This paper presents linear active disturbance rejection control (LADRC) for a two-degrees-of-freedom (2-DOF) manipulator system to achieve trajectory tracking. The system is widely used in engineering applications and exhibits the characteristics of high nonlinearity, strong coupling, and large uncertainty with two inputs and two outputs. First, the problem of dynamic coupling in the model of the 2-DOF manipulator is addressed by considering the dynamic coupling, model uncertainties, and external disturbances as total disturbances. Second, a linear extended state observer is designed to estimate the total disturbances, while a linear state error feedback control law is designed to compensate these disturbances. The main contribution is that the stability of the closed-loop system with two inputs and two outputs is analyzed, and the relationship between the performance of the closed-loop system and the controller parameters is established. The joint simulation of SolidWorks and Matlab/Simulink is conducted. The simulation and experimental results clearly indicate the superiority of LADRC over the PID for trajectory tracking and dynamic performance.

To comprehensively evaluate the camouflage effect of a camouflaged vehicle in a complex background based on the detection and identification processes of the time-limited search model, the visual saliency degree and feature similarity degree were used to represent the camouflage effect, and a camouflage effect evaluation method was proposed. For the visual saliency degree algorithm, in which the concepts of local saliency degree and target saliency degree were introduced, the visual saliency features of the camouflaged vehicle and background image were manually extracted, multiscale saliency matrices were constructed, and the comprehensive saliency function was used to calculate the visual saliency degree. Based on the bag of visual words model, the feature similarity degree algorithm extracted the local features from public images of the camouflaged vehicles, clustered these features to form a visual word bag, and calculated the feature similarity degree by comparing the local features of the camouflaged vehicle with the visual word bag. Through experimental verification and analysis of the results, the method considering complex landform background characteristics, battlefield target reconnaissance processes, and different camouflage statuses of the vehicle could objectively obtain the camouflage effect value of vehicles, quantitatively determine the advantageous application environment of different camouflaged states, and provide data support for camouflaged state design and concealed area selection.