考虑跟踪性能约束的近空间飞行器控制器设计
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摘要
针对一类可变后掠角的近空间飞行器(NSV)指令跟踪问题,考虑其受到外界扰动及参数不确定的影响,同时考虑系统的跟踪性能约束及姿态角速度约束,提出了一种基于模糊系统的切换控制器设计方法,确保系统在扰动影响以及给定约束下能够对给定信号进行稳定跟踪。建立了包含未知扰动及不确定项的近空间飞行器非线性切换模型;通过设计模糊系统对系统所受的总干扰进行实时估计,并基于反步法进行了切换控制器设计,在控制器中对干扰进行补偿;通过公共Barrier Lyapunov方法对系统稳定性及动态性能进行了分析。数值仿真算例校验了所提出方法的有效性及优越性。
A method for designing a switched controller based on a fuzzy logic system is proposed in this paper for a class of the variable-sweepback-angle near space vehicles(NSV) affected by the external disturbances and parameter uncertainties under the tracking performance and angular rate constraints. The nonlinear switching model of the vehicles containing the unknown disturbances and uncertainties is established. By designing a fuzzy logic system, all the unknown nonlinearities can be real-time estimated. Based on the back-stepping method, a switched controller is designed and the "total disturbances" are compensated in the controller. By means of the common Barrier Lyapunov function theory, it is proved that the proposed control strategy can guarantee the stability of the close-loop switching system and the tracking performance and state constraints are not violated. The numerical examples show the effectiveness and advantages of the proposed method
A method for designing a switched controller based on a fuzzy logic system is proposed in this paper for a class of the variable-sweepback-angle near space vehicles(NSV) affected by the external disturbances and parameter uncertainties under the tracking performance and angular rate constraints. The nonlinear switching model of the vehicles containing the unknown disturbances and uncertainties is established. By designing a fuzzy logic system, all the unknown nonlinearities can be real-time estimated. Based on the back-stepping method, a switched controller is designed and the "total disturbances" are compensated in the controller. By means of the common Barrier Lyapunov function theory, it is proved that the proposed control strategy can guarantee the stability of the close-loop switching system and the tracking performance and state constraints are not violated. The numerical examples show the effectiveness and advantages of the proposed method
引文
[1] 孙长银,穆朝絮,余瑶. 近空间高超声速飞行器控制的几个科学问题研究[J]. 自动化学报, 2013, 9(11):1901-1913. [Sun Chang-yin, Mu Zhao-xu, Yu Yao. Some control problems for near space hypersonic vehicles[J]. Acta Automatica Sinica, 2013, 9(11): 1901-1913.]
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[10] 蒲明,吴庆宪,姜长生,等. 基于二阶动态Terminal滑模的近空间飞行器控制[J]. 宇航学报, 2010, 31(4): 1056-1062. [Pu Ming, Wu Qing-xian, Jiang Chang-sheng, et al. Near space vehicle control based on second-order terminal sliding mode[J]. Journal of Astronautics, 2010, 31(4): 1056-1062. ]
[11] 路遥,孙友,路坤峰,等. 近空间飞行器输入饱和抑制模糊自适应控制器设计[J]. 宇航学报, 2018, 39(9): 986-994. [Lu Yao, Sun You, Lu Kun-feng, et al. Fuzzy adaptive control for near space hypersonic vehicles with saturation constraint of inputs[J]. Journal of Astronautics, 2018, 39(9): 986-994.]
[12] Salgado I, Mera M, Chairez I. Suboptimal adaptive control of dynamic systems with state constraints based on Barrier Lyapnov functions[J]. IET Control Theory & Applications, 2018, 12(8): 1116-1124.
[13] Tee K P, Ren B B, Ge S S. Control of nonlinear systems with time-varying output constraints[J]. Automatica, 2011, 47: 2511-2516.
[14] An H, Xia H W, Wang C H. Barrier Lyapnov function-based adaptive control for hypersonic flight vehicle[J]. Nonlinear Dynamic, 2017, 88(3): 1-21.
[15] Ma L, Li D P. Adaptive neural networks control using Barrier Lyapnov functions for DC motor system with time-varying state constraints[J]. 2018, DOI: https:// doi.org/10.1155/2018/5082401.
