摘要
研究了新型多旋翼飞行器的建模与轨迹跟踪控制.建立了非线性运动学和动力学模型,并提出基于全调节径向基神经网络和回馈递推的鲁棒自适应轨迹跟踪控制策略.首先设计了飞行器的位置误差PID控制器,用于实时消除飞行轨迹与期望轨迹的偏差,并为姿态控制环构建姿态角指令.采用全调节径向基神经网络估计飞行器动力学模型中的复合干扰,为避免回馈递推控制器设计过程中对虚拟控制信号的繁琐求导运算,减小对解析模型的依赖度,设计了一种基于指令滤波回馈递推的飞行器姿态控制器.该设计方法通过滤波器而非直接用解析方法对虚拟控制信号求导,大大简化了控制器的设计过程,节省了控制能量.仿真实验表明所提出的轨迹跟踪策略的正确性和有效性.
Modeling and trajectory tracking control for a multi-rotor unmanned aerial vehicle （UAV） is studied. Nonlinear kinematics and dynamics models of the aircraft are established. A robust adaptive trajectory tracking control scheme based on fully tuned radial basis function neural network （FTRBFNN） and command filtered backstepping is proposed for the multi-rotor aircraft. In the scheme, a position error PID controller of the aircraft is developed to eliminate deviation of the flight trajectory from the desired trajectory, and construct attitude angle commands for the attitude control loop. FTRBFNN is then used to estimate composite disturbance of the rotational dynamics. To avoid calculating pseudo control signal derivative analytically, and decrease dependence on the analytic model in the standard backstepping design, a command filtered backstepping technique is used to design the attitude controller. The technique uses a filter to calculate derivatives of the virtual control signal, instead of using analytical differentiation. It thus significantly simplifies backstepping implementation and saves control energy. Correctness and effectiveness of the proposed robust adaptive trajectory tracking scheme are verified through simulation experiment.
出处
《应用科学学报》
CAS
CSCD
北大核心
2014年第3期301-310,共10页
Journal of Applied Sciences
基金
高等学校博士学科点专项科研基金(No.20113218110013)
江苏省普通高校研究生科研创新计划项目基金(No.CXLX110200)
江苏省产学研联合创新基金(No.BY2012018)
山东省自然青年科学基金(No.ZR2012FQ030)资助
关键词
多旋翼飞行器
鲁棒自适应
神经网络
指令滤波
回馈递推
multi-rotor aircraft, robust adaptive, neural network （NN）, command filter, backstepping
