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.

Measurement of position and velocity of rotating objects relies on installation of additional devices, which can significantly change their dynamic properties. Non-contact methods appear not to have the above-mentioned drawback. To determine the angular kinematics, a video measurement technique stands as a non-contact alternative. The rotational motion can be recorded with a high-speed camera and then analyzed with free and open-source tracking software which allows one to detect and digitize positions of chosen markers and then to calculate angular positions of selected elements. Differentiation process determines rotary speed values. Analysis of long-term dynamical behavior by recording data visualized as position maps which possess half of the information usually stored in the well-known Poincaré maps is proposed.

Training a neural network can be a challenging task, particularly when working with complex models and large amounts of training data, as it consumes significant time and resources. This research proposes a hybrid model that combines population-based heuristic algorithms with traditional gradient-based techniques to enhance the training process. The proposed approach involves using a dynamic population-based heuristic algorithm to identify good initial values for the neural network weight vector. This is done as an alternative to the traditional technique of starting with random weights. After several cycles of distributing search agents across the search domain, the training process continues using a gradient-based technique that starts with the best initial weight vector identified by the heuristic algorithm. Experimental analysis confirms that exploring the search domain during the training process decreases the number of cycles needed for gradient descent to train a neural network. Furthermore, a dynamic population strategy is applied during the heuristic search, with objects added and removed dynamically based on their progress. This approach yields better results compared to traditional heuristic algorithms that use the same population members throughout the search process.