基于SMDO-NGPC的无人机姿态控制律设计
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摘要
针对无人机在飞行过程中存在的强非线性、快时变、强耦合,以及参数不确定和外部干扰情况下的鲁棒性差等姿态控制问题,采用了基于滑模干扰观测器的非线性广义预测控制算法对姿态控制律进行了设计。该方法融合了非线性广义预测控制算法的良好动态性和滑模干扰观测器的强鲁棒性,将无人机的姿态分为快、慢两个回路并分别将该方法应用到两个回路的控制律设计中,最后设计出快、慢回路控制律表达式及滑模干扰观测器模型。仿真结果表明:基于滑模干扰观测器的非线性广义预测控制算法的无人机姿态控制律能够克服外界干扰及气动参数大范围摄动的影响,具有良好的控制性能和抗干扰能力,可以更好地适应无人机快时变、高精度和强鲁棒的控制要求。
Aiming at the problem of strong nonlinearity,fast time-varying,strong coupling,and poor robustness under uncertain parameters and external disturbances,a nonlinear generalized predictive control algorithm based on sliding mode disturbance observer is used to design the attitude control law for UAV.This method combines the good dynamics of NGPC with the strong robustness of SMDO. The attitude of UAV is divided into two loops: fast loop and slow loop. The method is applied to the control law design of two loops respectively. Finally,the fast loop and slow loop control law expressions and sliding mode disturbance observers are designed. The simulation results show that the attitude control law of UAV based on sliding mode disturbance observer and nonlinear generalized predictive control algorithm can overcome the influence of external disturbance and large range perturbation of aerodynamic parameters,and has good control performance and anti-interference ability,which can better adapt to the requirements of UAV fast time-varying,high-precision and robust control.
Aiming at the problem of strong nonlinearity,fast time-varying,strong coupling,and poor robustness under uncertain parameters and external disturbances,a nonlinear generalized predictive control algorithm based on sliding mode disturbance observer is used to design the attitude control law for UAV.This method combines the good dynamics of NGPC with the strong robustness of SMDO. The attitude of UAV is divided into two loops: fast loop and slow loop. The method is applied to the control law design of two loops respectively. Finally,the fast loop and slow loop control law expressions and sliding mode disturbance observers are designed. The simulation results show that the attitude control law of UAV based on sliding mode disturbance observer and nonlinear generalized predictive control algorithm can overcome the influence of external disturbance and large range perturbation of aerodynamic parameters,and has good control performance and anti-interference ability,which can better adapt to the requirements of UAV fast time-varying,high-precision and robust control.
引文
[1]李春涛,胡盛华.基于动态逆的无人机飞行控制律设计[J].兵工自动化,2012,(5):1-4.
[2] Zhu J J,Banker D,Hall C E. X-33 ascent flight control design by trajectory linearization-a singular perturbation approach[C]//AIAA Guidance,Navigation and Control Conference and Exhibit,Denver:AIAA,2000:1-19.
[3] Clark D W. Advances in model-based predictive control[C]. International Journal of Adaptive Control and Signal Processing,1995,(9):383-384.
[4] Chen W H,Balance D J,Gawthrop P J. Optimal control of nonlinear systems:a predictive control approach[J].Automatica,2003,39(6):633-641.
[5]朱亮,姜长生.基于非线性干扰观测器的空天飞行器轨迹线性化控制[J].南京航空航天大学学报,2007,39(4):490-495.
[6]宫庆坤,姜长生,吴庆宪,等.基于滑模干扰观测器的歼击机超机动飞行控制[J].电光与控制,2014,21(11):14-17.
[7]陈路,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):428-431.
[8] Isidori A,王奔,庄圣贤.非线性控制系统(第三版)[M].北京:电子工业出版社,2005.
[9]高俊,王鹏,侯中喜.基于改进PID算法的无人机变速度控制[J].华中科技大学学报(自然科学版),2015,(10):1-4.
[10]戴永伟,钱志娟,董茂科.基于神经网络的无人机飞行智能控制技术研究[J].数字技术与应用,2012,(7):10.
[11]王峰.无人机飞行运动建模与自主飞行控制技术研究[D].南京:南京航空航天大学,2008.
[2] Zhu J J,Banker D,Hall C E. X-33 ascent flight control design by trajectory linearization-a singular perturbation approach[C]//AIAA Guidance,Navigation and Control Conference and Exhibit,Denver:AIAA,2000:1-19.
[3] Clark D W. Advances in model-based predictive control[C]. International Journal of Adaptive Control and Signal Processing,1995,(9):383-384.
[4] Chen W H,Balance D J,Gawthrop P J. Optimal control of nonlinear systems:a predictive control approach[J].Automatica,2003,39(6):633-641.
[5]朱亮,姜长生.基于非线性干扰观测器的空天飞行器轨迹线性化控制[J].南京航空航天大学学报,2007,39(4):490-495.
[6]宫庆坤,姜长生,吴庆宪,等.基于滑模干扰观测器的歼击机超机动飞行控制[J].电光与控制,2014,21(11):14-17.
[7]陈路,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):428-431.
[8] Isidori A,王奔,庄圣贤.非线性控制系统(第三版)[M].北京:电子工业出版社,2005.
[9]高俊,王鹏,侯中喜.基于改进PID算法的无人机变速度控制[J].华中科技大学学报(自然科学版),2015,(10):1-4.
[10]戴永伟,钱志娟,董茂科.基于神经网络的无人机飞行智能控制技术研究[J].数字技术与应用,2012,(7):10.
[11]王峰.无人机飞行运动建模与自主飞行控制技术研究[D].南京:南京航空航天大学,2008.