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Attitude Control for Quadcopter with Tilting Rotors using RBFNN-based Adaptive Terminal Sliding Mode Controller

机译:基于RBFNN的自适应终端滑模控制器对带有倾斜旋翼的四旋翼飞机的姿态控制

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The traditional quadcopter with four actuators is an under-actuated system, cannot achieve decoupling of line motion and angular motion. Quadcopter with tilting rotors can achieve 6-degree-of-freedom decoupling thanks to the mechanical structure where the motor base allows the motor to rotate around the arm axis, which greatly expands the application scenario of the quadcopter. In this paper, the rigid body dynamics model of the quadcopter with tilting rotors is deduced. In order to solve the non-unique of actuator action caused by over-actuated, a new linear control allocate scheme is adopted. A terminal sliding mode attitude controller(TSMC) based on RBF neural network uncertainty compensator is proposed, and the finite time convergence and global asymptotic stability of the closed-loop system are proved. Parameters of TSMC and RBF are optimized by particle swarm intelligence algorithm with two fitness functions. The main advantage of the proposed method is the ability to observe unknown bounded lumped uncertainties accurately and quickly. Simulation experiments demonstrate the effectiveness and robustness of the proposed controller.
机译:具有四个执行器的传统四轴飞行器是欠驱动系统,无法实现线运动和角运动的解耦。具有倾斜转子的四轴飞行器可以通过机械结构实现6自由度的解耦,其中电机底座允许电机绕臂轴旋转,这极大地扩展了四轴飞行器的应用场景。本文推导了具有旋翼的四轴飞行器的刚体动力学模型。为了解决由于过度执行导致执行器动作不唯一的问题,采用了一种新的线性控制分配方案。提出了一种基于RBF神经网络不确定性补偿器的终端滑模姿态控制器,并证明了闭环系统的有限时间收敛性和全局渐近稳定性。通过具有两个适应度函数的粒子群智能算法对TSMC和RBF的参数进行了优化。该方法的主要优点是能够准确,快速地观测未知的有限集总不确定性。仿真实验证明了所提出控制器的有效性和鲁棒性。

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