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In view of the large randomness, redundant path nodes, and low search efficiency of RRT-connect in a complex obstacle environment, this study intends to develop a path-planning method combining RRT-connect and Informed RRT*. First, to solve the problem of large sampling randomness, the Informed RRT* algorithm is combined to adopt a simpler rectangle and limit the sampling range to the rectangle. Second, for the poor quality of the search path, the dynamic step size is used for growth extension, the reverse greedy algorithm is used to delete redundant nodes, the spline curve is used to smooth the path such that the position meets the cubic spline curve and the speed meets the quadratic spline curve, and the final path is optimized. Finally, the proposed algorithm is verified in the simulation and real world using a self-developed quadrupedal robot. Compared with the original RRT-connect algorithm, the first solution time, total number of nodes, and initial path cost were reduced by more than 11%, 8.5%, and 2.5%, respectively.

Satellite borne flexible structure is a multi-degree-of-freedom system, which contains complex dynamic characteristics such as time-varying parameters, geometric nonlinearity, gap nonlinearity, and so on. Flexible structure suspension typically results in geometric nonlinearity. The oscillation equation with nonlinear term is established according to the law of motion of a nonlinear pendulum and considering the influence of medium swing angle and lateral force. The perturbation approach is used to get the relationship between vibration frequency and the nonlinear term, and the impact of factors on vibration characteristics is investigated. The satellite borne flexible structure’s active vibration control (AVC) system is then established. Considering proportional differential (PD) or fuzzy control adjustment, variable step size least mean square (VSS-LMS) adaptive filtering algorithm is used to calculate the control signal, and considering the influence of geometric nonlinearity, the actuator is used to suppress the vibration of the satellite borne flexible structure. Finally, the vibration response’s amplitude under steady-state excitation significantly decreases as an outcome of the vibration control simulation.

The Spider Wasp Optimization (SWO) algorithm is a swarm intelligence optimization technique inspired by the collective behaviors of social animals. This algorithm, designed to address optimization challenges, emulates the unique hunting, nesting, and mating behaviors of female spider wasps. It offers several advantages, including rapid search speed and high solution accuracy. However, when tackling complex optimization problems, it can encounter issues such as getting trapped in local optima, slow early convergence, and the need for manual adjustment of the “Trade-off Rate” (TR) parameter for different problems.To improve the performance and versatility of the SWO algorithm, a Multi-strategy Improved Spider Wasp Optimizer (MISWO) is proposed. Firstly, the Grey Wolf Algorithm is integrated into the initialization phase to enhance early convergence and improve the fitness of the initial population, thereby boosting the algorithm’s global optimization capabilities.Secondly, an adaptive step size operator and Gaussian mutation are introduced during the search phase to automatically adjust the search range at different optimization stages. This enhancement increases both the optimization accuracy and the algorithm’s ability to avoid local optima. The Trade-off Rate (TR) is dynamically selected to better accommodate a variety of problems. Finally, a dynamic lens imaging reverse learning strategy is employed to update optimal individuals, further improving the algorithm’s capacity to escape local optima. To validate the effectiveness of MISWO, it was tested on 23 benchmark functions and 7 engineering optimization problems, and compared with several state-of-the-art algorithms. Experimental results show that MISWO outperforms other algorithms in terms of optimization capability, stability, and adaptability across diverse problems.

Path-planning algorithms play a crucial role in improving the operational efficiency of robots. However, achieving efficient path planning in high-dimensional spaces, particularly for manipulators, is still a challenge. To address the limitations of the conventional rapidly exploring random tree (RRT) algorithm, such as low search ability, poor adaptability to complex environments, high randomness, and tortuous paths, this paper proposes an improved RRT algorithm for obstacle avoidance path planning of manipulators. Firstly, a kinematics model of the manipulator is established, followed by forward kinematics, and the reachable workspace is evaluated by Monte Carlo method. Then, an adaptive tree expansion and sampling strategy is introduced, guided by collision feedback, to effectively balance global exploration and local guidance. An initial path is rapidly generated through a global adaptive step-size strategy, and a smooth, feasible trajectory suitable for the manipulator’s motion is obtained via path optimization. To evaluate the performance of the proposed algorithm, three different environments, including both 2D and 3D scenarios, were constructed in MATLAB. The improved RRT was compared with the standard RRT, Bi-Directional RRT (Bi-RRT), Goal-Directed RRT (G-RRT), improved G-RRT, and Restricted Sampling Area RRT (RSA-RRT). Simulation results show that the improved RRT outperforms these algorithms, with lower average running time, iterations, path cost, and other performance metrics. In addition, the feasibility and practicality of the proposed algorithm are further validated through path planning and obstacle avoidance experiments conducted both in MATLAB and on a physical manipulator.

The charging station location model is a nonlinear programming model with complex constraints. In order to solve the problems of weak search ability and low solution accuracy of the whale optimization algorithm (WOA) in solving location models or high-dimensional problems, this paper proposes an improved whale optimization algorithm (IWOA) based on hybrid strategies. Chaos mapping and reverse learning mechanism are introduced in the original algorithm, and the change mode of convergence factor and probability threshold is improved. Through optimization experiments on 18 benchmark functions, the test results show that IWOA has the best solution ability. Finally, IWOA is used to solve a site selection optimization model aiming at the minimum comprehensive cost. The results show that the proposed algorithm and model can effectively reduce the comprehensive cost of site selection. This provides a necessary decision-making reference for the scientific site selection for electric vehicle charging stations.

