Multi-objective robust control based on fuzzy singularly perturbed models for hypersonic vehicles

Hu, Yunfeng; Yuan, Yuan; Min, He; Sun, Fuchun

doi:10.1007/s11432-011-4201-3

Cited by 19 publications

(13 citation statements)

References 11 publications

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“…The 2 and generalized 2 norms were used as the system performance indexes in [32], and multi-objective controller of active suspension system was designed to improve ride comfort and handling stability performances. The 2 / ∞ robust multi-objective controller was designed in [33] to enable the aircraft to better track the desired speed and desired attitude angle. At present, the multi-objective control method based on above mentioned multi-objective control architecture is widely used in aircraft attitude control, vehicle suspension control and mobile robot motion control, but less used in vehicle trajectory tracking control.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Comprehensive Control of Trajectory Tracking for Four-In-Wheel-Motor Drive Electric Vehicle With Differential Steering

Wang

Cui

et al. 2021

IEEE Access

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For 4MIDEV with differential steering, the trajectory tracking multi-objective control method is studied. The dynamics model of the four-in-wheel motor independent-drive vehicle (4MIDEV) is established comprehensively considering the differential steering dynamics characteristics, longitudinal dynamics characteristics, and lateral dynamics characteristics. Takagi Sugeno (T-S) fuzzy method is adopted to deal with the nonlinear and time-varying characteristics of the model parameters such as vehicle longitudinal speed and the front steering wheel angle. The trajectory tracking control targets are divided into the kinematic control target considering the trajectory tracking accuracy and the dynamics control target considering longitudinal speed tracking as well as lateral stability performance. The generalized 2 norm and ∞ norm are selected to constrain the kinematic control target and the dynamics control target respectively. The generalized 2 / ∞ trajectory tracking controller is designed to improve the trajectory tracking accuracy and longitudinal speed tracking as well as lateral stability performance. The hardware in the loop test (HIL) verified that the designed trajectory tracking multi-objective controller can improve the comprehensive performance of vehicle and balance the trajectory tracking accuracy between the longitudinal and lateral dynamics performance.

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Section: Introductionmentioning

confidence: 99%

Multi-Objective Comprehensive Control of Trajectory Tracking for Four-In-Wheel-Motor Drive Electric Vehicle With Differential Steering

Wang

Cui

et al. 2021

IEEE Access

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“…Note that fuzzy model can be employed to approximate non-linear system with any specified accuracy [6]. Now many scholars are paid attention to designing controllers for AHVs using fuzzy model [7][8][9]. To obtain better control performance, numerous control methods are utilised non-linear model to design controllers, for example, robust control [10,11], adaptive control [12][13][14], sliding mode control [15][16][17], neural-network control [18][19][20][21][22], kriging estimator [23], sum-of-squares method [24] and so on.…”

Section: Introductionmentioning

confidence: 99%

Non‐linear disturbance observer‐based back‐stepping control for airbreathing hypersonic vehicles with mismatched disturbances

Sun

Yang

et al. 2014

IET Control Theory & Applications

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This study concerns with robust tracking control problem for the longitudinal model of airbreathing hypersonic vehicles (AHVs). The AHVs include serious non-linearities, strong couplings, parametric perturbations and mismatched disturbances, which results in great difficultly in the controller design. By using back-stepping method and non-linear disturbance observer technique, a novel composite controller is proposed, which can guarantee system outputs asymptotically track their reference signals. A new idea is that disturbance estimations are introduced into virtual control law in each step to compensate the mismatched disturbances. As compared with other robust flight control methods for AHVs, the developed method exhibits not only excellent robustness and disturbance rejection performance but also the property of nominal performance recovery. Finally, the effectiveness of the proposed method is demonstrated by simulation results.

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“…As a key technology to make hypersonic flight available, control design is vital for the task and numerous researches are constructed on the related domain [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Neural control of hypersonic flight dynamics with actuator fault and constraint

Wang

Zhang

Jin

et al. 2015

Sci. China Inf. Sci.

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This paper deals with the control problem of actuator fault and saturation for hypersonic flight vehicles. Different from previous back-stepping design, the scheme is on transforming the dynamics into the "prediction function". The controller is constructed with high gain observer, while the effect of fault and saturation is compensated by neural networks. For the input saturation, the auxiliary dynamics is included to design the adaptive learning law. The neural weights and filtered tracking error are guaranteed to be bounded via Lyapunov approach. The effectiveness of the proposed method is verified by simulation of winged-cone model. et al. Neural control of hypersonic flight dynamics with actuator fault and constraint. Sci China Inf Sci, 2015, 58: 070206(10),

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Multi-objective robust control based on fuzzy singularly perturbed models for hypersonic vehicles

Cited by 19 publications

References 11 publications

Multi-Objective Comprehensive Control of Trajectory Tracking for Four-In-Wheel-Motor Drive Electric Vehicle With Differential Steering

Multi-Objective Comprehensive Control of Trajectory Tracking for Four-In-Wheel-Motor Drive Electric Vehicle With Differential Steering

Non‐linear disturbance observer‐based back‐stepping control for airbreathing hypersonic vehicles with mismatched disturbances

Neural control of hypersonic flight dynamics with actuator fault and constraint

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