Disturbance from luminance variation in the identification of visual sensors causes instability in the control system of target tracking, which leads to field of vision (FOV) motion and even the target missing. To solve this problem, a linear active disturbance reject controller (LADRC) is adopted to the visual tracking and inertial stable platform (VTISP) for the first time to improve the system’s robustness. As a result, the random disturbance from identification can be smoothed by the tracking differentiator (TD).An improved linear extended state observer (LESO) modified by the TD is provided to obtain the high-order state variables for feedback. That makes the system avoid noise in a differential process from the MEMS gyroscope and enhances the response time and stability in tracking control. Finally, simulation and experimental studies are conducted, and the feasibility of the LADRC is verified. Moreover, compared with the other controller in the VTISP for remote sensing, the superiority of the LADRC in system response time and stability is proved by the experiments.

A feedforward strap-down control with a compensator base on the linear extended state observer (LESO) is proposed for a miniaturized camera stabilized platform, which reduces the influence of the dead zone in speed regulation and uncertainties in parameters to reduce the level of angular bias to the field of vision (FOV) in a low-cost stabilized platform. Firstly, the feedforward control is inspired by an approximate linear model proposed for the dead zone to improve the response velocity of the system when tracking the varying reference. Then, the compensator, combining the LESO and proportional differential (PD) law, is designed to eliminate the disturbances including the model bias in the dead zone, inaccuracy in the plant model, and external disturbance. Moreover, the observation performance of the LESO is improved by a preprocessor based on a tracking differentiator (TD) to deal with the time delay and nonlinearities in sampling the state variables. Meanwhile, the complex and uncertain control plant is also simplified by an approximate model combining a disturbance compensator for practical application. Finally, the feasibility of the proposed controller is verified and analyzed by the simulation, and its effectiveness is simultaneously validated by the 2-DOF camera stabilized platform.