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GVP-RRT: a grid based variable probability Rapidly-exploring Random Tree algorithm for AGV path planning

COMPLEX & INTELLIGENT SYSTEMS 2024年第6期10卷 8273-8286页

作者： Zhou, Yaozhe Lu, Yujun Lv, Liye Zhejiang Sci Tech Univ Sch Mech Engn Hangzhou 310018 Peoples R China Zhejiang Sci Tech Univ Longgang Inst Wenzhou Peoples R China

In response to the issues of low solution efficiency, poor path planning quality, and limited search completeness in narrow passage environments associated with Rapidly-exploring Random Tree (RRT), this paper proposes a Grid-based Variable Probability Rapidly-exploring Random Tree algorithm (GVP-RRT) for narrow passages. The algorithm introduced in this paper preprocesses the map through gridization to extract features of different path regions. Subsequently, it employs random growth with variable probability density based on the features of path regions using various strategies based on grid, probability, and guidance to enhance the probability of growth in narrow passages, thereby improving the completeness of the algorithm. Finally, the planned route is subjected to path re-optimization based on the triangle inequality principle. The simulation results demonstrate that the planning success rate of GVP-RRT in complex narrow channels is increased by 11.5-69.5% compared with other comparative algorithms, the average planning time is reduced by more than 50%, and the GVP-RRT has a shorter average planning path length.

关键词： Path planning AGV optimization algorithm RRT

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Design optimization of Metasurface Filter Integrated With Base Station Antenna for Sub-6 GHz 5G Communications 31

Design Optimization of Metasurface Filter Integrated With Ba...

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31st Asia-Pacific Microwave Conference (APMC) - Microwave Linking the World

作者： Hwang, In-June Yun, Dal-Jae Lee, In-Ho Lee, Dong-Joon Uh, Kee-Han Hong, Young-Pyo KRISS Electromagnet Wave Metrol Grp Daejeon South Korea KRISS Sci Instruments Performance Evaluat Team Daejeon South Korea KRISS AI Convergence Technol Dev Team Daejeon South Korea ICH Co Ltd Ctr Res & Dev Gunpo South Korea

ISBN: (纸本)9781665494182

This paper proposes a metasurface filter integrated with a base station antenna for sub-6 GHz 5G communications. The metasurface filter, composed of a pixel-based geometric structure, is optimized using the conformational space annealing (CSA) algorithm. The proposed metasurface filter exhibits bandpass characteristics around the 3.5 GHz frequency, commonly used in sub-6 GHz 5G networks. The performance of the metasurface filter combined with a 4x1 patch antenna array was measured in a fully anechoic chamber. The measured gain pattern shows that the metasurface filter passes the signal with a small insertion loss in the passband and effectively suppresses out-of-band frequencies, providing frequency-selective performance.

关键词： Antenna array electromagnetic metasurface frequency selective filter optimization algorithm 5G mobile communication

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Enhanced Meta-Heuristic Approach for Echo Suppression in Synthetic Aperture Radar Using Non-Periodic Interrupted Sampling

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TRAITEMENT DU SIGNAL 2025年第2期42卷 1097-1110页

作者： Chukka, Anoosha Battula, Tirumala Krishna Anil Neerukonda Inst Technol & Sci Dept ECE Visakhapatnam 531162 India Jawaharlal Nehru Technol Univ Kakinada Dept ECE Kakinada 533003 India

Echo cancellation is a deception jamming technique designed to nullify the target's echo and prevent its detection by enemy radar. Traditional methods rely on time-domain synchronization, but they struggle to fully suppress echoes when there is an amplitude mismatch. This limitation often leaves the target partially visible in synthetic aperture radar (SAR) images, reducing the effectiveness of the jamming technique. Non-periodic interrupted sampling modulation (NP-ISM) is applied as it produces a continuous jamming strip instead of discrete false targets whereas in Periodic sampling multiple false targets emerge even the real target echo is canceled which makes it easy to be countered by enemy radar. The improved henry gas solubility optimization algorithm (IHGSOA) is applied in the echo cancellation model to optimize the random rectangular envelope pulse train in the NP-ISM which enhances signal cancellation. This significantly reduces the Peak to Average Ratio (PAR), enabling more effective target echo suppression. A polynomial non-linear frequency modulation (PNLFM) is employed to reduce the PAR leading to significant improvement in signal-to-noise ratio (SNR), enabling more effective target echo cancellation. The proposed IHGSOA-based echo cancellation model effectively aligns the cancellation and echo signals through time-delay synchronization, minimizing errors even in the presence of a mismatch. The simulation results show that using the above cancelling system the desired target echo amplitude drops to 50% and 40% of its original target echo, hence the system has a very high cancellation effect. Thus, the effectiveness of the IHGSOA is evaluated by comparing its performance with other conventional algorithms.

