基于主动建模的无人直升机增强LQR控制  

Enhanced LQR Control for Unmanned Helicopter Based on Active Model

作　　者：姜哲[1] 赵新刚[1] 齐俊桐[1] 韩建达[1] 王越超[1] 

机构地区：[1]中国科学院沈阳自动化研究所机器人学实验室

出　　处：《吉林大学学报（信息科学版）》2007年第5期553-559,共7页Journal of Jilin University（Information Science Edition）

基　　金：国家863计划基金资助项目(2006AA04Z206)

摘　　要：为了解决无人直升机控制问题,通过把主动建模与LQR(Linear Quadratic Regulator)控制相结合,提出一种能补偿模型差的控制方法。该方法在悬停状态下,采用简化模型设计LQR控制器,并通过UKF(Un-scented-Kalman-Filter)在线估计简化模型与全状态模型的模型差,使用模型差作为补偿项对LQR控制增强。针对实际直升机动力学模型进行仿真,验证了基于UKF的估计和增强LQR控制的有效性。仿真实验结果证明,基于UKF的主动建模技术能够快速估计状态和参数变化,并且增强LQR控制能够使系统适应模型不确定性。A control method that can compensate model error by integrating active model into LQR （Linear Quadratic Regulator） control is proposed. In the scheme, a normal LQR control designed from a simplified model at hovering is enhanced by means of UKF （Unscented-Kalman-Filter） based estimation, which tries to capture the model error between the simplified model and the full dynamics. Simulations about helicopter model are conducted to verify both the UKF-based estimation and the enhanced LQR control. Simulation results demonstrate that the UKF-based online modeling technique estimate quickly to the changes in both state and parameters, and the enhanced LQR control makes the control system adaptive to model uncertainties autonomously.

关 键 词：自动控制技术 增强LQR控制 主动建模 模型差 无色卡尔曼滤波(UKF) 直升机 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置] V249.122[自动化与计算机技术—控制科学与工程]