In view of the shortcomings of Probabilistic Roadmaps (PRM) in the case of narrow passages, an improved method based on optimal sampling strategy is proposed. By sampling the dense area of the obstacles, the sampling points distributed inside the obstacle are selected and uniformly sampled by the distanced, so that the sampling point is generated around the obstacle in the free area, thereby increasing the number of sampling points in the narrow passages. The simulation results show that the improved PRM has more sampling points in the narrow passages than the standard PRM. And the time of path planning, the success rate and the path length are also significantly improved.

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.

Many meta-heuristic algorithms have been used in designing PID controllers to control automatic voltage regulator (AVR) system. PIDD ² controller is a PID controller with second-order derivative gain. The Fireworks Whale Optimization Algorithm is an advanced metaheuristic algorithm that combines fireworks and whale optimization algorithms. It has good exploratory properties because it explores a large potential space and avoids premature local optima. This paper presents a PIDD ² controller for improving the performance of the AVR system using a hybrid whale optimization algorithm. The simulation results, divided into two parts based on whether the limitation of the excitation voltage is considered or not, include a detailed analysis of the proposed controller in terms of step response, stability analysis, trajectory tracking, parameter uncertainty, and rejection of disturbance. To demonstrate the effectiveness of the proposed controller of the AVR system, it was compared with the controllers in recent literature in each of the aforementioned terms. The proposed controller, in most comparisons, provides better results than other controllers. All these results demonstrate that the performance and robustness of the proposed controller are competitive.

Multi-robot coverage systems are widely used in operations such as environmental monitoring, disaster rescue, and pollution prevention. This study considers inherent positioning errors in positioning systems and ground mobile robots with limited communication distance and poor quality in practice. A centroidal Voronoi tessellation algorithm-based formation control technology for multi-robots is optimized. First, by constructing buffered Voronoi cells (BUVCs) for each robot, the collision avoidance ability of the multi-robot formation movement is improved. Next, the formation control problem of multi-robots in a limited communication range and non-convex environment is realized via discrete Voronoi partitioning, a communication distance constraint, and an obstacle avoidance strategy. Simulation and experiment results demonstrate that the proposed method can effectively solve the position generation problem of multi-robot coverage systems in a non-convex environment with actual sizes of the robots and positioning system errors and can further improve the collision avoidance performance of robots and the robustness of BUVC algorithms.

T-type three-level inverters have been extensively utilized in renewable energy generation, motor drive systems, and other power conversion applications. However, failures in semiconductor devices critically reduce the operational reliability of power conversion systems. While significant progress has been made in the diagnosis of single-switch open-circuit (OC) faults, the precise location and detection of simultaneous double-switch OC faults remain challenging. Therefore, this paper proposes a fault diagnosis method, integrating an improved adaptive sliding mode observer (IASMO) and dynamic current threshold detection. First, the IASMO is constructed through the hybrid logic dynamic model, achieving accurate and rapid estimation of phase currents. Then, integrating estimated with actual currents accomplishes the design of detection variables and adaptive thresholds. Subsequently, fault location variables are formulated to achieve accurate localization of both single-switch and double-switch faults. Finally, Simulation and experimental results demonstrate that the proposed method effectively identifies 18 types of OC faults within 75% of the current cycle, with high efficiency and robustness.