Blind deblurring of single image is a challenging image restoration problem. Recent various image priors have been successfully explored to solve this ill-posed problem. In this paper, based on the non-local self-similarity, we propose a novel method for blind image deblurring, which can simultaneously capture the intrinsic structure correlation and spatial sparsity of an image. Specifically, we use the hyper-Laplace prior to model the structure information of non-local similar patches, and embed it into the low-rank model as a smooth term of the energy equation. Since the established energy function is non-convex, an effective iterative optimization scheme is designed to effectively implement the proposed algorithm. In addition, we evaluate the proposed method for non-uniform deblurring problem. Extensive experimental results on both synthetic and real-world images show that the proposed method performs competitively against the state-of-the-art methods.

Electric servo system (ESS) is a servo mechanism in a control system of an aircraft, a ship, etc., which controls efficiency and directly affects the energy consumption and the dynamic characteristics of the system. However, the control performance of the ESS is affected by uncertainties such as friction, clearance, and component aging. In order to improve the control performance of the ESS, a control technology combining particle swarm optimization (PSO) and finite time servo system control (FTSSC) was introduced into ESS. In fact, it is difficult to know the uncertain physical parameters of the real ESS. In this paper, the genetic algorithm (GA) was introduced into PSO and the inertia weight was improved, which increased the parameter optimization precision and convergence speed. A new feedback controller is proposed to improve response speed and reduce errors by using FTSSC theory. The performance of the controller based on PSO identification algorithm was verified by co-simulation experiments based on Automatic Dynamic Analysis of Mechanical Systems (ADAMS) (MSC software, Los Angeles, CA, USA) and matrix laboratory (MATLAB)/Simulink (MathWorks, Natick, MA, USA). Meanwhile, the proposed strategy was validated on the servo test platform in the laboratory. Compared with the existing control strategy, the control error was reduced by 75% and the steady-state accuracy was increased by at least 50%.

The electric rudder system (ERS) is the executive mechanism of the flight control system, which can make the missile complete the route correction according to the control command. The performance and quality of the ERS directly determine the dynamic quality of the flight control system. However, the transient and static characteristic of ERS is affected by the uncertainty of physical parameters caused by nonlinear factors. Therefore, the control strategy based on genetic algorithm (GA) identification method and finite-time rudder control (FTRC) theory is studied to improve the control accuracy and speed of the system. Differently from the existing methods, in this method, the difficulty of parameter uncertainty in the controller design is solved based on the ERS mathematical model parameter identification strategy. Besides, in this way, the performance of the FTRC controller was verified by cosimulation experiments based on automatic dynamic analysis of mechanical systems (ADAMS) (MSC software, Los Angeles, CA, USA) and matrix laboratory (MATLAB)/Simulink (MathWorks, Natick, MA, USA). In addition, the advantages of the proposed method are verified by comparing with the existing strategy results on the rudder test platform, indicating that the control accuracy is improved by 70% and the steady-state error is reduced by at least 50%.