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Optimal path tracking control of a quadrotor UAV

机译:四旋翼无人机的最优路径跟踪控制

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This paper presents the linear quadratic tracking (LQT) control of a quadrotor UAV by solving discrete time matrix difference Riccati Equation. First, the nonlinear dynamic model of the quadrotor is obtained by using Newton's equations of motion. Then, the nonlinear dynamic model is linearized around hover condition. The linearized dynamic model is used to solve the optimal control problem. A trade off between good tracking performance and energy consumption is made while defining the performance index (cost function). Time-variant optimal control gains are found off-line by solving discrete time matrix difference Riccati Equation backwards in time. Finally, to validate optimal control system, simulations are performed by using the nonlinear dynamic model as plant and time-variant optimal control gains as state feedback control. The optimal control algorithm used in this paper uses time-variant control gains instead of fixed (time-invariant) control gains used in classical LQR control. Simulations show that, good tracking performance is achieved while decreasing energy consumption compared to the fixed gain LQR control. Some other advantageous properties of the optimal control system used in this paper compared to the fixed gain LQR control are also analyzed. In addition, disturbance rejection properties of the optimal control system are also studied. All algorithms and simulations are done by using MATLAB software.
机译:通过求解离散时间矩阵差Riccati方程,提出了四旋翼无人机的线性二次跟踪(LQT)控制。首先,通过使用牛顿运动方程获得四旋翼的非线性动力学模型。然后,将非线性动力学模型围绕悬停条件进行线性化。线性化动力学模型用于解决最优控制问题。在定义性能指标(成本函数)的同时,还要在良好的跟踪性能和能耗之间进行权衡。通过在时间上向后求解离散时间矩阵差异Riccati方程,可以离线找到时变最优控制增益。最后,为验证最优控制系统,以非线性动力学模型为工厂,时变最优控制增益为状态反馈控制,进行了仿真。本文中使用的最佳控制算法使用时变控制增益,而不是经典LQR控制中使用的固定(时变)控制增益。仿真表明,与固定增益LQR控制相比,在降低能耗的同时实现了良好的跟踪性能。还分析了与固定增益LQR控制相比,本文使用的最佳控制系统的其他一些有利特性。此外,还研究了最优控制系统的干扰抑制特性。所有算法和仿真都是通过使用MATLAB软件完成的。

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