小型无人直升机自主飞行控制算法研究
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摘要
小型无人直升机具有悬停、垂直起降与超低空飞行等灵活的飞行方式,因此它在军事与民用领域具有广泛的应用前景,并逐渐成为一个研究热点。然而,小型直升机系统的复杂性给其完全自主飞行带来很大的困难,同时也阻碍了它的快速发展,尤其我国的小型直升机技术研究相对于国外发达国家来说起步较晚,所以近些年来,我国科研工作者在改善小型直升机的自主飞行性能方面进行了深入探索。考虑到控制策略对自主飞行性能的重要影响,本文针对小型直升机系统中的强非线性、强耦合性、模型不确定性以及干扰等问题,提出了几种高性能的自主飞行控制算法。具体而言,本文的主要工作包括以下四个方面：
首先,设计了一种具有指数收敛性能的小型无人直升机分层控制器。传统的分层飞行控制器通常忽略了姿态响应过程,假设姿态角能够迅速到达平移控制器输出的姿态给定值,这必定对控制质量产生影响。针对该问题,本文分析了姿态响应的耦合过程,采用四元数描述了小型直升机的姿态误差,并基于这种描述方法设计了内环的姿态非线性控制器,同时也避免了姿态运动的奇异性。平移运动控制器采用反步法进行了设计。通过李雅普诺夫方法证明了该分层控制器的指数收敛性能。仿真结果也表明该分层控制器具有良好的控制性能。
第二,在考虑了主旋翼和副翼挥舞动态模型的基础上,应用反步法设计了自适应姿态与高度控制器。在实际工作时,由于不同飞行任务携带的不同负载会改变系统参数,因而需要重新调整控制器的增益,这非常不利于实际应用。为此,本文通过对小型无人直升机姿态动力学模型进行等效变换,使模型中的未知参数满足线性参数化条件,同时充分考虑主旋翼和副翼挥舞动作的影响,采用自适应策略设计了飞行控制器,较好地解决了不同工作条件下,小型无人直升机系统的质量和惯性矩阵等参数的不确定性问题。应用李雅普诺夫稳定性理论和芭芭拉引理证明了闭环控制系统的误差渐近收敛到原点处。仿真结果也表明该控制器在参数变化时依然能够保持良好的控制效果。
第三,针对测量环节中的持续干扰,设计了具有时变柔化因子的模型预测姿态通道控制算法。这种控制方法将小型直升机姿态通道的传递函数模型进行等效变换,从而得到嵌入了积分环节的状态空间模型,并以此来减小闭环系统的稳态误差。接着综合分析了姿态通道系统的初始响应速度和调节时间两个方面,设计了时变柔化因子方法,用来抑制测量环节中持续干扰对柔化轨迹的影响。基于上述内容,考虑了舵机的输出限制,在控制输入无约束和有约束两种情况下,分别设计了模型预测姿态通道控制器,并利用线性系统理论分析了闭环系统的稳定性。将所设计的控制器应用于小型直升机实验平台,实验结果表明该控制器可以加快系统响应速度,减小模型不确定性与持续干扰等因素对系统性能的影响。
第四,论文提出了一种基于非最小状态空间的姿态通道模型预测控制器。考虑到测量环节中存在持续干扰时,状态观测器性能降低的问题,本文建立了姿态通道系统的非最小状态空间模型,该模型的状态量由姿态通道的控制输入量与输出量构成。在此基础上,针对控制输入无约束和有约束两种情况,分别基于非最小状态空间模型设计了姿态通道模型预测控制器。利用线性系统理论证明了闭环系统在跟踪控制与抑制干扰方面具有良好的性能。飞行实验表明该控制算法具有良好的镇定与跟踪控制效果。
A small-scale unmanned helicopter has many specific flight modes, such as hovering, vertical take-off and landing, flying at a low altitude, and so on. Owing to its flexibility, helicopters are widely utilized in both military and civil fields. However, some difficulties in autonomous flight, which are caused by the complexity of helicopters, limit its rapid development. Especially, in contrast with the developed countries, studies on the unmanned helicopter starts late in China. Therefore, Chinese scientists have devoted much effort to enhance its autonomous flight performance. Considering the important effect of control strategies on flight performance, the dissertation proposes several high performance control algorithms for the nonlinearity, coupling, model uncertainty characteristics and disturbance of the small-scale unmanned helicopter system. Specifically, the major contribution of this work can be summarized as follows.
Firstly, a hierarchical flight controller with exponential convergent performance is designed for small-scale unmanned helicopters to deal with the coupling of the attitude dynamics and translation dynamics. The traditionally hierarchical flight controller generally ignores the attitude response process, and assumes that the attitude quickly reaches the set point developed by the translation controller, which usually reduces the control performance. In view of the above problem, the dissertation analyzes the couple process of attitude response, and describes the attitude error by the quaternion to avoid singularity. Based on the description, an attitude nonlinear controller is proposed. In addition, the translation controller is designed by utilizing the backstepping method. The Lyapunov method is employed to prove the stability of the proposed approach. Numerical simulations are included to verify its control performance.
Secondly, considering the main and aileron rotor flapping dynamics, an adaptive attitude and altitude controller is designed using the backstepping method. To achieve a satisfactory flight performance, it is important to tune suitable control gains for different flight missions in which the helicopter carries different loads. However, the tuning process is usually complicated for helicopter users. Therefore, we firstly make the unknown parameters meet the linear parametric conditions by the equivalent transformation of the attitude dynamics model. Subsequently, by analyzing the rotor flapping dynamics, an adaptive control algorithm is designed for the mass and inertia matrix unknown parameters from different loads. Lyapunov theory and Brabalat's lemma are further utilized to prove that all the closed-loop system errors converge to zero asymptotically. Simulation results show that the designed controller still maintains good control results in the presence of the system parameters variation.
Thirdly, a discrete-time model predictive attitude channel controller with the time-varying softening factor is proposed for the persistent disturbances that enter the system from the measurement components. In order to reduce the static error, we transform attitude channel transfer functions into the state space functions imbedded the integral. Then we carefully analyze the response speed and adjusting time of the attitude channels, and propose the time-varying softening factor to suppress the persistent disturbances effect on the reference trajectory. Moreover, considering the limited operation range of the helicopter rudder, the discrete-time model predictive attitude channel controllers are designed for both limited and unlimited control inputs. The stability of the close-loop system is proven by considering the fact that all the eigenvalues lie inside the unit circle. The designed controllers are then employed in a small-scale helicopter system, and the results show that it enhances the speed of the control system response, and decreases the influence of model uncertainty and persistent disturbances remarkably.
Finally, the dissertation proposes a discrete-time model predictive attitude channel controller based on the non-minimal phase state space model. In the model predictive control system with the persistent measurement disturbances, the performance of state observer is reduced. Owing to this problem, we develop the non-minimal phase state space model for the attitude channels of a small-scale helicopter, whose states consist of the attitude channel control input and output data. Then based on the non-minimal phase state space attitude channels model, the discrete-time model predictive attitude channel controllers are designed for both constrained and unconstrained control inputs. The linear system theory proves the tracking performance and anti-jamming ability of the controllers. Practical flight experiments illustrate the satisfactory performance of the proposed controllers.
首先,设计了一种具有指数收敛性能的小型无人直升机分层控制器。传统的分层飞行控制器通常忽略了姿态响应过程,假设姿态角能够迅速到达平移控制器输出的姿态给定值,这必定对控制质量产生影响。针对该问题,本文分析了姿态响应的耦合过程,采用四元数描述了小型直升机的姿态误差,并基于这种描述方法设计了内环的姿态非线性控制器,同时也避免了姿态运动的奇异性。平移运动控制器采用反步法进行了设计。通过李雅普诺夫方法证明了该分层控制器的指数收敛性能。仿真结果也表明该分层控制器具有良好的控制性能。
第二,在考虑了主旋翼和副翼挥舞动态模型的基础上,应用反步法设计了自适应姿态与高度控制器。在实际工作时,由于不同飞行任务携带的不同负载会改变系统参数,因而需要重新调整控制器的增益,这非常不利于实际应用。为此,本文通过对小型无人直升机姿态动力学模型进行等效变换,使模型中的未知参数满足线性参数化条件,同时充分考虑主旋翼和副翼挥舞动作的影响,采用自适应策略设计了飞行控制器,较好地解决了不同工作条件下,小型无人直升机系统的质量和惯性矩阵等参数的不确定性问题。应用李雅普诺夫稳定性理论和芭芭拉引理证明了闭环控制系统的误差渐近收敛到原点处。仿真结果也表明该控制器在参数变化时依然能够保持良好的控制效果。
第三,针对测量环节中的持续干扰,设计了具有时变柔化因子的模型预测姿态通道控制算法。这种控制方法将小型直升机姿态通道的传递函数模型进行等效变换,从而得到嵌入了积分环节的状态空间模型,并以此来减小闭环系统的稳态误差。接着综合分析了姿态通道系统的初始响应速度和调节时间两个方面,设计了时变柔化因子方法,用来抑制测量环节中持续干扰对柔化轨迹的影响。基于上述内容,考虑了舵机的输出限制,在控制输入无约束和有约束两种情况下,分别设计了模型预测姿态通道控制器,并利用线性系统理论分析了闭环系统的稳定性。将所设计的控制器应用于小型直升机实验平台,实验结果表明该控制器可以加快系统响应速度,减小模型不确定性与持续干扰等因素对系统性能的影响。
第四,论文提出了一种基于非最小状态空间的姿态通道模型预测控制器。考虑到测量环节中存在持续干扰时,状态观测器性能降低的问题,本文建立了姿态通道系统的非最小状态空间模型,该模型的状态量由姿态通道的控制输入量与输出量构成。在此基础上,针对控制输入无约束和有约束两种情况,分别基于非最小状态空间模型设计了姿态通道模型预测控制器。利用线性系统理论证明了闭环系统在跟踪控制与抑制干扰方面具有良好的性能。飞行实验表明该控制算法具有良好的镇定与跟踪控制效果。
A small-scale unmanned helicopter has many specific flight modes, such as hovering, vertical take-off and landing, flying at a low altitude, and so on. Owing to its flexibility, helicopters are widely utilized in both military and civil fields. However, some difficulties in autonomous flight, which are caused by the complexity of helicopters, limit its rapid development. Especially, in contrast with the developed countries, studies on the unmanned helicopter starts late in China. Therefore, Chinese scientists have devoted much effort to enhance its autonomous flight performance. Considering the important effect of control strategies on flight performance, the dissertation proposes several high performance control algorithms for the nonlinearity, coupling, model uncertainty characteristics and disturbance of the small-scale unmanned helicopter system. Specifically, the major contribution of this work can be summarized as follows.
