Sliding mode control of the X-33 vehicle in launch and re-entry modes

Shtessel, Yuri B.; McDuffie, James; Jackson, Mark; Gallaher, M.; Krupp, D.; Hendrix, Neal D.

doi:10.2514/6.1998-4414

Cited by 74 publications

(84 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The moment of inertia tensor is given in the study by Shtessel and McDuffie 29 The derivative of o c is needed in the inner-loop controller. To avoid the explosion of complexity in calculating the derivative, the following first-order filter is applied to get the differential of o c…”

Section: Simulation Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive sliding mode fault-tolerant control for hypersonic vehicle based on radial basis function neural networks

Zhai

Jiang

2017

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

In this article, an adaptive sliding mode fault-tolerant control scheme is proposed to address the problem of robust and fast attitude tracking for a hypersonic vehicle in the presence of unknown external disturbances, additive fault and partial loss of effectiveness fault. Firstly, the healthy and faulty models of the vehicle are given. Then, a radial basis function neural network is designed to estimate the unknown additive fault, and the adaptive method is applied to deal with the unknown partial loss of effectiveness fault. Combined with the sliding mode control theory, the fault-tolerant controllers are designed for the outer and inner loops of the faulty system, respectively. The adaptive laws are designed to update parameter estimates to implement the inner-loop controller. Closed-loop stability is analysed and simulation results verify the effectiveness of the proposed fault-tolerant control scheme.

show abstract

Section: Simulation Resultsmentioning

confidence: 99%

“…29 The dynamic equations and attitude kinematics of the aircraft in its re-entry mode are given as follows…”

Section: Problem Formulationmentioning

confidence: 99%

Adaptive sliding mode fault-tolerant control for hypersonic vehicle based on radial basis function neural networks

Zhai

Jiang

2017

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

show abstract

“…[14]. Since angle of attack ( ) α and angular rate ( ) ω are key variables where nonlinearities of the system lie in, only the membership functions of α and ω are given.…”

Section: Simulation Resultsmentioning

confidence: 99%

Reentry attitude tracking control based on fuzzy feedforward for reusable launch vehicle

Wang

Wu²,

Jiang³

et al. 2009

Int. J. Control Autom. Syst.

View full text Add to dashboard Cite

This paper presents a novel fuzzy tracking control method, which is used for the reentry attitude tracking problem of a reusable launch vehicle. Based on Takagi-Sugeno fuzzy description of reentry attitude dynamics, the tracking error of attitude angles is studied. Without the need of an augmented system, exponential stability of the tracking error can be obtained by a fuzzy feedforward controller. In addition, to achieve a good tracking performance, H∞ and decay rate η control methods are considered. The solutions to the robust fuzzy tracking control problem can be obtained by applying linear matrix inequalities and fuzzy logic controller tools. The simulation results demonstrate the effectiveness of the proposed method.

show abstract

“…, Ψ = g p,δ e g p,δ a g p,δ r g p,δ x 0 0 g q,δ e g q,δ a g q,δ r 0 0 g q,δ z g r,δ e g r,δ a g r,δ r 0 g r,δ y 0 g p,δ e =qSbC l,δ e , g p,δ a =qSbC l,δ a , g p,δ r =qSbC l,δ r g q,δ e =qScC m,δ e + X cgq S(C D,δ e sin α + C L,δ e cos α) g q,δ a =qScC m,δ a + X cgq S(C D,δ a sin α + C L,δ r cos α) g q,δ r =qScC m,δ r + X cgq SC D,δ r sin α, g r,δ e =qSbC n,δ e + X cgq SC Y,δ e g r,δ a =qSbC n,δ a + X cgq SC Y,δ a , g r,δ r =qSbC n,δ r + X cgq SC Y,δ r where the matrix Ψ is the control allocation of control torque to the control surface, and for more detailed aerodynamic parameters one can be referred to [13,18]. The NSV attitude model (40) with system fault ρ(x) can be written in the following form, which is similar to (1):…”

Section: Simulation Resultsmentioning

confidence: 99%

“…The flight control system of NSV operates through a wide range of flight conditions. Highly coupled control channels, the existence of environmental disturbances and poorly understood aerodynamic qualities complicate the control design process [13,18]. The dynamic equations of NSV are time-varying and highly nonlinear, and they are affected by insufficient knowledge of the system parameters, such as the inertia matrix, which are usually not fully known.…”

Section: Introductionmentioning

confidence: 99%

Fault Tolerant Control for a Class of Nonlinear Systems with Application to Near Space Vehicle

Jiang

Tao

et al. 2010

Circuits Syst Signal Process

View full text Add to dashboard Cite

In this paper, a fault tolerant control (FTC) scheme, which is based on backstepping and neural network (NN) methodology, is proposed for a general class of nonlinear systems with known structure and unknown faults. Firstly, the linearly parameterized radial basis function (RBF) NNs are employed to approximate unknown system faults, and the network weights are adapted using adaptive on-line parameter-learning algorithms. Then an adaptive backstepping based FTC is designed to compensate for the effect of system faults. The asymptotical stability of the closedloop system and uniform boundedness of the state tracking errors are proved according to Lyapunov theory. Finally, the designed strategy is applied to near space vehicle (NSV) attitude dynamics, and simulation results are presented to demonstrate the effectiveness of the proposed approach.

show abstract

Sliding mode control of the X-33 vehicle in launch and re-entry modes

Cited by 74 publications

References 7 publications

Adaptive sliding mode fault-tolerant control for hypersonic vehicle based on radial basis function neural networks

Adaptive sliding mode fault-tolerant control for hypersonic vehicle based on radial basis function neural networks

Reentry attitude tracking control based on fuzzy feedforward for reusable launch vehicle

Fault Tolerant Control for a Class of Nonlinear Systems with Application to Near Space Vehicle

Contact Info

Product

Resources

About