Integrated guidance and control for reusable launch vehicle in reentry phase

Tian, Bailing; Fan, Wenru; Zong, Qun

doi:10.1007/s11071-014-1877-0

Cited by 58 publications

(40 citation statements)

References 47 publications

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“…From the simulation results, we can see that there occurs a large overshoot process in a remarkably short period of time, and then the control torques are stable and change smoothly with the same magnitude as the external disturbance (

\overset{κ}{true} = 10^{2}

). Finally, compared with the references , we can draw a conclusion that the controller proposed in this paper can track the guidance commands more rapidly with smaller control moments when considering the same model uncertainty and additive external disturbances with the magnitude

\overset{κ}{true} = 10^{6}

and

\overset{κ}{true} = 10^{2}

, respectively.…”

Section: Simulation Resultsmentioning

confidence: 88%

“…In the meantime, to verify the validity and good stability of the designed AMSTW controller, the curves of the control moments (2000 s) and their local curves (before 15 s) are provided in Figure . It can be seen from Figure that the magnitude of control torques is the same as Δ d (

\overset{κ}{true} = 10^{6}

), and the behaviors of moments change smoothly without chattering during the reentry phase.Case In this case, the simulation results with

\overset{κ}{true} = 10^{2}

are added as to demonstrate the effectiveness of the proposed control method more sufficiently and reasonably. The simulation results of attitude tracking and control moments are provided in Figures .…”

Section: Simulation Resultsmentioning

confidence: 96%

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Adaptive-gain multivariable super-twisting sliding mode control for reentry RLV with torque perturbation

Dong

Zong

Tian

et al. 2016

Int. J. Robust. Nonlinear Control

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Summary Reusable launch vehicle (RLV) should be under control in the presence of model uncertainty and external disturbance, which is considered as torque perturbation in this paper during the reentry phase. Such a challenge imposes tight requirements to the enhanced robustness and accuracy of the vehicle autopilot. The key of this paper is to propose an adaptive‐gain multivariable super‐twisting sliding mode controller when considering that the bounds of uncertainty and perturbation are not known. The important features of the controller are that the adaptation algorithm can achieve non‐overestimating values of the control gains and the multivariable super‐twisting sliding mode approach can obtain a more elegant solution in finite time. According to the multiple‐time scale features, the dynamics of RLV attitude motion are divided into outer‐loop subsystem and inner‐loop subsystem. On this basis, the controllers are designed respectively to ensure the finite‐time reentry attitude tracking. In addition, a proof of the finite‐time convergence for the overall system is derived using the Lyapunov function technique and multiple‐time scale characteristic. Finally, simulation results of six degree‐of‐freedom RLV are provided to verify the effectiveness and robustness of the proposed controller in tracking the guidance commands as well as achieving a safe and stable reentry flight. Copyright © 2016 John Wiley & Sons, Ltd.

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\overset{κ}{true} = 10^{2}

\overset{κ}{true} = 10^{6}

and

\overset{κ}{true} = 10^{2}

, respectively.…”

Section: Simulation Resultsmentioning

confidence: 88%

\overset{κ}{true} = 10^{6}

), and the behaviors of moments change smoothly without chattering during the reentry phase.Case In this case, the simulation results with

\overset{κ}{true} = 10^{2}

Section: Simulation Resultsmentioning

confidence: 96%

Adaptive-gain multivariable super-twisting sliding mode control for reentry RLV with torque perturbation

Dong

Zong

Tian

et al. 2016

Int. J. Robust. Nonlinear Control

Self Cite

View full text Add to dashboard Cite

show abstract

“…Beside, Various techniques have been applied to hypersonic vehicles to deal with parametric uncertainties or bounded uncertainties and unmodeled system dynamics, such as the sliding model controller [13][14] and the l 1 adaptive controller [15], etc.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy disturbance observer‐based dynamic surface control for air‐breathing hypersonic vehicle with variable geometry inlets

Dou

Zong

et al. 2018

IET Control Theory & Applications

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Abstract:For an air-breathing hypersonic vehicle with a variable geometry inlet (AHV-VGI), a movable translating cowl is used to track the shock on lip conditions to capture enough air mass flow, which can extend the velocity range and be favorable to the acceleration and maneuvering flight. We firstly establish longitudinal dynamics for AHV-VGI, and consider the lumped disturbances which include the unknown external disturbance, the parameter uncertainties and the uncertainty parts introduced by translating cowl. Then, the dynamic surface control (DSC) strategy based on fuzzy disturbance observer (FDO) is proposed for AHV-VGI control. The control process for AHV-VGI is divided into two subsystems. For each subsystem, a sliding mode controller is designed, and FDOs are adopt to compensate the lumped disturbances, which can render the disturbance estimate errors convergent. Numerical simulations are presented to illustrate the effectiveness of the proposed method and the advantages of translating cowl. IntroductionAs a reliable and more cost-efficient way to access space, airbreathing hypersonic vehicle (AHV) has been investigated by many researchers in recent decades [1]. This type of vehicle has a unique design, incorporating a supersonic combustion scramjet engine located beneath the fuselage, which enable it quick response and global reach [2]. In practice, the AHV always fly within a wide flight envelope (range of flight conditions). However, for the AHV with fixed geometry inlet(AHV-FGI), once it is running at a low Mach number, the shockwave would deviate away from the scramjet lip. This leads to the scramjet engine could not get sufficient air stream, so that the thrust would be insufficient. In order to solve the above issue, AHV with variable-geometry inlet(AHV-VGI) are popularly studied. For example, NASA investigates a rotary lip VGI for the X-43A hypersonic aircraft [3]. The Space and Astronautical Science institution of Japan developed a variable geometry axisymmetric inlet for the ATREX engine [4]. Besides, the Russian scholar Kuranov investigated the Magneto Hydrodynamic controlled inlet [5].In the past decades, lots of efforts have also been put into flight control of AHV. Feedback linearization is a powerful tool to deal with the intrinsic nonlinear system [6]. The robust control of AHV is studied to improves the tracking control performance effectively [7]. To overcome the problem of system uncertainty, fuzzy logic system [8] and neural network [9] are employed due to their powerful ability of approximation for the smooth nonlinearities. Recently, transient performance-based control design [10][11] has become an important method for the research of uncertain nonlinear systems. Ref.[12] proposed a novel estimation-free prescribed performance non-affine control strategy for AHV to guarantee tracking error is limited to a predefined arbitrarily small residual set. Beside, Various techniques have been applied to hypersonic vehicles to deal with parametric uncertainties or bounded uncertainties and ...

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“…However, the corresponding NLP problems are NP-hard and have hindered a vast of research efforts in robotics [18]. A near-feasible initial guess is widely known to facilitate an NLP-solving process [22][23][24][25][26]. Herein, an initial guess refers to a guessed solution from which an optimization process starts.…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal decomposition: a knowledge-based initialization strategy for parallel parking motion optimization

Zhang

Shao

2016

Knowledge-Based Systems

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Integrated guidance and control for reusable launch vehicle in reentry phase

Cited by 58 publications

References 47 publications

Adaptive-gain multivariable super-twisting sliding mode control for reentry RLV with torque perturbation

Adaptive-gain multivariable super-twisting sliding mode control for reentry RLV with torque perturbation

Fuzzy disturbance observer‐based dynamic surface control for air‐breathing hypersonic vehicle with variable geometry inlets

Spatio-temporal decomposition: a knowledge-based initialization strategy for parallel parking motion optimization

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