Firstly, a hierarchical flight controller with exponential convergent performance is designed for small-scale unmanned helicopters to deal with the coupling of the attitude dynamics and translation dynamics. The traditionally hierarchical flight controller generally ignores the attitude response process, and assumes that the attitude quickly reaches the set point developed by the translation controller, which usually reduces the control performance. In view of the above problem, the dissertation analyzes the couple process of attitude response, and describes the attitude error by the quaternion to avoid singularity. Based on the description, an attitude nonlinear controller is proposed. In addition, the translation controller is designed by utilizing the backstepping method. The Lyapunov method is employed to prove the stability of the proposed approach. Numerical simulations are included to verify its control performance.
Secondly, considering the main and aileron rotor flapping dynamics, an adaptive attitude and altitude controller is designed using the backstepping method. To achieve a satisfactory flight performance, it is important to tune suitable control gains for different flight missions in which the helicopter carries different loads. However, the tuning process is usually complicated for helicopter users. Therefore, we firstly make the unknown parameters meet the linear parametric conditions by the equivalent transformation of the attitude dynamics model. Subsequently, by analyzing the rotor flapping dynamics, an adaptive control algorithm is designed for the mass and inertia matrix unknown parameters from different loads. Lyapunov theory and Brabalat's lemma are further utilized to prove that all the closed-loop system errors converge to zero asymptotically. Simulation results show that the designed controller still maintains good control results in the presence of the system parameters variation.
Thirdly, a discrete-time model predictive attitude channel controller with the time-varying softening factor is proposed for the persistent disturbances that enter the system from the measurement components. In order to reduce the static error, we transform attitude channel transfer functions into the state space functions imbedded the integral. Then we carefully analyze the response speed and adjusting time of the attitude channels, and propose the time-varying softening factor to suppress the persistent disturbances effect on the reference trajectory. Moreover, considering the limited operation range of the helicopter rudder, the discrete-time model predictive attitude channel controllers are designed for both limited and unlimited control inputs. The stability of the close-loop system is proven by considering the fact that all the eigenvalues lie inside the unit circle. The designed controllers are then employed in a small-scale helicopter system, and the results show that it enhances the speed of the control system response, and decreases the influence of model uncertainty and persistent disturbances remarkably.
Finally, the dissertation proposes a discrete-time model predictive attitude channel controller based on the non-minimal phase state space model. In the model predictive control system with the persistent measurement disturbances, the performance of state observer is reduced. Owing to this problem, we develop the non-minimal phase state space model for the attitude channels of a small-scale helicopter, whose states consist of the attitude channel control input and output data. Then based on the non-minimal phase state space attitude channels model, the discrete-time model predictive attitude channel controllers are designed for both constrained and unconstrained control inputs. The linear system theory proves the tracking performance and anti-jamming ability of the controllers. Practical flight experiments illustrate the satisfactory performance of the proposed controllers.
引文
[1]Valavanis K P. Advances in Unmanned Aerial Vehicles State of the Art and the Road to Autonomy Intelligent Systems, Control and Automation:Science and Engineering. Berlin: Springer-Verlag,2007.
[2]Nonami K, Kendoul F, Suzuki S, et al. Autonomous Flying Robots:Unmanned Aerial Vehicles and Micro Aerial Vehicles. Tokoy:Springer,2010.
[3]孙仕祺,马杰.无人机发展历程、现状及其所面临的挑战.飞行导弹,2005,25(1)：14-19.
[4]辛哲奎.基于视觉的小型无人直升机地面目标跟踪技术研究：[博士学位论文].天津：南开大学,2010.
[5]黄爱凤,邓克绪.民用无人机发展现状及关键技术.航空航天科技创新与长三角经济转型发展论坛论文集,2013：24-30.
[6]吴立新,刘平生,卢健.无人机分类研究.洪都科技,2005：1-11.
[7]邓寅喆,黄圣财,刘亮龚,等.超小型旋翼无人驾驶直升机的研究现状.机电一体化,2004,(1)：18-21.
[8]Kendoul F. Survey of Advances in Guidance, Navigation, and Control of Unmanned Rotorcraft Systems. Journal of Field Robotics,2012,29(2):315-377.
[9]Koslowski M, Kandil A A, Badreddin E. Modeling and identification of a small-scale unmanned autonomous helicopter. In:IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS),2012:2160-2165.
[10]Taamallah S, Bombois X, Van den Hof P. Optimal control for power-off landing of a small-scale helicopter a pseudospectral approach. In:American Control Conference (ACC),2012: 914-919.
[11]Raptis I A, Valavanis K P, Vachtsevanos G J. Linear tracking control for small-scale unmanned helicopters. IEEE Transactions on Control Systems Technology,2012,20(4): 995-1010.
[12]Cai G, Chen B M, Lee T H. Unmanned Rotorcraft Systems. London:Springer,2011.
[13]王赓.小型无人直升机自主飞行控制系统研究：[博士学位论文].上海：上海交通大学,2007.
[14]URL:http://iarc.angel-strike.com/
[15]Bergerman M, Amidi O, Miller J R, et al. Cascaded position and heading control of a robotic helicopter. In:IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). San Diego, CA, USA,2007:135-140.
[16]La Civita M, Papageorgibu G, Messner W C, et al. Design and flight testing of a gain-scheduled H_inf loop shaping controller for wide-envelope flight of a robotic helicopter. In: Proceedings of the American Control Conference. Denver, Colorado,2003:4195-4200.
[17]La Civita M, Papageorgiou G, Messner W C, et al. Integrated modeling and robust control for full-envelope flight of robotic helicopters. In:IEEE International Conference on Robotics and Automation (ICRA). Pittsburgh, PA, USA,2003:552-557.
[18]Mettler B. Identification Modeling and Characteristics of Miniature Rotorcraft. Norwell: Kluwer Academic Publishers,2003.
[19]Amidi O, Kanade T, Miller J R. Vision-based autonomous helicopter research at Carnegie Mellon Robotics Institute 1991-1997. In:Proceedings of Helicopter Japan. Gifu, Japan,1998: 2387-2394.
[20]La Civita M, Papageorgiou G, Messner W C, et al. Design and flight testing of a high-bandwidth H_inf Loop Shaping Controller for a robotic helicopter. Journal of Guidance, Control, and Dynamics,2006,29(2):485-494.
[21]Scherer S, Singh S, Chamberlain L, et al. Flying fast and low among obstacles:Methodology and experiments. The International Journal of Robotics Research,2008,27(5):549-574.
[22]Kim B S, Calise A J, Sattigeri R J. Adaptive,Integrated guidance and control design for line-of-sight-based formation flight. Journal of Guidance,Control and Dynamics,2005,30(5): 1386-1398.
[23]Ben L, Eric J, George V. Augmenting UAV autonomy:vision-based navigation and target tracking for unmanned aerial vehicles. IEEE Robotics and Automation Magazine,2006:63-71.
[24]Chowdhary G, Johnson E N. Flight test validation of a neural network based long term learning adaptive flight controller. In:AIAA Guidance, Navigation and Control Conference, Chicago, Illinois,2009.
[25]Chowdhary G, Sobers D M, Pravitra C, et al. Integrated guidance navigation and control for a fully autonomous indoor UAV. In:Guidance Navigation and Control Conference Portland, OR: AIAA,2011.
