自主学习干扰观测器驱动的重复使用运载器再入段滑模控制
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摘要
针对重复使用运载器(RLV)再入段的姿态控制问题,设计一种具有自主学习干扰观测器(SLDO)的滑模控制器。基于奇异摄动理论及时标分离原则,将RLV的姿态动力学方程划分为外环和内环子系统。根据RLV再入段模型不确定性和外部干扰均随时间变化、不可忽略且无法预知边界等特点,结合2型模糊神经结构、误差反馈学习架构以及滑模控制(SMC)理论,提出一种新型在线自主学习干扰观测器。设计基于SLDO驱动的多元超螺旋滑模控制器,完成对再入段姿态的跟踪。最后,针对6自由度RLV模型进行了仿真分析,仿真结果证明了控制方法的有效性以及鲁棒性。
A sliding mode controller based on self-learning disturbance observer( SLDO) is designed for a reusable launch vehicle( RLV). According to the singular perturbation theory,a RLV dynamic model is divided into the outer-loop and inner-loop subsystems. Since the model uncertainties and the disturbances vary with time and have unknown boundaries,combining with the type-2 neuro-fuzzy structure,feedback-error learning scheme and sliding mode control( SMC) theory,a novel online SLDO is presented. The multivariable supertwisting sliding mode controller driven by a SLDO is designed to track the re-entry trajectory precisely and convergent rapidly. Finally,by the simulation and analysis of the 6-degree-of-freedom model of RLV in the reentry phase,the effectiveness and the robustness of the integrated control scheme are verified.
A sliding mode controller based on self-learning disturbance observer( SLDO) is designed for a reusable launch vehicle( RLV). According to the singular perturbation theory,a RLV dynamic model is divided into the outer-loop and inner-loop subsystems. Since the model uncertainties and the disturbances vary with time and have unknown boundaries,combining with the type-2 neuro-fuzzy structure,feedback-error learning scheme and sliding mode control( SMC) theory,a novel online SLDO is presented. The multivariable supertwisting sliding mode controller driven by a SLDO is designed to track the re-entry trajectory precisely and convergent rapidly. Finally,by the simulation and analysis of the 6-degree-of-freedom model of RLV in the reentry phase,the effectiveness and the robustness of the integrated control scheme are verified.
引文
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[2] Mathavaraj S,Halbe O,Padhi R. Robust control of a reusable launch vehicle in reentry phase using model following neuroadaptive design[C]. AIAA Guidance,Navigation,and Control Conference,Toronto,Canada,August 2-5,2010.
[3] Smith R, Ahmed A. Robust parametrically varying attitude controller designs for the X-33 vehicle[C]. AIAA Guidance,Navigation,and Control Conference and Exhibit,Denver,USA,August 14-17,2000.
[4]胡军.高超声速飞行器非线性自适应姿态控制[J].宇航学报,2017,38(12):1281-1288.[Hu Jun. The nonlinear adaptive attitude control for hypersonic vehicle[J]. Journal of Astronautics,2017,38(12):1281-1288.]
[5] Johnson E N,Calise A J. Limited authority adaptive flight control for reusable launch vehicles[J]. Journal of Guidance Control&Dynamics,2012,26(6):906-913.
[6]甄子洋,朱平,江驹,等.基于自适应控制的近空间高超声速飞行器研究进展[J].宇航学报,2018,39(4):355-367.[Research progress of adaptive control for hypersonic vehicle in near space[J]. Journal of Astronautics,2018,39(4):355-367.]
[7] Zong Q, Wang F, Tian B L. Nonlinear adaptive filter backstepping flight control for reentry vehicle with input constraint and external disturbances[J]. Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering,2014,228(6):889-907.
[8] Yang J,Li S,Yu X. Sliding-mode control for systems with mismatched uncertainties via a disturbance observer[J]. IEEE Transactions on Industrial Electronics,2013,60(1):160-169.
[9] Chen W H. Nonlinear disturbance observer-enhanced dynamic inversion control of missiles[J]. Journal of Guidance Control&Dynamics,2003,26(1):161-166.
[10] Kayacan E, Peschel J M, Chowdhary G. A self-learning disturbance observer for nonlinear systems in feedback-error learning scheme[J]. Engineering Applications of Artificial Intelligence,2017,62:276-285.
[11] Nagesh I,Edwards C. A multivariable super-twisting sliding mode approach[J]. Automatica,2014,50(3):984-988.
[12] Dong Q, Zong Q, Tian B, et al. Integrated finite-time disturbance observer and controller design for reusable launch vehicle in reentry phase[J]. Journal of Aerospace Engineering,2016,30(1):04016076.
[2] Mathavaraj S,Halbe O,Padhi R. Robust control of a reusable launch vehicle in reentry phase using model following neuroadaptive design[C]. AIAA Guidance,Navigation,and Control Conference,Toronto,Canada,August 2-5,2010.
[3] Smith R, Ahmed A. Robust parametrically varying attitude controller designs for the X-33 vehicle[C]. AIAA Guidance,Navigation,and Control Conference and Exhibit,Denver,USA,August 14-17,2000.
[4]胡军.高超声速飞行器非线性自适应姿态控制[J].宇航学报,2017,38(12):1281-1288.[Hu Jun. The nonlinear adaptive attitude control for hypersonic vehicle[J]. Journal of Astronautics,2017,38(12):1281-1288.]
[5] Johnson E N,Calise A J. Limited authority adaptive flight control for reusable launch vehicles[J]. Journal of Guidance Control&Dynamics,2012,26(6):906-913.
[6]甄子洋,朱平,江驹,等.基于自适应控制的近空间高超声速飞行器研究进展[J].宇航学报,2018,39(4):355-367.[Research progress of adaptive control for hypersonic vehicle in near space[J]. Journal of Astronautics,2018,39(4):355-367.]
[7] Zong Q, Wang F, Tian B L. Nonlinear adaptive filter backstepping flight control for reentry vehicle with input constraint and external disturbances[J]. Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering,2014,228(6):889-907.
[8] Yang J,Li S,Yu X. Sliding-mode control for systems with mismatched uncertainties via a disturbance observer[J]. IEEE Transactions on Industrial Electronics,2013,60(1):160-169.
[9] Chen W H. Nonlinear disturbance observer-enhanced dynamic inversion control of missiles[J]. Journal of Guidance Control&Dynamics,2003,26(1):161-166.
[10] Kayacan E, Peschel J M, Chowdhary G. A self-learning disturbance observer for nonlinear systems in feedback-error learning scheme[J]. Engineering Applications of Artificial Intelligence,2017,62:276-285.
[11] Nagesh I,Edwards C. A multivariable super-twisting sliding mode approach[J]. Automatica,2014,50(3):984-988.
[12] Dong Q, Zong Q, Tian B, et al. Integrated finite-time disturbance observer and controller design for reusable launch vehicle in reentry phase[J]. Journal of Aerospace Engineering,2016,30(1):04016076.