In order to improve the convergence speed of the nonlinear spline adaptive filter (SAF) in the identification of block-oriented systems, an interval variable step-size algorithm is proposed. Traditional SAF algorithm uses constant step size during iteration, leading to a contradiction between convergence speed and steady-state accuracy. In this paper, a new kind of variable step-size algorithm is proposed, fully considering the particularity of spline interpolation in the nonlinear part of the block-oriented model. The step size of each interpolation interval is independent from that of other intervals, and it is dominated by the correlated squared error which is evaluated by an exponential-weighted averaging (EWA) process. In this paper, the independent step size in each interpolation interval is also updated through an EWA process of the correlated error. The effects of the parameters on the convergence performance of the proposed strategy have been theoretically analyzed and verified by simulations. Finally, some numerical simulations have confirmed that the proposed interval variable step-size approach can significantly improve the convergence speed as well as reduce the steady-state error compared with the traditional SAF and the existing variable step-size SAF algorithms.

This paper presents a distributed optimization strategy for reactive power–voltage control in distribution networks with high photovoltaic (PV) penetration under limited sensor deployment scenarios. To address voltage violations and minimize network power losses, a novel distributed optimization framework is developed that utilizes selective nodal measurements from PV-integrated nodes and critical T-junction locations, coupled with inter-node communication for information exchange. The methodology integrates an adaptive step size algorithm within a dynamic projected primal–dual distributed optimization framework, eliminating manual parameter tuning requirements while ensuring theoretical convergence guarantees through Lyapunov stability analysis. Comprehensive validation on the IEEE 33-bus distribution test system demonstrates that the proposed strategy achieves significant performance improvements. The distributed control framework reduces measurement infrastructure requirements while maintaining near-optimal performance, demonstrating superior economic efficiency and operational reliability. These results establish the practical viability of the proposed approach for real-world distribution network applications with high renewable energy integration, providing a cost-effective solution for voltage regulation under incomplete observability conditions.

This study proposes a canopy-adaptive TAD-IRRT* (target-biased sampling, artificial potential field, and dynamic step-size informed rapidly-exploring random tree star) algorithm to solve the collision-free 3D path-planning problem for a 6-DOF apple-harvesting robotic arm. To improve computational speed and search directionality, the method integrates target-biased sampling and a distance-regulated artificial potential field (APF) into the Informed-RRT* framework. Furthermore, an obstacle-distance-based dynamic step-size mechanism is introduced to optimize spatial exploration. The generated routes undergo greedy path pruning and cubic B-spline smoothing to ensure kinematic executability. The simulation results in complicated ROS-based scenarios demonstrate that the TAD-IRRT* algorithm achieves a 100% planning success rate, reducing the average computational time and joint-space path length by approximately 60.1% and 15.6%, respectively, compared to the standard Informed-RRT*. Kinematic analysis via Fourier curve fitting (R[sup.2]=0.9849) confirms continuous angular velocity and acceleration without high-frequency chattering. Physical prototype experiments in the dense-obstacle scenarios show that the proposed method increases the path execution success rate by 36.7% and reduces the average execution time by 41% compared to the standard Informed-RRT* algorithm. The proposed approach effectively balances high-quality path generation with low computational overhead, providing a reliable and safe solution that significantly reduces mechanical wear.

In a k -step adaptive linear multistep methods the coefficients depend on the k − 1 most recent step size ratios. In a similar way, both the actual and the estimated local error will depend on these step ratios. The classical error model has been the asymptotic model, c h p + 1 y ( p + 1) ( t ), based on the constant step size analysis, where all past step sizes simultaneously go to zero. This does not reflect actual computations with multistep methods, where the step size control selects the next step, based on error information from previously accepted steps and the recent step size history. In variable step size implementations the error model must therefore be dynamic and include past step ratios, even in the asymptotic regime. In this paper we derive dynamic asymptotic models of the local error and its estimator, and show how to use dynamically compensated step size controllers that keep the asymptotic local error near a prescribed tolerance tol . The new error models enable the use of controllers with enhanced stability, producing more regular step size sequences. Numerical examples illustrate the impact of dynamically compensated control, and that the proper choice of error estimator affects efficiency.

This work aims to improve the performance of direct torque control (DTC) technique for induction motor based photovoltaic (PV) water pumping system (PVWPS). The innovative aspect of this work consists in introducing the adaptive fuzzy logic control and the fuzzy logic control techniques as alternative approaches to conventional DTC to control the PVWPS. To ensure a good operation of the PV array, a variable step size incremental conductance (VSS INC) is implemented. Simulation studies of the proposed topology based on intelligent approaches will be investigated using Matlab/Simulink under various operating conditions to validate the suitability of the proposed PVWPS. From the obtained results, the proposed control strategies appear to be very convenient for water pumping applications. Article Highlights An intelligent control based on the advanced techniques is proposed for PV water pumping system. An adaptive fuzzy logic PID approach and optimal fuzzy rules are proposed for better operation of PV system. The suggested PV water pumping system achieves better performance, in particular minimization of torque and flux ripples, reduction of torque overshoot and high dynamic response.