[26]Chowdhary G, Sobers D M, Salaun E, et al. Fully Autonomous indoor flight relying on only five very low-cost range sensors. Journal of Aerospace Computing, Information, and Communication,2013,10(1):21-31.
[27]Chowdhary G, Yucelen T, Miihlegg M, et al. Concurrent learning adaptive control of linear systems with exponentially convergent bounds. International Journal of Adaptive Control and Signal Processing,2012.
[28]Johnson E N, Schrage D P. The Georgia Tech Unmanned Aerial Research Vehicle:GTMax. In:AIAA Guidance,Navigation,and Control Conference and Exhibit. Austin,Texas,2003: 527-539.
[29]G.Chowdhary, E.N.Johnson. Adaptive Neural Network Flight Control Using both Current and Recorded Data. In:AIAA Guidance Navigation and Control Conference. Hilton Head,SC, 2007:1721-1740.
[30]Wu A D, Johnson E N, Kaess M, et al. Autonomous flight in gps-denied environments using monocular vision and inertial sensors. In:Proceedings of AIAA Information Technology Aerospace,2010.
[31]Kim H J, Shim D H, Sastry S. Flying robots:modeling, control and decision making. In: IEEE International Conference on Robotics and Automation,2002:66-71.
[32]Kim H J, Vidal R, Shim D H, et al. A hierarchical approach to probabilistic pursuit-evasion games with unmanned ground and aerial vehicles. In:Proceedings of the 40th IEEE Conference on Decision and Control,2001:634-639.
[33]Lygeros J, Tomlin C, Sastry S. Controllers for reachability specifications for hybrid systems. Automatica,1999,35(3):349-370.
[34]Shim D H, Kim H J, Sastry S. Decentralized nonlinear model predictive control of multiple flying robots. In:Proceedings of the 42nd IEEE Conference on Decision and Control,2003: 3621-3626.
[35]Shim D H, Hoam C, Sastry S S. Conflict-free navigation in unknown urban environments. IEEE Robotics & Automation Magazine,2006,13(3):27-33.
[36]Shim D H, Sastry S. An evasive maneuvering algorithm for UAVs in see-and-avoid situations. In:Proceedings of the American Control Conference. New York, NY, United states, 2007:3886-3891.
[37]Shim D H, Sastry S. A situation-aware flight control system design using real-time model predictive control for unmanned autonomous helicopters. In:AIAA Guidance, Navigation, and Control Conference. Keystone, CO, United states,2006:855-862.
[38]Gavrilets V, Shterenberg A, Martinos I, et al. Avionics system for aggressive maneuvers. IEEE Aerospace and Electronic Systems Magazine,2001,16(9):38-43.
[39]Frazzolli E, Dahleh M, Feron E. Real-time motion planning for agile autonomous vehicles. Journal o f Guidance,Control and Dynamics,2002, (25):116-129.
[40]Gavrilets V, Frazzoli E, Mettler B, et al. Aggressive maneuvering of small autonomous helicopters:A human-centered approach. International Journal of Robotics Research,2001,20(10): 795-807.
[41]Gavrilets V, Martinos M, Mettler B, et al. Control logic for automated aerobatic flight of miniature helicopter. In:Proceedings of the AIAA Guidance, Navigation, and Control Conference. Monterey.CA,2002:1-8.
[42]Gavrilets V, Mettler B, Feron E. Human-inspired control logic for automated maneuvering of miniature helicopter. Journal of Guidance Control and Dynamics,2004,27(5):752-759.
[43]Gavrilets V. Autonomous aerobatic maneuvering of miniature helicopter:[Ph.DThesis]. Boston,USA:MIT,2003.
[44]Alighanbari M, Bertuccelli L F, How J P. A robust approach to the UAV task assignment problem. In:Proceedings of the 45th IEEE Conference on Decision and Control,2006: 5935-5940.
[45]Bertuccelli L F, Wu A, How J P. Robust Adaptive Markov Decision Processes:Planning with Model Uncertainty. IEEE Control Systems,2012,32(5):96-109.
[46]Bry A, Bachrach A, Roy N. State estimation for aggressive flight in GPS-denied environments using onboard sensing. In:IEEE International Conference on Robotics and Automation (ICRA),2012:1-8.
[47]Geramifard A, Redding J D, Joseph J, et al. Model estimation within planning and learning. In:American Control Conference (ACC),2012:793-799.
[48]Case C, Suresh B, Coates A, et al. Autonomous sign reading for semantic mapping. In:IEEE International Conference on Robotics and Automation (ICRA),2011:3297-3303.
[49]Coates A, Baumstarck P, Quoc L, et al. Scalable learning for object detection with GPU hardware. In:IEEE/RSJ International Conference on Intelligent Robots and Systems,2009: 4287-4293.
[50]Coates A, Carpenter B, Case C, et al. Text detection and character recognition in scene images with unsupervised feature learning. In:International Conference on Document Analysis and Recognition (ICDAR),2011:440-445.
[51]Quigley M, Stavens D, Coates A, et al. Sub-meter indoor localization in unmodified environments with inexpensive sensors. In:IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS),2010:2039-2046.
[52]Abbeel P, Coates A, Quigley M, et al. An application of reinforcement learning to aerobatic helicopter flight. NIPS,2007:1-8.
[53]Abbeel P, Coates A, Ng A Y. Autonomous helicopter aerobatics through apprenticeship learning. International Journal of Robotics Research,2010,29(13):1608-1639.
[54]Tang J, Singh A, Goehausen N, et al. Parameterized maneuver learning for autonomous helicopter flight. In:IEEE International Conference on Robotics and Automation (ICRA),2010: 1142-1148.
[55]Kondak K, Deeg C, Hommel G, et al. Mechanical model and control of an autonomous small size helicopter with a stiff main rotor. In:IEEE/RSJ International Conference on Intelligent Robots and Systems,2004:2469-2474.
[56]Musial M, Brandenburg U W, Hommel G Cooperative autonomous mission planning and execution for the flying robot MARVIN. Intelligent Autonomous Systems,2000:636-643.
[57]Musial M, Brandenburg U W, Hommel G. Success of an Inexpensive system design:The flying robot MARVIN Unmanned Air Vehicle Systems. In:Proceedings of the Sixteenth International UAVs Conference. Bristol.UK,,2001.
[58]杜建福.基于预测控制的小型无人直升机自主飞行研究：[博士学位论文].上海：上海交通大学,2008,7.
[59]URL:http://pdv.cs.tu-berlin.de/MARVIN/.
[60]Kondak K, Bernard M, Meyer N, et al. Autonomously flying VTOL-robots:modeling and control. In:IEEE International Conference on Robotics and Automation,2007:736-741.
[61]Bernard M, Kondak K. Generic slung load transportation system using small size helicopters. In:IEEE International Conference on Robotics and Automation,2009:3258-3264.
[62]URL:http://pdv.cs.ta-berlin.de/lfafr/.
[63]Cai G W, Chen B M, Dong X X, et al. Design and implementation of a robust and nonlinear flight control system for an unmanned helicopter. Mechatronics,2011,21(5):803-820.
[64]Cai G W, Wang B, Chen B M, et al. Design and Implementation of a flight control system for an unmanned rotorcraft using RPT control approach. Asian Journal of Control,2013,15(1): 95-119.
[65]Yun B, Cai G, Chen B M, et al. GPS signal enhancement and attitude determination for a mini and low-cost unmanned aerial vehicle. Transactions of the Institute of Measurement and Control,2011,33(6):665-682.
[66]Nakanishi H, Kanata S, Sawaragi T. Measurement model of barometer in ground effect of unmanned helicopter and its application to estimate terrain clearance. In:IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR),2011:232-237.
[67]Gerig M B. Modeling.guidance and control of aerobatic maneuvers of an autonomous helicopter:[PhD Dissertation]. Zurich:The Swiss Federal Institute of Technology,2008.
[68]Ahmed B, Pota H R. Dynamic compensation for control of a rotary wing UAV using positive position feedback. Journal of Intelligent & Robotic Systems,2011,61(1):43-56.
[69]Sukhatme S H A G. Vision-Based Navigation through Urban Canyons. Journal of Field Robotics,2009,26(5):431-452.
[70]“飞豹杯”2004中国空中机器人大赛URL:http://it.sohu.com/s2004/AirRobot.shtml.
[71]Song D, Qi J, Han J, et al. Active model based predictive control for unmanned helicopter in full flight envelope. In:IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2010:616-621.
[72]Song D, Han J, Liu G Active model-based predictive control and experimental investigation on unmanned helicopters in full flight envelope. IEEE Transactions on Control Systems Technology, accepted.
[73]Zhao X, Liu G, Han J. Yaw control of small-scale helicopter with time-varying uncertainty: an adaptive robust H2 control approach. In:American Control Conference,2007:5260-5265.
