This study addresses the optimization of control performance for automatic voltage regulator systems by proposing a fractional-order PIDD² (FOPIDD²) controller design method based on the hybrid Black-winged Kite Algorithm (BWOA). To overcome the challenges of complex parameter tuning and adaptability to high-dimensional nonlinear optimization in PID controllers, the BWOA integrates the precise search mechanism of the Black-winged Kite Algorithm (BKA) with the spiral encircling strategy of the Whale Optimization Algorithm (WOA). By dividing high-fitness individuals into subgroups for parallel optimization, combined with an elitism preservation mechanism and Levy flight perturbation, the BWOA effectively balances global exploration and local exploitation capabilities, preventing premature convergence. Furthermore, a multi-factor objective function is adopted to optimize the six parameters of the FOPIDD² controller. Numerical simulations in MATLAB evaluate the controller’s performance across multiple dimensions, including transient response, frequency-domain stability, trajectory tracking, parameter uncertainty, and disturbance rejection, with comparisons to other recent controllers. Simulation results demonstrate that the BWOA-FOPIDD² controller achieves superior performance in most metrics. Therefore, the proposed method provides an efficient hybrid optimization framework for AVR system controller design.

Tracking extended targets aims to estimate the kinematic state and shape of the target of interest with a varying number of noisy detections received by a sensor. The key challenge in this problem stems from its nonlinearity and high dimensionality due to the target maneuver and model complexities. This paper presents a modified adaptive extended Kalman filter based on the random hypersurface model (RHM) to address this problem. First, the target maneuver is judged by using the input estimate (IE) chi-square detector. Then, the magnitude of the target maneuver is used to modify the prior of the shape parameters. Based on the prior information, we derive an extended Kalman filter for a closed-form recursive measurement update. The simulation and experimental results demonstrate the usefulness of the proposed method for tracking the maneuvering star-convex extended target.

To deal with the problem of multitarget tracking with measurement origin uncertainty, the paper presents a multitarget tracking algorithm based on Adaptive Network Graph Segmentation (ANGS). The multitarget tracking is firstly formulated as an Integer Programming problem for finding the maximum a posterior probability in a cost flow network. Then, a network structure is partitioned using an Adaptive Spectral Clustering algorithm based on the Nyström Method. In order to obtain the global optimal solution, the parallel A* search algorithm is used to process each sub-network. Moreover, the trajectory set is extracted by the Track Mosaic technique and Rauch–Tung–Striebel (RTS) smoother. Finally, the simulation results achieved for different clutter intensity indicate that the proposed algorithm has better tracking accuracy and robustness compared with the A* search algorithm, the successive shortest-path (SSP) algorithm and the shortest path faster (SPFA) algorithm.

This paper proposes a bearing fault diagnosis method based on the Dempster–Shafer evidence fusion of cloud model memberships from multi-channel data, which provides an explicable calculation process and a final result. Firstly, vibration signals from the drive end and fan end of the rolling bearing are used as dual-channel data sources to extract multi-dimensional features from time and frequency domains. Then, cloud models are employed to build models for each feature under different conditions, utilizing three digital characteristic parameters to characterize the distribution and uncertainty of features under different operating conditions. Thus, the membership degree vectors of test samples from two channels can be calculated using reference models. Subsequently, D-S evidence theory is applied to fuse membership degree vectors of the two channels, effectively enhancing the robustness and accuracy of the diagnosis. Experiments are conducted on the rolling bearing fault dataset from Case Western Reserve University. Results demonstrate that the proposed method achieves an accuracy of 96.32% using evidence fusion of the drive-end and fan-end data, which is obviously higher than that seen in preliminary single-channel diagnosis. Meanwhile, the final results can give suggestions of the possibilities of anther, which is benefit for technicists seeking to investigate the actual situation.

For non-ellipsoid extended target tracking, the process disturbances and measurement noises are described by independent random variables, i.e. Gaussian distribution. In many real scenarios, however, the disturbances and noises are completely unknown except for bounds. This paper proposes a novel set-member filter for maneuver non-ellipsoid extended target tracking with unknown but bounded noise. The non-ellipsoid extent of the extended target is described by the Random Hypersurface Model (RHM), the predicted state and shape pa-rameters are obtained by calculate the Minkowski sum of the ellipsoids of the state and shape parameters and unknown but bounded noises, respectively. At the measurement update stage, the predicted shape parameters are modified based on the maneuver angle, and the minimal ellipsoid containing the pseudo-measurement nonlinear function is cal-culate by solving the semi-positive definite programming problem, in which the trace of the shape matrix of the pseu-do-measurement nonlinear function is as the objective function. The update shape parameters are updated by calcu-lating the intersection set of predicted shape ellipsoid and the minimal pseudo-measurement ellipsoid. An evaluation of performance in a simulated environment shows that the proposed SMF-NEET approach outperforms the existing RHM-AEKF and RHM-UKF methods with up to 83.9% and 88.9% improvement in the accuracy in position, 40.3% and 58.2% in velocity, 26.6% and 31.5% in the shape, respectively, respectively, while tracking a maneuver non-ellipsoid extend target with unknown but bounded Noises.

Multi target tracking refers to the use of data obtained by radar sensors to track and identify the position, speed and other related attributes of multiple targets or objects. The association between measurements and possible origins within the target range is difficult, especially for occlusion or detection blind spots, which can lead to missing measurements. This paper aims to solve this challenge by solving the data association component in tracking. A multi target tracking algorithm based on Generalized maximum multi clique problem (GMMCP) is proposed. The data association problem is transformed into a mixed binary shaping programming problem. The Ipsolver toolbox is used to solve this problem. The optimal solution is obtained, and the accuracy of the method is calculated by experiments. The results are compared with the other three algorithms to verify the effectiveness of the proposed algorithm.