Aerodynamic Heating Ground Simulation of Hypersonic Vehicles Based on Model-Free Control Using Super Twisting Nonlinear Fractional Order Sliding Mode

Lv, Xiaodong; Zhang, Guangming; Zhu, Mingxiang; Shi, Zhihan; Bai, Zhiqing; Alexandrov, Igor

doi:10.3390/math10101664

Cited by 3 publications

(1 citation statement)

References 35 publications

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“…As mentioned above, the surface temperature of HV is highly nonlinear and transient, and GTESTD is also a nonlinear system. In order to facilitate the design of control strategies and acquire the control performance of real thermal simulations, the mathematical model of GTESTD needs to be established [28].…”

Section: Mathematical Modelmentioning

confidence: 99%

Adaptive Neural Network Global Fractional Order Fast Terminal Sliding Mode Model-Free Intelligent PID Control for Hypersonic Vehicle’s Ground Thermal Environment

Lv,

Zhang,

Bai

et al. 2023

Aerospace

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In this paper, an adaptive neural network global fractional order fast terminal sliding mode model-free intelligent PID control strategy (termed as TDE-ANNGFOFTSMC-MFIPIDC) is proposed for the hypersonic vehicle ground thermal environment simulation test device (GTESTD). Firstly, the mathematical model of the GTESTD is transformed into an ultra-local model to ensure that the control strategy design process does not rely on the potentially inaccurate dynamic GTESTD model. Meanwhile, time delay estimation (TDE) is employed to estimate the unknown terms of the ultra-local model. Next, a global fractional-order fast terminal sliding mode surface (GFOFTSMS) is introduced to effectively reduce the estimation error generated by TDE. It also eliminates arrival time, accelerates the convergence speed of the sliding phase, guarantees finite time arrival, avoids the singularity phenomenon, and bolsters robustness. Then, as the upper bound of the disturbance error is unknown, an adaptive neural network (ANN) control is designed to approximate the upper bound of the estimation error closely and mitigate the chattering phenomenon. Furthermore, the stability of the control system and the convergence time are proven by the Lyapunov stability theorem and are calculated, respectively. Finally, simulation results are conducted to validate the efficacy of the proposed control strategy.

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Section: Mathematical Modelmentioning

confidence: 99%

Adaptive Neural Network Global Fractional Order Fast Terminal Sliding Mode Model-Free Intelligent PID Control for Hypersonic Vehicle’s Ground Thermal Environment

Lv,

Zhang,

Bai

et al. 2023

Aerospace

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A Review on Data-Driven Model-Free Sliding Mode Control

Castellanos-Cárdenas,

Posada,

Orozco-Duque

et al. 2024

Algorithms

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Sliding mode control (SMC) has been widely used to control linear and nonlinear dynamics systems because of its robustness against parametric uncertainties and matched disturbances. Although SMC design has traditionally addressed process model-based approaches, the rapid advancements in instrumentation and control systems driven by Industry 4.0, coupled with the increased complexity of the controlled processes, have led to the growing acceptance of controllers based on data-driven techniques. This review article aims to explore the landscape of SMC, focusing specifically on data-driven techniques through a comprehensive systematic literature review that includes a bibliometric analysis of relevant documents and a cumulative production model to estimate the deceleration point of the scientific production of this topic. The most used SMC schemes and their integration with data-driven techniques and intelligent algorithms, including identifying the leading applications, are presented.

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Numerical Analyses and a Nonlinear Composite Controller for a Real-Time Ground Aerodynamic Heating Simulation of a Hypersonic Flying Object

Zhang

Wang

et al. 2022

Mathematics

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This paper contains two parts: numerical analyses and a control method. The numerical analyses of a hypersonic flying object’s aerodynamic heating environment are based on three different two-dimensional outflow fields via finite element calculations. Then, the reference temperature trajectories of a hypersonic flying object are obtained. The other one is an intelligent proportional-derivative (IPD) with a nonlinear global sliding mode control (NGSMC) based on a nonlinear extended state observer (NESO) for a real-time ground aerodynamic heating simulation of a hypersonic flying object, named a thermal-structural test with quartz lamp heaters. The composite controller is made of three sub-components: a model free frame that is independent of the system dynamic model along with an ultra-local model; a NESO for the lumped disturbances observation; and an integral sliding mode control with a nonlinear function for the observation errors compensation. The flight environment of the hypersonic flying object is from Mach number 0.6 to Mach number 5.0, with between flight altitude of 31,272 m and flight altitude of 13,577 m. The comparative results demonstrate some superiorities of the proposed composite controller in terms of tracking errors and robustness.

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Aerodynamic Heating Ground Simulation of Hypersonic Vehicles Based on Model-Free Control Using Super Twisting Nonlinear Fractional Order Sliding Mode

Cited by 3 publications

References 35 publications

Adaptive Neural Network Global Fractional Order Fast Terminal Sliding Mode Model-Free Intelligent PID Control for Hypersonic Vehicle’s Ground Thermal Environment

Adaptive Neural Network Global Fractional Order Fast Terminal Sliding Mode Model-Free Intelligent PID Control for Hypersonic Vehicle’s Ground Thermal Environment

A Review on Data-Driven Model-Free Sliding Mode Control

Numerical Analyses and a Nonlinear Composite Controller for a Real-Time Ground Aerodynamic Heating Simulation of a Hypersonic Flying Object

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