[74]Zhao X, Zhang R. Adaptive robust guaranteed cost control with application to yaw control of helicopter. In:International Conference on Machine Learning and Cybernetics,2007:669-675.
[75]Zhao X, Jiang Z, Han J, et al. Adaptive robust LQR control with the application to the yaw control of small-scale helicopter. In:Proceedings of the IEEE International Conference on Mechatronics and Automation,2006:1002-1007.
[76]王争,何玉庆,韩建达.多无人直升机的相对动力学建模方法及其编队控制.控制理论与应用,2011,28(1)：108-112.
[77]宋大雷,齐俊桐,韩建达,等.旋翼飞行机器人系统建模与主动模型控制理论及实验研究.自动化学报,2011,37(4)：480495.
[78]Qi J, Song D, Dai L, et al. The new evolution for SIA rotorcraft UAV project. Journal of Robotics,2010,1-9.
[79]谈黎,贾培发.微小型自主直升机自抗扰控制.机器人,2009,31(5)：385-390.
[80]王冠林,史海庆,谭洪胜,等.基于视觉导航的无人直升机自主着陆.南京理工大学学报(自然科学版),2009,33(增刊)：117-122.
[81]陈皓生,陈大融.悬停状态下微型直升机航向模型的系统辨识.清华大学学报(自然科学版),2003,43(2)：184-187.
[82]Yu Y, Sheng Z Y. Robust attitude control for a 3DOF helicopter with multi-operation points. Journal of System Science and Comlexity,2008,19(1):1-12.
[83]周涛.微小型无人直升机简化模型及控制系统研究：[博士学位论文].杭州：浙江大学,2006.
[84]吴建德.基于频域辨识的微小型无人直升机的建模与控制研究：[博士学位论文].杭州：浙江大学,2007.
[85]徐玉.微小型无人直升机飞控平台与姿态融合算法研究：[博士学位论文].杭州：浙江大学,2008.
[86]任沁源.基于视觉信息的微小型无人直升机地标识别与位姿估计研究：[博士学位论文].杭州：浙江大学,2008.
[87]王德宇.小型无人直升机飞行控制系统研究：[硕士学位论文].长沙：国防科学技术大学,2005.
[88]王建文.无人直升机状态估计算法研究：[博士学位论文].长沙：国防科学技术大学,2008.
[89]宋宝泉.小型无人直升机非线性建模与控制算法研究：[博士学位论文].长沙：国防科学技术大学,2010.
[90]杨帆,熊笑,陈宗基,等.超小型直升机动力学模型的建模、辨识与验证.北京航空航天大学学报,2010,(8)：913-917.
[91]刘鹏,武哲.改进的频域辨识方法及其在直升机建模中的应用.控制理论与应用,2011,(12)：1747-1753.
[92]杜玉虎,房建成,盛蔚,等.基于最小二乘与自适应免疫遗传算法的小型无人直升机系统辨识.机器人,2012,(1)：72-77.
[93]杜玉虎,房建成,盛蔚,等.一种小型无人直升机自主起飞控制方法.自动化学报,2012,(8)：1385-1392.
[94]刘刚.多目标跟踪算法及实现研究：[博士学位论文].西安：西北工业大学,2003.
[95]史青海,彭军,李波,等.基于MIT自适应方法的直升机自动过渡控制系统设计仿真.直升机技术,2012,(3)：4346.
[96]邢小军,闫建国,袁冬莉.基于神经网络自适应滤波的直升机悬停高度稳定研究.西北工业大学学报,2011,(4)：627-631.
[97]郭庆.小型无人旋翼飞行平台的研制及关键技术研究：[硕士学位论文].西安：西北工业大学,2007.
[98]曾广鉴.不同复杂程度的直升机飞行动力学建模方法研究：[硕士学位论文].南京：南京航空航天大学,2012.
[99]王冲.无人机语音指令控制系统技术研究：[硕士学位论文].南京：南京航空航天大学,2012.
[100]周立冬.无人直升机前飞状态飞行控制律设计技术研究：[硕士学位论文].南京：南京航空航天大学,2011.
[101]范武斌.直升机显模型跟踪控制律设计与优化技术研究：[硕士学位论文].南京：南京航空航天大学,2011.
[102]中国航空学会举办国际空中机器人大赛.科协论坛,2012,(11)：12.
[103]URL:http://uav.sia.cn/.
[104]宋彦国,张呈林.小型无人直升机模糊飞行控制系统设计.南京航空航天大学学报,2007,39(1)：103-106.
[105]URL:http://uav.nuaa.edu.cn/.
[106]URL:http://www.slmx.cn/.
[107]URL:http://www.hanhe-aviation.com/index.html.
[108]Kim H J,Shim D H.A flight control system for aerial robots:Algorithms and experiments. Control Engineering Practice,2003,11(12):1389-1400.
[109]Li Y, Liu W, Song Q.Improved LQG control for small unmanned helicopter based on active model in uncertain environment.In:International Conference on Electronics,Communications and Control(ICECC),2011:289-292.
[110]Jiang Z, Han J D, Wang Y C, et al. Enhanced LQR control for unmanned helicopter in hover. In:International Symposium on Systems and Control in Aerospace and Astronautics,2006: 1438-1443.
[111]Papageorgiou G, Glover K. Hinf loop-shaping:Why is it a sensible procedure for designing robust flight controllers. In:AIAA Conference on Guidance,Navigation and Control,1999.
[112]Smerlas A J, Walker D J, Postlethwaite I, et al. Evaluating H controllers on the NRC Bell 205 fly-by-wire helicopter. Control Engineering Practice,2001,9(1):1-10.
[113]Fujiwara D, Shin J, Hazawa K, et al. Hinf hovering and guidance control for autonomous small-scale unmanned helicopter. In:IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Sendai, Japan,2004:2463-2468.
[114]La Civita M, Messner W, Kanade T. Modeling of small-scale helicopters with integrated first-principles and system identification techniques. In:Proceedings of the 58th Forum of the American Helicopter Society; 2002:2505-2516.
[115]Pei H L, Hu Y, Wu Y. Gain scheduling control of a small unmanned helicopter. In:IEEE International Conference on Control and Automation. Guangzhou, China,2008:1168-1173.
[116]Takahashi M D, Schulein G, Whalley M. Flight control law design and development for an autonomous rotorcraft. In:The 64th American Helicopter Society International Annual Forum. Montreal, Canada,2008:1652-1671.
[117]Prouty R W. Helicopter Performance, stability and control. Boston, Massachusetts: Breton,PWS Engineering, Duxbury,1986.
[118]Koo T J, Sastry S. Output tracking control design of a helicopter model based on approximate linearization. In:Proceedings of the IEEE Conference on Decision and Control. Tampa, FL, USA,1998:3635-3640.
[119]Koo T J, Sastry S. Differential flatness based full authority helicopter control design. In: The 38th IEEE Conference on Decision and Control (CDC). Phoenix, AZ, USA,1999: 1982-1987.
[120]Peng K, Cai G, Chen B M, et al. Design and implementation of an autonomous flight control law for a UAV helicopter. Automatica,2009,45(10):2333-2338.
[121]Johnson E N, Kannan S K. Adaptive trajectory control for autonomous helicopters. Journal of Guidance.Control and Dynamics,2005,28(3):524-538.
[122]Kannan S K. Adaptive control of systems in cascade with saturation:[Ph.D Thesis]:Georgia Institute of Technology,2005.
[123]URL:http://www.adaptiveflight.com/.
[124]Christophersen H B, Pickell R W, Neidhoefer J C, et al. A compact guidance, navigation, and control system for unmanned aerial vehicles. Journal of Aerospace Computing, Information and Communication,2006,3(5):187-213.
[125]Yavrucuk I, Prasad J V R, Unnikrishnan S. Envelope protection for autonomous unmanned aerial vehicles. Journal of Guidance, Control, and Dynamics,2009,32(1):248-261.
[126]Marconi L, Naldi R. Aggressive control of helicopters in presence of parametric and dynamical uncertainties. Mechatronics,2008,18(7):381-389.
[127]Mahony R, Hamel T. Robust trajectory tracking for a scale model autonomous helicopter. International Journal of Robust and Nonlinear Control,2004,14(12):1035-1059.
[128]Lee C T, Tsai C C. Nonlinear adaptive aggressive control using recurrent neural networks for a small scale helicopter. Mechatronics,2010,20(4):474-484.
[129]Ahmed B, Pota H R, Garratt M. Flight control of a rotary wing UAV using backstepping. International Journal of Robust and Nonlinear Control,2010,20(6):639-658.
[130]Pota H R, Ahmed B, Garratt M. Velocity control of a UAV using backstepping control. In: The 45th IEEE Conference on Decision and Control. San Diego, CA, United states,2006: 5894-5899.
[131]Yang J H, Hsu W C. Adaptive backstepping control for electrically driven unmanned helicopter. Control Engineering Practice,2009,17(8):903-913.
[132]Bouabdallah S, Murrieri P, Siegwart R. Design and control of an indoor micro quadrotor. In: IEEE International Conference on Robotics and Automation. New Orleans, LA, United states, 2004:4393-4398.
