Bolts constitute a very important subset of mechanical fasteners. In order to tighten bolts, a degree of bolt preload scatter is to be expected. Since the torque control of tightening bolts is the most popular means of controlling the preload, an appropriate tightening torque becomes pivotal. This paper investigates the torque control problem of bolt tightening process. This process is not as simple as it looks because the inherently nonlinear process contains many uncertainties. To conquer the adverse effects of the uncertainties, this paper designs an adaptive-gain second-order sliding mode controller. Theoretically, such design can guarantee that the bolt tightening process has the closed-loop stability in the sense of Lyapunov. From the aspect of practice, the control method is carried out by a platform. Some comparisons illustrate the feasibility and effectiveness of the designed controller.

This paper presents a super-twisting-based sliding mode control method for the formation problem of multi-robot systems. The multiple robots contain plenty of uncertainties and disturbances. Such a control method has two adaptive gains that can contribute to the robustness and improve the response of the formation maneuvers despite these uncertainties and disturbances. Based on the leader-follower frame, this control method was investigated. The closed-loop formation stability is theoretically guaranteed in the sense of Lyapunov. From the aspect of practice, the control method was carried out by a multi-robot system to achieve some desired formation patterns. Some numerical results were demonstrated to verify the feasibility of the control method. Some comparisons were also illustrated to support the superiority and effectiveness of the presented sliding mode control method.

This paper addresses a second-order sliding mode control method for the formation problem of multirobot systems. The formation patterns are usually symmetrical. This sliding mode control is based on the super-twisting law. In many real-world applications, the robots suffer from a great diversity of uncertainties and disturbances that greatly challenge super-twisting sliding mode formation maneuvers. In particular, such a challenge has adverse effects on the formation performance when the uncertainties and disturbances have an unknown bound. This paper focuses on this issue and utilizes the technique of an extreme learning machine to meet this challenge. Within the leader–follower framework, this paper investigates the integration of the super-twisting sliding mode control method and the extreme learning machine. The output weights of this extreme learning machine are adaptively adjusted so that this integrated formation design has guaranteed closed-loop stability in the sense of Lyaponov. In the end, some simulations are implemented via a multirobot platform, illustrating the superiority and effectiveness of the integrated formation design in spite of uncertainties and disturbances.

Inspired by human vision, retina-like imaging has the feature of rotation and scaling invariance, which is beneficial for target tracking and recognition. However, there is a limitation that the center of field of view (FOV) should match with the target centroid. Currently, there is no effective method to quantitatively estimate the effects from this mismatch. To solve the issue, a novel evaluation method is proposed by applying the log-polar transform and Fourier transform. Then reconstruct the target image by exchanging magnitude and phase information between the target image and its rotated or scaled counterpart. Using two current methods of digital image processing, scale-invariant feature transform (SIFT) and biaxial projection similarity analysis (BPS), as evaluation criteria, the proposed method performs efficiently for all eccentric angles. Simulation results show that for a specific target, the invariance of rotation and scaling works effectively for a value above 0.69.

The dynamics model of cylindrical roller bearing (CRB) in aeroengine main shaft was promoted and solved by Hilber–Hughes–Taylor (HHT) integer algorithm with variable step in combination with symmetric multiprocessing (SMP) parallel solving technology, and a finite element model of roller to cage contact was built. The dynamic characteristics of CRB at the stage of start-up and stop were analyzed firstly, and then, the collision forces between rollers and cage were used as the boundary conditions of the finite element model to discuss the influences of working conditions, structural parameters, and materials on the stress distribution and safety characteristic of cage at the stage of start-up and stop. The findings will provide the theoretical basis for the designing of CRB in aeroengine main shaft.