PID controllers are crucial for industrial control because of their simple structure and good robustness. In order to further improve the accuracy of PID controllers, this paper proposes an improved sparrow search algorithm (ISSA) to prevent the problem of the algorithm being prone to falling into the local optimum at the late stage of iteration. Based on the standard sparrow search algorithm, the position update formula and the step size control parameter are optimized to help quickly jump out of the local, and to obtain the optimal solution in the whole domain. Finally, to verify the accuracy and stability of the improved algorithm, nine standard test functions are first simulated. Then, the PID parameter optimization tests are finished with the chilled water and battery charging systems, where the lifting load and applying perturbation are carried out. Both the simulation and test results show that ISSA improves the convergence speed and accuracy, and performs better in terms of stability.

This study aims to determine optimal forming parameters for Incremental Sheet Forming process Commercially Pure titanium Grade 2 sheets in terms of formability improvement, force reduction, and efficiency of forming. Based on the central composite design, data were collected during 20 runs and then variation analysis was performed. The experiments were performed on a 3 axis CNC milling machine equipped with a Kistler dynamometer plate. Subsequently, regression models have been developed to describe process responses by input factors. As crucial parameters, the relative velocity and step size of the tool that affect the forming force and the height of the fracture have been determined. Finally, the application of optimization algorithm has emerged optimal input factors in terms of selected multi-criteria goal. The results of this study suggest that there is a process window that allows the formation of 45° wall angle drawpieces of commercially pure titanium Grade 2.

A transformer is an important part of power transmission and transformation equipment. Once a fault occurs, it may cause a large-scale power outage. The safety of the transformer is related to the safe and stable operation of the power system. Aiming at the problem that the diagnosis result of transformer fault diagnosis method is not ideal and the model is unstable, a transformer fault diagnosis model based on improved particle swarm optimization online sequence extreme learning machine (IPSO-OS-ELM) algorithm is proposed. The improved particle swarmoptimization algorithm is applied to the transformer fault diagnosis model based on the OS-ELM, and the problems of randomly selecting parameters in the hidden layer of the OS-ELM and its network output not stable enough, are solved by optimization. Finally, the effectiveness of the improved fault diagnosis model in improving the accuracy is verified by simulation experiments.