[133]Bouabdallah S, Siegwart R. Backstepping and sliding-mode techniques applied to an indoor micro quadrotor. In:IEEE International Conference on Robotics and Automation. Barcelona, Spain,2005:2247-2252.
[134]Chen F, Jiang B, Tao G A self-repairing control scheme for the helicopter via adaptive sliding mode back-stepping technology. In:The 8th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes. Mexico City, Mexico,2012:788-793.
[135]Chen F Y, Luan W L, Hou R. A self-repairing control for the uncertain system of a helicopter using adaptive sliding mode technology. In:The 3rd international Conference on Manufacturing Science and Engineering. Xiamen, China,2012:529-533.
[136]Lin C H, Jan S S, Hsiao F B. Autonomous hovering of an experimental unmanned helicopter system with proportional-integral sliding mode control. Journal of Aerospace Engineering,2011,24(3):338-348.
[137]Xu Y J. Multi-timescale nonlinear robust control for a miniature helicopter. IEEE Transactions on Aerospace and Electronic Systems,2010,46(2):656-671.
[138]Sugeno M. Fuzzy control of an unmanned helicopter toward real applications. In: Proceedings of the 1st Joint Conference on Intelligent Systems/ISAI/IFIS. Cancun, Mex,1996: 22-23.
[139]Sugeno M, Winston H, Hirano I, et al. Intelligent control of an unmanned helicopter based on fuzzy logic. In:Proceedings of the 51st Annual Forum-American Helicopter Society. Fort Worth, TX, USA,1995:791-803.
[140]Garcia R D, Valavanis K P. The implementation of an autonomous Helicopter testbed. Journal of Intelligent and Robotic Systems:Theory and Applications,2009,54(3):423-454.
[141]Garcia* R D, Valavanis K P, Kandel A. Autonomous helicopter navigation during a tail rotor failure utilizing fuzzy logic. In:Mediterranean Conference on Control and Automation. Athens, Greece,2007.
[142]Buskey G, Wyeth G, Roberts J. Autonomous helicopter hover using an artificial neural network. In:IEEE International Conference on Robotics and Automation (ICRA). Seoul, Korea, 2001:1635-1640.
[143]Gavrilets V, Mettler B, Feron E. Nonlinear model for a small-size acrobatic helicopter. In: AIAA Guidance, Navigation, and Control Conference and Exhibit,2001:6-9.
[144]Kim S K, Tilbury D M. Mathematical modeling and experimental identification of an unmanned helicopter robot with flybar dynamics. Journal of Robotic Systems,2004,21(3): 95-116.
[145]Kendoul F. Nonlinear hierarchical flight controller for unmanned rotorcraft:Design, stability, and experiments. Journal of Guidance, Control, and Dynamics,2009,32(6):1954-1958.
[146]Ahmed B, Kendoul F. Flight control of a small helicopter in unknown wind conditions. In: The 49th IEEE Conference on Decision and Control (CDC),2010:3536-3541.
[147]Zhou H, Pei H, Zhao Y. Trajectory tracking control of a small unmanned helicopter using MPC and backstepping. In:American Control Conference (ACC),2011:1593-1597.
[148]Chen M, Ge S, Ren B. Robust attitude control of helicopters with actuator dynamics using neural networks. Control Theory & Applications, IET,2010,4(12):2837-2854.
[149]Lee C T, Tsai C C. Adaptive backstepping integral control of a small-scale helicopter for airdrop missions. Asian Journal of Control,2010,12(4):531-541.
[150]Cheviron T, Plestan F, Chriette A. A robust guidance and control scheme of an autonomous scale helicopter in presence of wind gusts. International Journal of Control,2009,82(12): 2206-2220.
[151]Wen J T Y, Kenneth K D. The attitude control problem. IEEE Transactions on Automatic Control,1991,36(10):1148-1162.
[152]Danapalasingam K A, Leth J J, la Cour Harbo A, et al. Robust helicopter stabilization in the face of wind disturbance. In:The 49th IEEE Conference on Decision and Control (CDC),2010: 3832-3837.
[153]Isidori A, Marconi L, Serrani A. Robust nonlinear motion control of a helicopter. IEEE Transactions on Automatic Control,2003,48(3):413-426.
[154]申辉,方勇纯,孙秀云.一种基于切换模型的无人直升机偏航通道系统辨识方法.见：中国控制会议.西安,2013：已录用.
[155]Naidu D S, Calise A J. Singular perturbations and time scales in guidance and control of aerospace systems:A survey. Journal of Guidance, Control, and Dynamics,2001,24(6): 1057-1078.
[156]Karimoddini A, Cai G, Chen B M, et al. Multi-layer flight control synthesis and analysis of a small-scale Uav helicopter. In:IEEE Conference on Robotics Automation and Mechatronics (RAM),2010:321-326.
[157]Wang T, Chen Y, Liang J, et al. Combined of vector field and linear quadratic Gaussian for the path following of a small unmanned helicopter. IET Control Theory & Applications,2012, 6(17):2696-2703.
[158]Chaturvedi N A, Sanyal A K, Mcclamroch N H. Rigid-Body attitude control using rotation matrices for continuous, singularity-free control laws. IEEE Control Systems Magazine,2011, 31(3):30-51.
[159]Bertrand S, Guenard N, Hamel T, et al. A hierarchical controller for miniature VTOL UAVs:Design and stability analysis using singular perturbation theory. Control Engineering Practice,2011,19(10):1099-1108.
[160]Fan C, Guo S, Li D, et al. Nonlinear predictive attitude control with a disturbance observer of an unmanned helicopter on the test bench. In:IEEE Conference on Robotics, Automation and Mechatronics (RAM),2011:304-309.
[161]Teimoori H, Pota H R, Garratt M, et al. Attitude Control of a Miniature Helicopter using Optimal Sliding Mode Control. In:Australian Control Conference. Sydney, Australia,2012: 295-300.
[162]Guo R, Wu A, Lang Z, et al. A nonlinear attitude control method for an unmanned helicopter. In:The 2nd International Asia Conference on Informatics in Control, Automation and Robotics (CAR),2010:166-169.
[163]Jeong D Y, Kang T, Dharmayanda H R, et al. H_infinity attitude control system design for a small-scale autonomous helicopter with nonlinear dynamics and uncertainties. Journal of Aerospace Engineering,2011,25(4):501-518.
[164]方勇纯,卢桂章.非线性系统理论.北京：清华大学出版社,2009.
[165]Wang L P. Model predictive control system design and implementation using MATLAB: Springer,2009.
[166]Wang L P, Young P C. An improved structure for model predictive control using non-minimal state space realisation. Journal of Process Control,2006,16(4):355-371.
[167]Young P, Behzadi M, Wang C, et al. Direct digital and adaptive control by input-output state variable feedback pole assignment. International Journal of Control,1987,46(6):1867-1881.
[2]Nonami K, Kendoul F, Suzuki S, et al. Autonomous Flying Robots:Unmanned Aerial Vehicles and Micro Aerial Vehicles. Tokoy:Springer,2010.
[3]孙仕祺,马杰.无人机发展历程、现状及其所面临的挑战.飞行导弹,2005,25(1)：14-19.
[4]辛哲奎.基于视觉的小型无人直升机地面目标跟踪技术研究：[博士学位论文].天津：南开大学,2010.
[5]黄爱凤,邓克绪.民用无人机发展现状及关键技术.航空航天科技创新与长三角经济转型发展论坛论文集,2013：24-30.
[6]吴立新,刘平生,卢健.无人机分类研究.洪都科技,2005：1-11.
[7]邓寅喆,黄圣财,刘亮龚,等.超小型旋翼无人驾驶直升机的研究现状.机电一体化,2004,(1)：18-21.
[8]Kendoul F. Survey of Advances in Guidance, Navigation, and Control of Unmanned Rotorcraft Systems. Journal of Field Robotics,2012,29(2):315-377.
[9]Koslowski M, Kandil A A, Badreddin E. Modeling and identification of a small-scale unmanned autonomous helicopter. In:IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS),2012:2160-2165.
[10]Taamallah S, Bombois X, Van den Hof P. Optimal control for power-off landing of a small-scale helicopter a pseudospectral approach. In:American Control Conference (ACC),2012: 914-919.
[11]Raptis I A, Valavanis K P, Vachtsevanos G J. Linear tracking control for small-scale unmanned helicopters. IEEE Transactions on Control Systems Technology,2012,20(4): 995-1010.
[12]Cai G, Chen B M, Lee T H. Unmanned Rotorcraft Systems. London:Springer,2011.
[13]王赓.小型无人直升机自主飞行控制系统研究：[博士学位论文].上海：上海交通大学,2007.
[14]URL:http://iarc.angel-strike.com/
[15]Bergerman M, Amidi O, Miller J R, et al. Cascaded position and heading control of a robotic helicopter. In:IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). San Diego, CA, USA,2007:135-140.