关键词： echo cancellation synthetic aperture radar (SAR) non-periodic interrupted sampling modulation improved henry gas solubility optimization algorithm peak to average ratio polynomial non-linear frequency modulation (PNLFM) NLFM

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Analytical Solution for Third-Body Perturbations with Double Averaging

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2025年第2期48卷 384-392页

作者： Nie, Tao Zhang, Shijie Yi, Tao Ren, Jiadong Beijing Inst Technol Key Lab Autonomous Navigat & Control Deep Space Ex Sch Aerospace Engn Beijing 100081 Peoples R China Yinhe Hangtian Beijing Commun Technol Co Ltd Beijing 100081 Peoples R China Shanghai Acad Spaceflight Technol Shanghai Inst Satellite Engn Shanghai 201109 Peoples R China

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Dimension-Reduced optimization for Uniform Near-Field Synthesis of Irregular Arrays

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IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS 2022年第5期21卷 908-912页

作者： Guo, Shang Zhao, Deshuang Wang, Bing-Zhong Univ Elect Sci & Technol China Sch Phys Chengdu 611731 Peoples R China Univ Elect Sci & Technol China Yangtze Delta Reg Inst Hu Zhou Huzhou 313000 Peoples R China

A novel optimization method of a significantly reduced optimizing dimension is presented to quickly synthesize the uniform near fields of irregular arrays. In this method, the element excitations of the array are no longer executed as the direct optimization parameters. Instead, we build a new search space made of the smooth reference masks (SRMs) and perform the optimization directly based on the parameters of the SRMs rather than the element excitations. To obtain the desired uniform near fields, only two critical parameters of the SRMs need to be optimized. Comparing the direct element-excitation optimizations, the proposed method significantly reduces the optimizing dimension and makes the synthesis much more efficient and faster. Several numerical examples and their comparisons with the well-developed convex optimization algorithm were presented. The results demonstrate the effectiveness and the significant reduction of time consumption of the proposed method.

关键词： optimization Antenna arrays Microwave theory and techniques optimization methods Microwave antenna arrays Electromagnetic heating Search problems Array pattern near-field synthesis optimization algorithm uniform microwave field (UMF)

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Dynamic model construction and parameter identification for the intelligent mining electric shovel considering different disturbances

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STRUCTURAL AND MULTIDISCIPLINARY optimization 2025年第5期68卷 1-22页

作者： Hu, Zhengguo Lin, Shibin Gong, Zhuangzhuang He, Xiwang Pang, Yong Song, Xueguan Dalian Univ Technol Sch Mech Engn Dalian 116024 Peoples R China State Key Lab High Performance Precis Mfg Dalian 116024 Peoples R China

The harsh working environment of the front-end mechanism of mining electric shovels poses significant challenges for intelligent excavation. The intelligent mining electric shovel relies on the dynamic model of the front-end mechanism during excavation trajectory planning and tracking control, but accurately constructing the dynamic model of large electric shovels is difficult. Therefore, a dynamic model parameter identification framework for intelligent mine shovel is proposed in this paper. Firstly, the dynamic model of the front-end working mechanism of the intelligent mine electric shovel (IMES) is constructed based on the Lagrange method, and it is simplified and linearized. Then, an optimal excavation excitation trajectory optimization method based on high order polynomials is proposed to generate excitation trajectory. In order to improve the identification accuracy, a hybrid filtering method combining mean filtering and wavelet filtering is proposed in this paper, which is used to process the experimental data of optimal excitation trajectory and ensure that the data meet the identification requirements. Finally, two identification methods of dynamic model parameters are proposed, which are based on adaptive ridge regression and optimization algorithm. Through these two methods, the dynamic parameters of IMES are identified respectively under different uncertain disturbance conditions. Through numerical simulation and experimental verification, the feasibility of the framework for dynamic parameter identification of IMES is proved. The proposed framework provides theoretical support for subsequent trajectory planning and adaptive tracking control of IMES.

关键词： Intelligent mine electric shovel Dynamic model Dynamic parameter identification Optimal excavation excitation trajectory Adaptive ridge regression optimization algorithm

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Trajectory tracking control of a 6-DOF robotic arm based on improved FOPID

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INTERNATIONAL JOURNAL OF DYNAMICS AND CONTROL 2025年第4期13卷 1-14页

作者： Jiang, Zhou Zhang, Xiaohua Liu, Guoquan Anhui Automobile Vocat & Tech Coll Dept Mech & Elect Engn Hefei 230601 Anhui Peoples R China Zhongkai Univ Agr & Engn Coll Automat Guangzhou 510225 Guangdong Peoples R China East China Univ Technol Sch Mech & Elect Engn Guanglan Ave 418 Nanchang 330013 Peoples R China

Robotic arm is a complex system with multiple inputs and outputs, strong nonlinearity and strong coupling, and the research of high precision trajectory tracking control technology for robotic arm has been an important issue for scholars at home and abroad. This paper takes the six-degree-of-freedom (6-DOF) robotic arm as its study object and designs a fractional-order PID (FOPID) control method. To improve its control accuracy, a parameter tuning method of fractional-order beetle antennae particle swarm algorithm (FBPA) optimized FOPID controller is proposed. This method puts the beetle antennae search (BAS) algorithm together with the particle swarm optimization (PSO) algorithm, introduces the concept of fractional-order calculus into the algorithm, dynamically adjusts the inertial weights and fractional order and finally improves the optimization effect of the algorithm. The simulation experiments of MATLAB/Simulink indicate that in comparison with the traditional PID control method, the FOPID control method optimized by the FBPA has high control accuracy and small overshooting, which meets the high-precision control requirements of the 6-DOF robotic arm.

