an ideal impact angle in the three-dimensional space, this paper promises to probe into the problem of three-dimensional terminal guidance. With the goal of the highly target acceleration and short terminal guidance time, a guidance law, based on the advanced fast non-singular terminal sliding mode theory, is designed to quickly converge the line-of-sight (LOS) angle and the LOS angular rate within a finite time. In the design process, the target acceleration is regarded as an unknown boundary external disturbance of the guidance system, and the RBF neural network is used to estimate it. In order to improve the estimation accuracy of RBF neural network and accelerate its convergence, the parameters of RBF neural network are adjusted online in real time. At the same time, an adaptive law is designed to compensate the estimation error of the RBF neural network, which improves the convergence speed of the guidance system. Theoretical analysis demonstrates that the state and the sliding manifold of the guidance system converge in finite time. According to Lyapunov theory, the stability of the system can be guaranteed by online adjusting the parameters of RBF neural network and adaptive parameters. The numerical simulation results verify the effectiveness and superiority of the proposed guidance law.

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“…Equation ( 7) can be further simplified into Equation (9). The expressions of f 1 , f 2 and b 1 , b 2 are respectively demonstrated by Equations ( 10) and (11).…”

Section: Formulation Of Guidance Modementioning

confidence: 99%

“…In Ref. [11], a nonlinear suboptimal guidance law, which minimises an integrated quadratic performance index, appears. To simplify the guidance algorithm, theta-D method is used to replace solving Hamilton-Jacobi-Bellman equation of the nonlinear system.…”

Section: Introductionmentioning

confidence: 99%

Three-dimensional finite-time guidance law based on sliding mode adaptive RBF neural network against a highly manoeuvering target

Zhang

Han

2022

Aeronaut. j.

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show abstract

“…Due to their unique features, designing a controller that be able to optimally and robustly manage nonlinear systems subjected to mismatched uncertainties has been the desire of researchers. In this respect, different control methods have been proposed to make nonlinear systems immune against mismatched uncertainties; such as robust control methods, 14‐20 adaptive control schemes, 21‐24 and hybrid control systems 25‐33 . In dealing with mismatched uncertainties, an appropriate controller should make an uncertain nonlinear system robust, while achieving a desired performance.…”

Section: Introductionmentioning

confidence: 99%

Disturbance observer‐based two‐Layer control strategy design to deal with both matched and mismatched uncertainties

Shafei

Bahrami

Talebi

2020

Intl J Robust & Nonlinear

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This study presents a novel control strategy for managing nonlinear systems in the presence of mismatched uncertainties. Dealing with the uncertainties that do not satisfy the so‐called matching conditions is an ongoing issue in control engineering. In this regard, and for the first time, a disturbance observer (DO)‐based hybrid control system, which considers robustness as well as control signal optimality, is developed in this article. For this purpose, and with the aim of robustly managing uncertain nonlinear systems and achieving optimized control effort, an optimal control law based on the state‐dependent Riccati equation is integrated with a DO‐based second‐order sliding mode controller. The Lyapunov stability theory is applied to verify the asymptotic stability of the designed control system. Computer simulations are performed to demonstrate the efficacy and the superiority of our novel controller over two other existing methods (DO‐based sliding mode control [SMC] and DO‐based adaptive SMC). The simulation results show that the presented method is capable of managing uncertain nonlinear systems robustly and with much less control effort than the two mentioned methods.

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A Novel Fixed-Time Convergence Three-Dimensional Guidance Law for Intercepting Highly Maneuvering Targets

Zhang

2022

Int. J. Aeronaut. Space Sci.

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Three-dimensional suboptimal guidance law based on θ – D technique and nonlinear disturbance observer

Cited by 5 publications

References 28 publications

Three-dimensional finite-time guidance law based on sliding mode adaptive RBF neural network against a highly manoeuvering target

Three-dimensional finite-time guidance law based on sliding mode adaptive RBF neural network against a highly manoeuvering target

Disturbance observer‐based two‐Layer control strategy design to deal with both matched and mismatched uncertainties

A Novel Fixed-Time Convergence Three-Dimensional Guidance Law for Intercepting Highly Maneuvering Targets

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