[16]La Civita M, Papageorgibu G, Messner W C, et al. Design and flight testing of a gain-scheduled H_inf loop shaping controller for wide-envelope flight of a robotic helicopter. In: Proceedings of the American Control Conference. Denver, Colorado,2003:4195-4200.
[17]La Civita M, Papageorgiou G, Messner W C, et al. Integrated modeling and robust control for full-envelope flight of robotic helicopters. In:IEEE International Conference on Robotics and Automation (ICRA). Pittsburgh, PA, USA,2003:552-557.
[18]Mettler B. Identification Modeling and Characteristics of Miniature Rotorcraft. Norwell: Kluwer Academic Publishers,2003.
[19]Amidi O, Kanade T, Miller J R. Vision-based autonomous helicopter research at Carnegie Mellon Robotics Institute 1991-1997. In:Proceedings of Helicopter Japan. Gifu, Japan,1998: 2387-2394.
[20]La Civita M, Papageorgiou G, Messner W C, et al. Design and flight testing of a high-bandwidth H_inf Loop Shaping Controller for a robotic helicopter. Journal of Guidance, Control, and Dynamics,2006,29(2):485-494.
[21]Scherer S, Singh S, Chamberlain L, et al. Flying fast and low among obstacles:Methodology and experiments. The International Journal of Robotics Research,2008,27(5):549-574.
[22]Kim B S, Calise A J, Sattigeri R J. Adaptive,Integrated guidance and control design for line-of-sight-based formation flight. Journal of Guidance,Control and Dynamics,2005,30(5): 1386-1398.
[23]Ben L, Eric J, George V. Augmenting UAV autonomy:vision-based navigation and target tracking for unmanned aerial vehicles. IEEE Robotics and Automation Magazine,2006:63-71.
[24]Chowdhary G, Johnson E N. Flight test validation of a neural network based long term learning adaptive flight controller. In:AIAA Guidance, Navigation and Control Conference, Chicago, Illinois,2009.
[25]Chowdhary G, Sobers D M, Pravitra C, et al. Integrated guidance navigation and control for a fully autonomous indoor UAV. In:Guidance Navigation and Control Conference Portland, OR: AIAA,2011.
[26]Chowdhary G, Sobers D M, Salaun E, et al. Fully Autonomous indoor flight relying on only five very low-cost range sensors. Journal of Aerospace Computing, Information, and Communication,2013,10(1):21-31.
[27]Chowdhary G, Yucelen T, Miihlegg M, et al. Concurrent learning adaptive control of linear systems with exponentially convergent bounds. International Journal of Adaptive Control and Signal Processing,2012.
[28]Johnson E N, Schrage D P. The Georgia Tech Unmanned Aerial Research Vehicle:GTMax. In:AIAA Guidance,Navigation,and Control Conference and Exhibit. Austin,Texas,2003: 527-539.
[29]G.Chowdhary, E.N.Johnson. Adaptive Neural Network Flight Control Using both Current and Recorded Data. In:AIAA Guidance Navigation and Control Conference. Hilton Head,SC, 2007:1721-1740.
[30]Wu A D, Johnson E N, Kaess M, et al. Autonomous flight in gps-denied environments using monocular vision and inertial sensors. In:Proceedings of AIAA Information Technology Aerospace,2010.
[31]Kim H J, Shim D H, Sastry S. Flying robots:modeling, control and decision making. In: IEEE International Conference on Robotics and Automation,2002:66-71.
[32]Kim H J, Vidal R, Shim D H, et al. A hierarchical approach to probabilistic pursuit-evasion games with unmanned ground and aerial vehicles. In:Proceedings of the 40th IEEE Conference on Decision and Control,2001:634-639.
[33]Lygeros J, Tomlin C, Sastry S. Controllers for reachability specifications for hybrid systems. Automatica,1999,35(3):349-370.
[34]Shim D H, Kim H J, Sastry S. Decentralized nonlinear model predictive control of multiple flying robots. In:Proceedings of the 42nd IEEE Conference on Decision and Control,2003: 3621-3626.
[35]Shim D H, Hoam C, Sastry S S. Conflict-free navigation in unknown urban environments. IEEE Robotics & Automation Magazine,2006,13(3):27-33.
[36]Shim D H, Sastry S. An evasive maneuvering algorithm for UAVs in see-and-avoid situations. In:Proceedings of the American Control Conference. New York, NY, United states, 2007:3886-3891.
[37]Shim D H, Sastry S. A situation-aware flight control system design using real-time model predictive control for unmanned autonomous helicopters. In:AIAA Guidance, Navigation, and Control Conference. Keystone, CO, United states,2006:855-862.
[38]Gavrilets V, Shterenberg A, Martinos I, et al. Avionics system for aggressive maneuvers. IEEE Aerospace and Electronic Systems Magazine,2001,16(9):38-43.
[39]Frazzolli E, Dahleh M, Feron E. Real-time motion planning for agile autonomous vehicles. Journal o f Guidance,Control and Dynamics,2002, (25):116-129.
[40]Gavrilets V, Frazzoli E, Mettler B, et al. Aggressive maneuvering of small autonomous helicopters:A human-centered approach. International Journal of Robotics Research,2001,20(10): 795-807.
[41]Gavrilets V, Martinos M, Mettler B, et al. Control logic for automated aerobatic flight of miniature helicopter. In:Proceedings of the AIAA Guidance, Navigation, and Control Conference. Monterey.CA,2002:1-8.
[42]Gavrilets V, Mettler B, Feron E. Human-inspired control logic for automated maneuvering of miniature helicopter. Journal of Guidance Control and Dynamics,2004,27(5):752-759.
[43]Gavrilets V. Autonomous aerobatic maneuvering of miniature helicopter:[Ph.DThesis]. Boston,USA:MIT,2003.
[44]Alighanbari M, Bertuccelli L F, How J P. A robust approach to the UAV task assignment problem. In:Proceedings of the 45th IEEE Conference on Decision and Control,2006: 5935-5940.
[45]Bertuccelli L F, Wu A, How J P. Robust Adaptive Markov Decision Processes:Planning with Model Uncertainty. IEEE Control Systems,2012,32(5):96-109.
[46]Bry A, Bachrach A, Roy N. State estimation for aggressive flight in GPS-denied environments using onboard sensing. In:IEEE International Conference on Robotics and Automation (ICRA),2012:1-8.
[47]Geramifard A, Redding J D, Joseph J, et al. Model estimation within planning and learning. In:American Control Conference (ACC),2012:793-799.
[48]Case C, Suresh B, Coates A, et al. Autonomous sign reading for semantic mapping. In:IEEE International Conference on Robotics and Automation (ICRA),2011:3297-3303.
[49]Coates A, Baumstarck P, Quoc L, et al. Scalable learning for object detection with GPU hardware. In:IEEE/RSJ International Conference on Intelligent Robots and Systems,2009: 4287-4293.
[50]Coates A, Carpenter B, Case C, et al. Text detection and character recognition in scene images with unsupervised feature learning. In:International Conference on Document Analysis and Recognition (ICDAR),2011:440-445.
[51]Quigley M, Stavens D, Coates A, et al. Sub-meter indoor localization in unmodified environments with inexpensive sensors. In:IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS),2010:2039-2046.
[52]Abbeel P, Coates A, Quigley M, et al. An application of reinforcement learning to aerobatic helicopter flight. NIPS,2007:1-8.
[53]Abbeel P, Coates A, Ng A Y. Autonomous helicopter aerobatics through apprenticeship learning. International Journal of Robotics Research,2010,29(13):1608-1639.
[54]Tang J, Singh A, Goehausen N, et al. Parameterized maneuver learning for autonomous helicopter flight. In:IEEE International Conference on Robotics and Automation (ICRA),2010: 1142-1148.
[55]Kondak K, Deeg C, Hommel G, et al. Mechanical model and control of an autonomous small size helicopter with a stiff main rotor. In:IEEE/RSJ International Conference on Intelligent Robots and Systems,2004:2469-2474.
[56]Musial M, Brandenburg U W, Hommel G Cooperative autonomous mission planning and execution for the flying robot MARVIN. Intelligent Autonomous Systems,2000:636-643.
[57]Musial M, Brandenburg U W, Hommel G. Success of an Inexpensive system design:The flying robot MARVIN Unmanned Air Vehicle Systems. In:Proceedings of the Sixteenth International UAVs Conference. Bristol.UK,,2001.
[58]杜建福.基于预测控制的小型无人直升机自主飞行研究：[博士学位论文].上海：上海交通大学,2008,7.
[59]URL:http://pdv.cs.tu-berlin.de/MARVIN/.
[60]Kondak K, Bernard M, Meyer N, et al. Autonomously flying VTOL-robots:modeling and control. In:IEEE International Conference on Robotics and Automation,2007:736-741.
[61]Bernard M, Kondak K. Generic slung load transportation system using small size helicopters. In:IEEE International Conference on Robotics and Automation,2009:3258-3264.
