Aiming at the leader-following consensus problem of heterogeneous descriptor multi-agent systems under unknown nonlinear exogenous disturbance， a distributed control protocol was proposed. Firstly， a disturbance observer was designed to observe the nonlinear exogenous disturbance， and the convergence condition of the observation error was derived. Secondly， a distributed control protocol was designed on the basis of the state observer， so as to achieve leader-following consensus of the system to the leader， and its sufficiency and rationality were proved on the basis of graph theory， matrix theory， and descriptor system theory. Finally， the design and analysis of the control protocol were extended to the leader-following bipartite consensus problem of heterogeneous descriptor multi-agent systems based on competition and cooperation relationships. Simulation results demonstrate that the proposed control protocol achieves the object control effectively. Comparison experimental results with systems without disturbance observer further verify the necessity of the disturbance observer design. It can be seen that the proposed protocol further extends the applicability of consensus theory in complex environments.

To solve the problem that high dimension of descriptor decreases the matching speed of Scale Invariant Feature Transform (SIFT) algorithm, an improved SIFT algorithm was proposed. The feature point was acted as the center, the circular rotation invariance structure was used to construct feature descriptor in the approximate size circular feature points' neighborhood, which was divided into several sub-rings. In each sub-ring, the pixel information was to maintain a relatively constant and positions changed only. The accumulated value of the gradient within each ring element was sorted to generate the feature vector descriptor when the image was rotated. The dimensions and complexity of the algorithm was reduced and the dimensions of feature descriptor were reduced from 128 to 48. The experimental results show that, the improved algorithm can improve rotating registration repetition rate to more than 85%. Compared with the SIFT algorithm, the average matching registration rate increases by 5%, the average time of image registration reduces by about 30% in the image rotation, zoom and illumination change cases. The improved SIFT algorithm is effective.