关键词： 6-DOF robotic arm Trajectory tracking optimization algorithm FOPID controller

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Vehicle-Mounted SRM DITC Strategy Based on Optimal Switching Angle TSF

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WORLD ELECTRIC VEHICLE JOURNAL 2025年第1期16卷

作者： Wang, Hongyao Wu, Jingbo Xie, Chengwei Guo, Zhijun Henan Univ Sci & Technol Coll Vehicle & Traff Engn Luoyang 471003 Peoples R China

Switched reluctance motors (SRMs) offer several advantages, including a magnet- and winding-free rotor, high mechanical strength, and exceptional output efficiency. However, the doubly salient pole structure and high-frequency switching power supply result in significant torque ripple and electromagnetic noise, which limit the application in the field of new energy vehicles. To address these issues, this paper proposes a direct instantaneous torque control (DITC) strategy based on an optimal switching angle torque sharing function (TSF). Firstly, an improved cosine TSF is designed to reasonably distribute the total reference torque among the phases, stabilizing the synthesized torque of SRM during the commutation interval. Subsequently, an improved artificial bee colony (ABC) algorithm is used to obtain the optimal switching angle data at various speeds, integrating these data into the torque distribution module to derive the optimal switching angle model. Finally, the effectiveness of the proposed control strategy is validated through simulations of an 8/6-pole SRM. Simulation results demonstrate that the proposed control strategy effectively suppresses torque ripple during commutation and reduces the peak current at the beginning of phase commutation.

关键词： control strategy optimization algorithm switched reluctance motor torque sharing function

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Prediction of weld morphology in laser-welded 316L stainless steel using a multilayer feedforward neural network

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WELDING IN THE WORLD 2025年第7期69卷 1787-1797页

作者： Diao, Yalong Shi, Wenqing Zhang, Bingqing Jiang, Longwei Lin, Yiming Guangdong Ocean Univ Sch Elect & Informat Engn Zhanjiang 524088 Guangdong Peoples R China Guangdong Ocean Univ Sch Mat Sci & Engn Yangjiang 529500 Guangdong Peoples R China

This study aims to predict the melt pool depth and width of 316L stainless steel welds during laser welding using a multilayer feed-forward neural network (MLFNN). The Taguchi method was employed to design the laser welding parameters and generate experimental data on melt depth and width. This allowed for an in-depth investigation of the effects of these parameters on the melt pool characteristics of 316L stainless steel. The results demonstrate that the MLFNN model, with a 3-10-10-10-2 structure, exhibits high accuracy and stability across training, validation, and testing phases. The correlation coefficient R-value between predicted and experimental results is 0.99995, indicating an excellent fit to the experimental data. The model's predictions can effectively reduce defects in 316L stainless steel during laser welding, significantly enhancing weld quality.

关键词： Laser welding 316L stainless steel Neural network optimization algorithm Melt pool prediction

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Joint ABS Deployment and TBS Antenna Downtilt optimization for Coverage Maximization

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IEEE WIRELESS COMMUNICATIONS LETTERS 2022年第7期11卷 1329-1333页

作者： Li, Huan Zhai, Daosen Zhang, Ruonan Wang, Chen Tang, Xiao Northwestern Polytech Univ Sch Elect & Informat Xian 710072 Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China

In this letter, we consider an air-and-ground cooperative network, where several aerial base stations (ABS) help terrestrial base stations (TBS) for coverage enhancement. In this network, we first quantify the space-time coverage ratio (STCR) by fully considering the antenna models and the dynamic of the ABS, and then formulate a joint ABS deployment and TBS antenna downtilt optimization problem with the objective to maximize the STCR of the concerned area. The objective function involves many control variables and judgement operations, which make the problem very complex. To solve the problem effectively, we first adopt the genetic algorithm (GA). Using the solutions of the GA as training samples, we propose a deep neural network architecture to further reduce the computational time. Simulation results indicate that the proposed GA significantly improves the coverage ratio and the deep neural network (DNN) architecture achieves orders of magnitude acceleration in computational time with acceptable performance.

关键词： Antennas Genetic algorithms Interference Three-dimensional displays Wireless communication Signal to noise ratio optimization Cooperative communication unmanned aerial vehicle network coverage optimization algorithm

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