[62]URL:http://pdv.cs.ta-berlin.de/lfafr/.
[63]Cai G W, Chen B M, Dong X X, et al. Design and implementation of a robust and nonlinear flight control system for an unmanned helicopter. Mechatronics,2011,21(5):803-820.
[64]Cai G W, Wang B, Chen B M, et al. Design and Implementation of a flight control system for an unmanned rotorcraft using RPT control approach. Asian Journal of Control,2013,15(1): 95-119.
[65]Yun B, Cai G, Chen B M, et al. GPS signal enhancement and attitude determination for a mini and low-cost unmanned aerial vehicle. Transactions of the Institute of Measurement and Control,2011,33(6):665-682.
[66]Nakanishi H, Kanata S, Sawaragi T. Measurement model of barometer in ground effect of unmanned helicopter and its application to estimate terrain clearance. In:IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR),2011:232-237.
[67]Gerig M B. Modeling.guidance and control of aerobatic maneuvers of an autonomous helicopter:[PhD Dissertation]. Zurich:The Swiss Federal Institute of Technology,2008.
[68]Ahmed B, Pota H R. Dynamic compensation for control of a rotary wing UAV using positive position feedback. Journal of Intelligent & Robotic Systems,2011,61(1):43-56.
[69]Sukhatme S H A G. Vision-Based Navigation through Urban Canyons. Journal of Field Robotics,2009,26(5):431-452.
[70]“飞豹杯”2004中国空中机器人大赛URL:http://it.sohu.com/s2004/AirRobot.shtml.
[71]Song D, Qi J, Han J, et al. Active model based predictive control for unmanned helicopter in full flight envelope. In:IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2010:616-621.
[72]Song D, Han J, Liu G Active model-based predictive control and experimental investigation on unmanned helicopters in full flight envelope. IEEE Transactions on Control Systems Technology, accepted.
[73]Zhao X, Liu G, Han J. Yaw control of small-scale helicopter with time-varying uncertainty: an adaptive robust H2 control approach. In:American Control Conference,2007:5260-5265.
[74]Zhao X, Zhang R. Adaptive robust guaranteed cost control with application to yaw control of helicopter. In:International Conference on Machine Learning and Cybernetics,2007:669-675.
[75]Zhao X, Jiang Z, Han J, et al. Adaptive robust LQR control with the application to the yaw control of small-scale helicopter. In:Proceedings of the IEEE International Conference on Mechatronics and Automation,2006:1002-1007.
[76]王争,何玉庆,韩建达.多无人直升机的相对动力学建模方法及其编队控制.控制理论与应用,2011,28(1)：108-112.
[77]宋大雷,齐俊桐,韩建达,等.旋翼飞行机器人系统建模与主动模型控制理论及实验研究.自动化学报,2011,37(4)：480495.
[78]Qi J, Song D, Dai L, et al. The new evolution for SIA rotorcraft UAV project. Journal of Robotics,2010,1-9.
[79]谈黎,贾培发.微小型自主直升机自抗扰控制.机器人,2009,31(5)：385-390.
[80]王冠林,史海庆,谭洪胜,等.基于视觉导航的无人直升机自主着陆.南京理工大学学报(自然科学版),2009,33(增刊)：117-122.
[81]陈皓生,陈大融.悬停状态下微型直升机航向模型的系统辨识.清华大学学报(自然科学版),2003,43(2)：184-187.
[82]Yu Y, Sheng Z Y. Robust attitude control for a 3DOF helicopter with multi-operation points. Journal of System Science and Comlexity,2008,19(1):1-12.
[83]周涛.微小型无人直升机简化模型及控制系统研究：[博士学位论文].杭州：浙江大学,2006.
[84]吴建德.基于频域辨识的微小型无人直升机的建模与控制研究：[博士学位论文].杭州：浙江大学,2007.
[85]徐玉.微小型无人直升机飞控平台与姿态融合算法研究：[博士学位论文].杭州：浙江大学,2008.
[86]任沁源.基于视觉信息的微小型无人直升机地标识别与位姿估计研究：[博士学位论文].杭州：浙江大学,2008.
[87]王德宇.小型无人直升机飞行控制系统研究：[硕士学位论文].长沙：国防科学技术大学,2005.
[88]王建文.无人直升机状态估计算法研究：[博士学位论文].长沙：国防科学技术大学,2008.
[89]宋宝泉.小型无人直升机非线性建模与控制算法研究：[博士学位论文].长沙：国防科学技术大学,2010.
[90]杨帆,熊笑,陈宗基,等.超小型直升机动力学模型的建模、辨识与验证.北京航空航天大学学报,2010,(8)：913-917.
[91]刘鹏,武哲.改进的频域辨识方法及其在直升机建模中的应用.控制理论与应用,2011,(12)：1747-1753.
[92]杜玉虎,房建成,盛蔚,等.基于最小二乘与自适应免疫遗传算法的小型无人直升机系统辨识.机器人,2012,(1)：72-77.
[93]杜玉虎,房建成,盛蔚,等.一种小型无人直升机自主起飞控制方法.自动化学报,2012,(8)：1385-1392.
[94]刘刚.多目标跟踪算法及实现研究：[博士学位论文].西安：西北工业大学,2003.
[95]史青海,彭军,李波,等.基于MIT自适应方法的直升机自动过渡控制系统设计仿真.直升机技术,2012,(3)：4346.
[96]邢小军,闫建国,袁冬莉.基于神经网络自适应滤波的直升机悬停高度稳定研究.西北工业大学学报,2011,(4)：627-631.
[97]郭庆.小型无人旋翼飞行平台的研制及关键技术研究：[硕士学位论文].西安：西北工业大学,2007.
[98]曾广鉴.不同复杂程度的直升机飞行动力学建模方法研究：[硕士学位论文].南京：南京航空航天大学,2012.
[99]王冲.无人机语音指令控制系统技术研究：[硕士学位论文].南京：南京航空航天大学,2012.
[100]周立冬.无人直升机前飞状态飞行控制律设计技术研究：[硕士学位论文].南京：南京航空航天大学,2011.
[101]范武斌.直升机显模型跟踪控制律设计与优化技术研究：[硕士学位论文].南京：南京航空航天大学,2011.
[102]中国航空学会举办国际空中机器人大赛.科协论坛,2012,(11)：12.
[103]URL:http://uav.sia.cn/.
[104]宋彦国,张呈林.小型无人直升机模糊飞行控制系统设计.南京航空航天大学学报,2007,39(1)：103-106.
[105]URL:http://uav.nuaa.edu.cn/.
[106]URL:http://www.slmx.cn/.
[107]URL:http://www.hanhe-aviation.com/index.html.
[108]Kim H J,Shim D H.A flight control system for aerial robots:Algorithms and experiments. Control Engineering Practice,2003,11(12):1389-1400.
[109]Li Y, Liu W, Song Q.Improved LQG control for small unmanned helicopter based on active model in uncertain environment.In:International Conference on Electronics,Communications and Control(ICECC),2011:289-292.
[110]Jiang Z, Han J D, Wang Y C, et al. Enhanced LQR control for unmanned helicopter in hover. In:International Symposium on Systems and Control in Aerospace and Astronautics,2006: 1438-1443.
[111]Papageorgiou G, Glover K. Hinf loop-shaping:Why is it a sensible procedure for designing robust flight controllers. In:AIAA Conference on Guidance,Navigation and Control,1999.
[112]Smerlas A J, Walker D J, Postlethwaite I, et al. Evaluating H controllers on the NRC Bell 205 fly-by-wire helicopter. Control Engineering Practice,2001,9(1):1-10.
[113]Fujiwara D, Shin J, Hazawa K, et al. Hinf hovering and guidance control for autonomous small-scale unmanned helicopter. In:IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Sendai, Japan,2004:2463-2468.
[114]La Civita M, Messner W, Kanade T. Modeling of small-scale helicopters with integrated first-principles and system identification techniques. In:Proceedings of the 58th Forum of the American Helicopter Society; 2002:2505-2516.
[115]Pei H L, Hu Y, Wu Y. Gain scheduling control of a small unmanned helicopter. In:IEEE International Conference on Control and Automation. Guangzhou, China,2008:1168-1173.
[116]Takahashi M D, Schulein G, Whalley M. Flight control law design and development for an autonomous rotorcraft. In:The 64th American Helicopter Society International Annual Forum. Montreal, Canada,2008:1652-1671.
[117]Prouty R W. Helicopter Performance, stability and control. Boston, Massachusetts: Breton,PWS Engineering, Duxbury,1986.
[118]Koo T J, Sastry S. Output tracking control design of a helicopter model based on approximate linearization. In:Proceedings of the IEEE Conference on Decision and Control. Tampa, FL, USA,1998:3635-3640.
[119]Koo T J, Sastry S. Differential flatness based full authority helicopter control design. In: The 38th IEEE Conference on Decision and Control (CDC). Phoenix, AZ, USA,1999: 1982-1987.
