Predictive Sliding Mode Control for Attitude Tracking of Hypersonic Vehicles Using Fuzzy Disturbance Observer

Fan³

et al. 2020

IEEE Access

The control system design for the reusable launch vehicles (RLVs), especially in the autonomous horizontal takeoff phase, is a highly challenging task. Significant issues arise due to the high nonlinearity, large uncertainties of aerodynamic coefficients as well as strong coupling among axes of the airframe. This paper studies autonomous takeoff control problem of the RLVs by the means of trajectory linearization control (TLC) and model predictive control (MPC) theory. The six degree of freedom dynamic model is firstly established, and the flight strategy of takeoff and climb stage is provided through the characteristic analysis of RLVs. Furthermore, the guidance law for the climbing phase is proposed via the TLC method against the high nonlinearity, and a speed based gain-schedule strategy is given under the consideration of both aerodynamic force and friction force. In order to eliminate the ground effect interference, an improved model predictive control approach is presented by introducing the online parameter estimation of the ground effect interaction coefficient, and a coupled model predictive controller is designed by introducing the feedback of sideslip angle into the roll control channel to eliminate the coupling effect. Finally, the performance of the design method for autonomous takeoff control of RLVs is demonstrated through the comparison simulation analysis.

“…(1) MPC modeling With the help of Taylor's expansion, the following longitudinal discrete prediction model can be derived from attitude dynamic model of the RLVs [26][27][28].…”

Section: A Design Of Longitudinal Attitude Stability Control Law Basmentioning

confidence: 99%

“…Q denotes weight coefficient to reflect the importance level of tracking error and consumed control energy, and n denotes the maximum predicted length at one time. Longitudinal MPC strategyConsidering of the optimization problem of the proposed performance index(27), the required MPC input can be obtained by the minimal principle:…”

mentioning

confidence: 99%

Control Design for the Autonomous Horizontal Takeoff Phase of the Reusable Launch Vehicles

Fan³

et al. 2020

IEEE Access

“…Correspondingly, the closed-loop stability and robustness of a dynamic-inversion flight controller for reentry vehicles were quantified in consideration of the influence along with the different flight dynamics. In addition, a methodology was presented using a combination of the linear dynamic-inversion controller and adaptive filter in order to implement MIMO reconfigurable flight control [6]. Such control design could improve significantly the tracking performance, handling qualities, and PIO tendencies for the closed system.…”

Section: Introductionmentioning

confidence: 99%

Neural-Based Compensation of Nonlinearities in an Airplane Longitudinal Model with Dynamic-Inversion Control

Computational Intelligence and Neuroscience

Feiteng

2017

The inversion design approach is a very useful tool for the complex multiple-input-multiple-output nonlinear systems to implement the decoupling control goal, such as the airplane model and spacecraft model. In this work, the flight control law is proposed using the neural-based inversion design method associated with the nonlinear compensation for a general longitudinal model of the airplane. First, the nonlinear mathematic model is converted to the equivalent linear model based on the feedback linearization theory. Then, the flight control law integrated with this inversion model is developed to stabilize the nonlinear system and relieve the coupling effect. Afterwards, the inversion control combined with the neural network and nonlinear portion is presented to improve the transient performance and attenuate the uncertain effects on both external disturbances and model errors. Finally, the simulation results demonstrate the effectiveness of this controller.

“…Using nonlinear disturbance observer, M. Chen and W.-H. Chen [38] developed a sliding mode control to avoid chattering and stabilize a class of nonlinear systems. In [39], an improved fuzzy disturbance observer based predictive sliding mode control was presented to enhance composite disturbance rejection performance and eliminate chattering. However, observer-based sliding mode control requires the transient response of the observer should be significantly shorter than system's transient response.…”

Section: Introductionmentioning

confidence: 99%

Chattering-Free Time Scale Separation Sliding Mode Control Design with Application to Power System Chaos Suppression

Mathematical Problems in Engineering

et al. 2016

This paper presents a novel chattering-free sliding mode control method for a class of disturbed nonlinear systems, which achieves fast and exact disturbance estimation, eliminates chattering, and recovers the performance of nominal system and nominal control input. The proposed approach combines time scale separation design and sliding mode control. Different from the existing disturbance estimation based sliding mode control methods, the proposed scheme achieves fast and exact disturbance estimation through time scale separation and eliminates discontinuous switching term, thereby achieving good chattering alleviation effect and providing good transient response. The proposed control method is applied to suppress chaos in power system and simulation results confirm the effectiveness and robustness of proposed control scheme and highlight the advantages of the proposed control scheme over the existing disturbance estimation based sliding mode control methods in terms of chattering alleviation effect and transient response.