[16] Wang L X. Adaptive fuzzy systems and control[M]. Englewood Cliffs, New Jersey, USA: Prentice-Hall, 1994.
[2] Schmidt D K. Modeling and near-space stationkeeping control of a large high-altitude airship[J]. Journal of Guidance, Control and Dynamic, 2007, 30(2): 540-547.
[3] Kurt D H, Lieutentan C. Near space: should air force space command take control of its shore?[M]. Alabama: Air Force Press, 2006.
[4] 张军,姜长生,方炜. 变结构近空间飞行器大飞行包络控制特性研究[J]. 宇航学报, 2009, 30(2):543-549. [Zhang Jun, Jiang Chang-sheng, Fang Wei. Variable structure near space vehicle control characteristics of large flight envelope[J]. Journal of Astronautics, 2009, 30(2): 543-549.]
[5] Jiang B, Gao Z F, Shi P, et al. Adaptive fault-tolerant tracking control of near-space vehicle using takagi-sugeno fuzzy models[J]. IEEE Transactions on Fuzzy Systems, 2010, 18(5): 1000-1007.
[6] Gao Z F, Jiang B, Qi R Y, et al. Robust reliable control for a near space vehicle with parametric uncertainties and actuator faults[J]. International Journal of System Science, 2011, 42(12): 2113-2124.
[7] Wang Y F, Jiang C S, Wu Q X. Attitude tracking control for variable structure near space vehicles based on switched nonlinear systems[J]. Chinese Journal of Aeronautics, 2013, 26(1): 186-193.
[8] 路遥,董朝阳,王青,等. 近空间飞行器变增益非线性切换控制器设计[J]. 控制与决策, 2017, 32(4): 613-618. [Lu Yao, Dong Chao-yang, Wang Qing, et al. Variable gain nonlinear switching controller design for near space vehicle[J]. Control and Decision, 2017, 32(4): 613-618.]
[9] 程路,姜长生,都延丽,等. 基于滑模干扰观测器的近空间飞行器非线性广义预测控制[J]. 宇航学报, 2010, 31(2): 423-431. [Cheng L, Jiang C S, Du Y L, et al. The research of SMDO based NGPC method for NSV control system[J]. Journal of Astronautics, 2010, 31(2): 423-431.]
[10] 蒲明,吴庆宪,姜长生,等. 基于二阶动态Terminal滑模的近空间飞行器控制[J]. 宇航学报, 2010, 31(4): 1056-1062. [Pu Ming, Wu Qing-xian, Jiang Chang-sheng, et al. Near space vehicle control based on second-order terminal sliding mode[J]. Journal of Astronautics, 2010, 31(4): 1056-1062. ]
[11] 路遥,孙友,路坤峰,等. 近空间飞行器输入饱和抑制模糊自适应控制器设计[J]. 宇航学报, 2018, 39(9): 986-994. [Lu Yao, Sun You, Lu Kun-feng, et al. Fuzzy adaptive control for near space hypersonic vehicles with saturation constraint of inputs[J]. Journal of Astronautics, 2018, 39(9): 986-994.]
[12] Salgado I, Mera M, Chairez I. Suboptimal adaptive control of dynamic systems with state constraints based on Barrier Lyapnov functions[J]. IET Control Theory & Applications, 2018, 12(8): 1116-1124.
[13] Tee K P, Ren B B, Ge S S. Control of nonlinear systems with time-varying output constraints[J]. Automatica, 2011, 47: 2511-2516.
[14] An H, Xia H W, Wang C H. Barrier Lyapnov function-based adaptive control for hypersonic flight vehicle[J]. Nonlinear Dynamic, 2017, 88(3): 1-21.
[15] Ma L, Li D P. Adaptive neural networks control using Barrier Lyapnov functions for DC motor system with time-varying state constraints[J]. 2018, DOI: https:// doi.org/10.1155/2018/5082401.
[16] Wang L X. Adaptive fuzzy systems and control[M]. Englewood Cliffs, New Jersey, USA: Prentice-Hall, 1994.