[120]Peng K, Cai G, Chen B M, et al. Design and implementation of an autonomous flight control law for a UAV helicopter. Automatica,2009,45(10):2333-2338.
[121]Johnson E N, Kannan S K. Adaptive trajectory control for autonomous helicopters. Journal of Guidance.Control and Dynamics,2005,28(3):524-538.
[122]Kannan S K. Adaptive control of systems in cascade with saturation:[Ph.D Thesis]:Georgia Institute of Technology,2005.
[123]URL:http://www.adaptiveflight.com/.
[124]Christophersen H B, Pickell R W, Neidhoefer J C, et al. A compact guidance, navigation, and control system for unmanned aerial vehicles. Journal of Aerospace Computing, Information and Communication,2006,3(5):187-213.
[125]Yavrucuk I, Prasad J V R, Unnikrishnan S. Envelope protection for autonomous unmanned aerial vehicles. Journal of Guidance, Control, and Dynamics,2009,32(1):248-261.
[126]Marconi L, Naldi R. Aggressive control of helicopters in presence of parametric and dynamical uncertainties. Mechatronics,2008,18(7):381-389.
[127]Mahony R, Hamel T. Robust trajectory tracking for a scale model autonomous helicopter. International Journal of Robust and Nonlinear Control,2004,14(12):1035-1059.
[128]Lee C T, Tsai C C. Nonlinear adaptive aggressive control using recurrent neural networks for a small scale helicopter. Mechatronics,2010,20(4):474-484.
[129]Ahmed B, Pota H R, Garratt M. Flight control of a rotary wing UAV using backstepping. International Journal of Robust and Nonlinear Control,2010,20(6):639-658.
[130]Pota H R, Ahmed B, Garratt M. Velocity control of a UAV using backstepping control. In: The 45th IEEE Conference on Decision and Control. San Diego, CA, United states,2006: 5894-5899.
[131]Yang J H, Hsu W C. Adaptive backstepping control for electrically driven unmanned helicopter. Control Engineering Practice,2009,17(8):903-913.
[132]Bouabdallah S, Murrieri P, Siegwart R. Design and control of an indoor micro quadrotor. In: IEEE International Conference on Robotics and Automation. New Orleans, LA, United states, 2004:4393-4398.
[133]Bouabdallah S, Siegwart R. Backstepping and sliding-mode techniques applied to an indoor micro quadrotor. In:IEEE International Conference on Robotics and Automation. Barcelona, Spain,2005:2247-2252.
[134]Chen F, Jiang B, Tao G A self-repairing control scheme for the helicopter via adaptive sliding mode back-stepping technology. In:The 8th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes. Mexico City, Mexico,2012:788-793.
[135]Chen F Y, Luan W L, Hou R. A self-repairing control for the uncertain system of a helicopter using adaptive sliding mode technology. In:The 3rd international Conference on Manufacturing Science and Engineering. Xiamen, China,2012:529-533.
[136]Lin C H, Jan S S, Hsiao F B. Autonomous hovering of an experimental unmanned helicopter system with proportional-integral sliding mode control. Journal of Aerospace Engineering,2011,24(3):338-348.
[137]Xu Y J. Multi-timescale nonlinear robust control for a miniature helicopter. IEEE Transactions on Aerospace and Electronic Systems,2010,46(2):656-671.
[138]Sugeno M. Fuzzy control of an unmanned helicopter toward real applications. In: Proceedings of the 1st Joint Conference on Intelligent Systems/ISAI/IFIS. Cancun, Mex,1996: 22-23.
[139]Sugeno M, Winston H, Hirano I, et al. Intelligent control of an unmanned helicopter based on fuzzy logic. In:Proceedings of the 51st Annual Forum-American Helicopter Society. Fort Worth, TX, USA,1995:791-803.
[140]Garcia R D, Valavanis K P. The implementation of an autonomous Helicopter testbed. Journal of Intelligent and Robotic Systems:Theory and Applications,2009,54(3):423-454.
[141]Garcia* R D, Valavanis K P, Kandel A. Autonomous helicopter navigation during a tail rotor failure utilizing fuzzy logic. In:Mediterranean Conference on Control and Automation. Athens, Greece,2007.
[142]Buskey G, Wyeth G, Roberts J. Autonomous helicopter hover using an artificial neural network. In:IEEE International Conference on Robotics and Automation (ICRA). Seoul, Korea, 2001:1635-1640.
[143]Gavrilets V, Mettler B, Feron E. Nonlinear model for a small-size acrobatic helicopter. In: AIAA Guidance, Navigation, and Control Conference and Exhibit,2001:6-9.
[144]Kim S K, Tilbury D M. Mathematical modeling and experimental identification of an unmanned helicopter robot with flybar dynamics. Journal of Robotic Systems,2004,21(3): 95-116.
[145]Kendoul F. Nonlinear hierarchical flight controller for unmanned rotorcraft:Design, stability, and experiments. Journal of Guidance, Control, and Dynamics,2009,32(6):1954-1958.
[146]Ahmed B, Kendoul F. Flight control of a small helicopter in unknown wind conditions. In: The 49th IEEE Conference on Decision and Control (CDC),2010:3536-3541.
[147]Zhou H, Pei H, Zhao Y. Trajectory tracking control of a small unmanned helicopter using MPC and backstepping. In:American Control Conference (ACC),2011:1593-1597.
[148]Chen M, Ge S, Ren B. Robust attitude control of helicopters with actuator dynamics using neural networks. Control Theory & Applications, IET,2010,4(12):2837-2854.
[149]Lee C T, Tsai C C. Adaptive backstepping integral control of a small-scale helicopter for airdrop missions. Asian Journal of Control,2010,12(4):531-541.
[150]Cheviron T, Plestan F, Chriette A. A robust guidance and control scheme of an autonomous scale helicopter in presence of wind gusts. International Journal of Control,2009,82(12): 2206-2220.
[151]Wen J T Y, Kenneth K D. The attitude control problem. IEEE Transactions on Automatic Control,1991,36(10):1148-1162.
[152]Danapalasingam K A, Leth J J, la Cour Harbo A, et al. Robust helicopter stabilization in the face of wind disturbance. In:The 49th IEEE Conference on Decision and Control (CDC),2010: 3832-3837.
[153]Isidori A, Marconi L, Serrani A. Robust nonlinear motion control of a helicopter. IEEE Transactions on Automatic Control,2003,48(3):413-426.
[154]申辉,方勇纯,孙秀云.一种基于切换模型的无人直升机偏航通道系统辨识方法.见：中国控制会议.西安,2013：已录用.
[155]Naidu D S, Calise A J. Singular perturbations and time scales in guidance and control of aerospace systems:A survey. Journal of Guidance, Control, and Dynamics,2001,24(6): 1057-1078.
[156]Karimoddini A, Cai G, Chen B M, et al. Multi-layer flight control synthesis and analysis of a small-scale Uav helicopter. In:IEEE Conference on Robotics Automation and Mechatronics (RAM),2010:321-326.
[157]Wang T, Chen Y, Liang J, et al. Combined of vector field and linear quadratic Gaussian for the path following of a small unmanned helicopter. IET Control Theory & Applications,2012, 6(17):2696-2703.
[158]Chaturvedi N A, Sanyal A K, Mcclamroch N H. Rigid-Body attitude control using rotation matrices for continuous, singularity-free control laws. IEEE Control Systems Magazine,2011, 31(3):30-51.
[159]Bertrand S, Guenard N, Hamel T, et al. A hierarchical controller for miniature VTOL UAVs:Design and stability analysis using singular perturbation theory. Control Engineering Practice,2011,19(10):1099-1108.
[160]Fan C, Guo S, Li D, et al. Nonlinear predictive attitude control with a disturbance observer of an unmanned helicopter on the test bench. In:IEEE Conference on Robotics, Automation and Mechatronics (RAM),2011:304-309.
[161]Teimoori H, Pota H R, Garratt M, et al. Attitude Control of a Miniature Helicopter using Optimal Sliding Mode Control. In:Australian Control Conference. Sydney, Australia,2012: 295-300.
[162]Guo R, Wu A, Lang Z, et al. A nonlinear attitude control method for an unmanned helicopter. In:The 2nd International Asia Conference on Informatics in Control, Automation and Robotics (CAR),2010:166-169.
[163]Jeong D Y, Kang T, Dharmayanda H R, et al. H_infinity attitude control system design for a small-scale autonomous helicopter with nonlinear dynamics and uncertainties. Journal of Aerospace Engineering,2011,25(4):501-518.
[164]方勇纯,卢桂章.非线性系统理论.北京：清华大学出版社,2009.
[165]Wang L P. Model predictive control system design and implementation using MATLAB: Springer,2009.
[166]Wang L P, Young P C. An improved structure for model predictive control using non-minimal state space realisation. Journal of Process Control,2006,16(4):355-371.
[167]Young P, Behzadi M, Wang C, et al. Direct digital and adaptive control by input-output state variable feedback pole assignment. International Journal of Control,1987,46(6):1867-1881.