红外相机扫描镜轨迹跟踪的迭代学习控制方法

为提高星载广域红外相机的观测效率与凝视成像质量，扫描镜需要在几十毫秒的时间内完成角度切换，实现角秒级的轨迹跟踪控制。在闭环带宽有限的情况下，性能指标难以通过基于经典控制理论的算法实现。针对枢轴支撑的扫描镜机构，提出了一种基于迭代学习的高阶系统轨迹跟踪控制方法，推导了迭代学习律，并通过预测型算法对学习律进行了优化，避免了误差高阶导数的计算。然后通过频域分析说明了算法收敛性，选取了关键参数。通过仿真与原理样机实测验证了其应用效果。测试结果表明，算法在闭环带宽低于2 Hz的情况下，无需辨识被控对象的高阶特性，即可实现扫描镜对角加加速度超过106 （°）/s3轨迹的高精度跟踪控制，跟踪误差优于±1.5"，满足相机应用要求。

Trajectory tracking control for scanning mirror of infrared camera based on iterative learning algorithm

In order to improve the efficiency of observation and the quality of staring imaging,satellite-borne wide field infrared camera has a strict requirement on the scanning mirror control system.The scanning mirror is required to achieve fast steering in tens of milliseconds and trajectory tracking with arc second level control precision.Due to the limit of control system bandwidth,the motion performances are difficult to realize by algorithm based on classical control theory.For the scanning mirror with pivot supporting,a trajectory tracking control method for high order controlled plant based on iterative learning algorithm was proposed.The design and optimization process of learning law was given.By using anticipatory learning scheme,the calculations of high order derivatives for tracking error were avoided.Furthermore,the convergence condition and the key parameter of control algorithm were derived by frequency domain analysis.Its application effect was verified by both simulation and prototype test.The prototype test results show that,in the scanning mirror closed-loop control system with less than 2 Hz bandwidth and no identification for high order characteristics of the controlled plant,the tracking error of a desired trajectory with above 10⁶(°)/s³ angular jerk is reduced to±1.5"after adopting the iterative learning algorithm,which meets the performance requirements of infrared camera system.