Design of adaptive switching control for hypersonic aircraft

Jiao, Xiaohong; Jiang, Ju

doi:10.1177/1687814015610465

Cited by 8 publications

(5 citation statements)

References 38 publications

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“…where the symbols with their descriptions are shown in Table 1 as follows: The symbols M yy , L, D, and T can be expressed as follows [31,32]:…”

Section: Nmv Model Analysismentioning

confidence: 99%

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Adaptive Neural Network-Based Sliding Mode Backstepping Control for Near-Space Morphing Vehicle

Huang,

Jiang,

2023

Aerospace

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In order to obtain good flight performance in the near-space morphing vehicle (NMV) cruise phase, this paper proposes an adaptive sliding mode backstepping control scheme based on a neural network, aiming at the reduction of elevator control efficiency and issues of uncertainties. Firstly, this paper analyzes the aerodynamic parameters of NMV in the states of winglet stretching and retracting during the cruise phase. Based on the above, the flight efficiency of NMV can be improved by retracting winglets in the level flight mode and stretching winglets in the altitude climbing mode. Secondly, an enhanced triple power reaching law (ETPRL) is proposed to ensure that the sliding mode control system can converge quickly and reduce chattering. Then, the sliding mode control based on ETPRL and backstepping control are combined to ensure the stability of the system, and adaptive control laws are developed to estimate and compensate for uncertainties. In addition, in face of the problem of reduced elevator control efficiency, the adaptive neural network is used to estimate and compensate for interference on the control channel to improve tracking accuracy and robustness of NMV. Finally, three sets of simulations verified the effectiveness of the proposed method.

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“…where the symbols with their descriptions are shown in Table 1 as follows: The symbols M yy , L, D, and T can be expressed as follows [31,32]:…”

Section: Nmv Model Analysismentioning

confidence: 99%

“…In order to verify the superiority of the approach law proposed in this paper, a traditional sliding mode controller [31] is used in this section to track the speed of NMV. The sliding mode surface adopted in this simulation is defined as follows [31]:…”

Section: Simulation 1: Simulation Of Reaching Lawsmentioning

confidence: 99%

Adaptive Neural Network-Based Sliding Mode Backstepping Control for Near-Space Morphing Vehicle

Huang,

Jiang,

2023

Aerospace

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“…In equation (28), it is difficult to measure the approximation error E. If a too large E has been determined, a large chattering may take place. To decrease the chatting problem, we adopt fuzzy switching method 37 to approximate e and the approximation of E is written aŝ E =b T f ð30Þ where f is the fuzzy vector andb adjusts with adaptive law.…”

Section: Controller Designmentioning

confidence: 99%

Single-input adaptive fuzzy sliding mode control of the lower extremity exoskeleton based on human–robot interaction

Jin

Zhu

et al. 2017

Advances in Mechanical Engineering

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This article introduces a human–robot interaction controller toward the lower extremity exoskeleton whose aim is to improve the tracking performance and drive the exoskeleton to shadow the wearer with less interaction force. To acquire the motion intention of the wearer, two subsystems are designed: the first is to infer the wearer is in which phase based on floor reaction force detected by a multi-sensor system installed in the sole, and the second is to infer the motion velocity based on the multi-axis force sensor and admittance model. An improved single-input fuzzy sliding mode controller is designed, and the adaptive switching controller is combined to promote the tracking performance considering system uncertainties. Adaptation laws are designed based on the Lyapunov stability theorem. Therefore, the stability of the single-input adaptive fuzzy sliding mode control can be guaranteed. Finally, the proposed methods are applied to the lower extremity exoskeleton, especially in the swing phase. Its effectiveness is validated by comparative experiments.

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“…Hypersonic vehicles are difficult to control due to their fast time variation, dynamic nonlinearity, strong coupling, and model uncertainty. The characteristics of the nonlinear dynamic model place high demands on the trajectory tracking control [1][2][3][4]. To ensure stable flight under complex constraints, the control system must have fast response and antidisturbance properties [3].…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Predictive Control with Sliding Mode for Hypersonic Vehicle

Bai

Xie

et al. 2023

International Journal of Aerospace Engineering

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Hypersonic vehicles are difficult to control due to their rapid time variation, dynamic nonlinearity, strong coupling, and model uncertainty. This paper proposes a new nonlinear predictive controller to solve the problem. An improved model predictive controller is used to improve the dynamic control performance of hypersonic vehicles by converting nonlinear dynamics into a state-dependent linear model. The sliding surface can significantly increase the speed of convergence. The radial basis function is used to reduce the influence of system uncertainty. The stability of the proposed controller is analyzed based on the Lyapunov approach. The comparison of simulation results verifies the excellent control performance of the proposed method both in convergence speed and antidisturbance ability.

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Design of adaptive switching control for hypersonic aircraft

Cited by 8 publications

References 38 publications

Adaptive Neural Network-Based Sliding Mode Backstepping Control for Near-Space Morphing Vehicle

Adaptive Neural Network-Based Sliding Mode Backstepping Control for Near-Space Morphing Vehicle

Single-input adaptive fuzzy sliding mode control of the lower extremity exoskeleton based on human–robot interaction

Nonlinear Predictive Control with Sliding Mode for Hypersonic Vehicle

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