The double pendulum type gantry crane is a typical symmetry underactuated motion system. It has control problems in that the swing of the payload is difficult to suppress and the precise positioning of the trolley is not accurate. A new nonlinear coupling control method based on improved energy is proposed in this paper. We define coupled control signal among trolley, hook and payload. An improved energy storage function is established based on the new coupling control signal. Consequently, a nonlinear anti-swing controller is constructed straightforwardly, and the closed-loop system stability is subject to strict mathematics analysis by Lyapunov and LaSalle’ s theorem. Moreover, the new energy function based on the coupling behaving between the trolley motion and the payload swing leads to the improved control performance. Numerical simulation results show that the proposed method has better performance than traditional controllers. It not only effectively suppresses the swing of the load and the hook, but also precisely controls the displacement of the trolley. It has strong robustness to the displacement of the payload, the change of the gantry crane parameters and the external disturbance.

The movement of the gantry crane is controlled by an symmetry underactuated system, and has poor robustness in precise positioning. A new active control method based on the machine vision positioning is proposed in this paper, and the trajectories are planned after the detection of starting and ending points. A new type of energy storage function is given in this paper, and a coupling control law is derived to minimize the load swing in the process of precise positioning. The equilibrium point of the closed-loop system is checked though Lyapunov and LaSalle’s theorems, and the calculation results are verified through experimental investigations. The results show that the equilibrium points are asymptotically stable, and the proposed control method is of better robustness. The findings provide a new kind of control method with higher efficiency, and can help with the precise control of gantry cranes.

Image stitching aims at generating high-quality panoramas with the lowest computational cost. In this paper, we present an improved parallax image-stitching algorithm using feature blocks (PIFB), which achieves a more accurate alignment and faster calculation speed. First, each image is divided into feature blocks using an improved fuzzy C-Means (FCM) algorithm, and the characteristic descriptor of each feature block is extracted using scale invariant feature transform (SIFT). The feature matching block of the reference image and the target image are matched and then determined, and the image is pre-registered using the homography calculated by the feature points in the feature block. Finally, the overlapping area is optimized to avoid ghosting and shape distortion. The improved algorithm considering pre-blocking and block stitching effectively reduced the iterative process of feature point matching and homography calculation. More importantly, the problem that the calculated homography matrix was not global has been solved. Ghosting and shape warping are significantly eliminated by re-optimizing the overlap of the image. The performance of the proposed approach is demonstrated using several challenging cases.