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Combining MFAC with an improved cuckoo algorithm for flexible robotic rotary arm joint control

INTERNATIONAL JOURNAL OF INTELLIGENT ROBOTICS AND APPLICATIONS 2025年 1-14页

作者： Liu, Song Anhui Wenda Univ Informat Engn Sch Intelligent Mfg Hefei 231201 Peoples R China

Traditional optimization methods for robotic arm control are prone to getting stuck in local optima and unable to find global optima. In response to these issues, a series of improvement methods are studied to optimize the cuckoo algorithm. The innovation of the research lies in combining the improved cuckoo search algorithm (ICS) with model free adaptive control (MFAC) for attitude control of flexible articulated robotic arms (FRA). By introducing fuzzy neural networks and principal component analysis (PCA) to optimize the cuckoo search algorithm, the search efficiency and accuracy of the algorithm are improved. Meanwhile, a new intelligent algorithm based FRA control model was designed by combining the strong adaptability and robustness of MFAC with the precise tracking ability of sliding mode controller. The experiment demonstrated that the model had excellent performance, achieving an accuracy of 95.4% in just 150 iterations, which was superior to other models. When the robotic arm grasped lightweight and heavy objects, the system control was relatively accurate, with average errors of 0.25 mm and 0.35 mm, respectively. This study not only possesses essential theoretical value for the control of flexible robotic arms but also provides new ideas and methods for the development of related fields.

关键词： Model free adaptive control improved cuckoo algorithm Flexible robotic arm Principal component analysis Attitude control

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Dynamic Aggregation Optimization of Multiple Power Sources in Virtual Power Plant Based on improved cuckoo algorithm Considering Conditional Value at Risk 5

Dynamic Aggregation Optimization of Multiple Power Sources i...

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5th International Conference on Power and Energy Technology, ICPET 2023

作者： Fu, Yongjun Xiong, Zaibao Wang, Jiayong Qu, Yaping Li, Yunwei Jiao, Yu Inner Mongolia Hmhj Aluminium Electricity Co. Ltd Tongliao China State Nuclear Electric Power Planning Design & Research Institute CO. Ltd Beijing China State Grid Changji Electric Power Supply Co. Ltd Wulumuqi China

ISBN: (纸本)9798350339673

The uncertainty of renewable energy output and market electricity price of virtual power plants, such as wind and solar energy, will lead to certain risks in its income. Reasonable allocation of the capacity of wind, photovoltaic, energy storage and conventional units of virtual power plants can reduce system costs and maximize the interests of investors. With the minimum investment and operation costs as the optimization goal, conditional risk value is used as the indicator of risk measurement, A dynamic aggregation optimization model of multiple power sources in virtual power plants based on risk measurement in portfolio theory is established. On this basis, the influence of risk preference on the planning of multiple power sources aggregation optimization in virtual power plants is discussed, and the improved cuckoo algorithm is used to solve the optimization model. Finally, the correctness of the model is verified by an example simulation. The results show that when the manager of the virtual power plant chooses the appropriate risk preference, the dynamic aggregation model of multiple power sources of the virtual power plant can effectively reduce the risk loss of the virtual power plant and improve the stability of the power supply of the virtual power plant. © 2023 IEEE.

关键词： conditional Value at Risk (CVaR) dynamic polymerization improved cuckoo algorithm uncertainty virtual power plant

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ARAIM Protection Level Optimization Based on Feedback-Structure Subset Grouping

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IEEE SENSORS JOURNAL 2025年第7期25卷 11823-11838页

作者： Yan, Jiashuang Fang, Zhibo Sun, Rui Gao, Ming Mao, Yi Jiang, Cheng Xu, Ying Chinese Acad Sci Aerosp Informat Res Inst Beijing 100094 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100049 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Civil Aviat Nanjing 211106 Peoples R China Hohai Univ Coll IOT Engn Changzhou 213022 Peoples R China Beijing Univ Posts & Telecommun Sch Informat & Commun Engn Beijing 100088 Peoples R China

As an advanced algorithm for receiver autonomous integrity monitoring (RAIM), advanced RAIM (ARAIM) has gained considerable attention in the civil aviation sector and is gradually finding applications in other fields. However, with the increasing number of visible satellites, the number of fault subsets processed by the multiple hypothesis solution separation (MHSS) method grows exponentially, imposing a substantial computational burden on the receiver. Furthermore, ARAIM's uniform distribution of integrity and continuity risks among fault subsets results in overly conservative protection levels (PLs). These challenges are often addressed as separate issues. However, this study proposes a novel PL optimization algorithm that incorporates a subset grouping method with a feedback structure to reduce the number of fault subsets, thereby decreasing detection time. In addition, an improved cuckoo search algorithm (ICSA) is developed to allocate integrity and continuity risks more effectively, optimizing the PLs. Experimental results demonstrate the effectiveness of the proposed method. Compared to ARAIM, without IMU, the protection level optimization of proposed algorithm improves by 34.38% and 35.06% in the vertical and horizontal directions, respectively;with IMU, the protection level optimization of proposed algorithm improves by 74.21% and 74.49% in the vertical and horizontal directions, respectively. In addition, due to the fault subsets reduction, the fault detection time is reduced by 54%, 47%, and 26% compared with ARAIM, FSPA, and Feedback ARAIM, respectively.

关键词： Fault detection feedback-structure improved cuckoo algorithm protection levels (PLs) subset grouping feedback-structure improved cuckoo algorithm protection levels (PLs) subset grouping

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Blockchain enabled task offloading based on edge cooperation in the digital twin vehicular edge network

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JOURNAL OF CLOUD COMPUTING-ADVANCES SYSTEMS AND APPLICATIONS 2023年第1期12卷 1-15页

作者： Li, Chunhai Chen, Qiyong Chen, Mingfeng Su, Zhaoyu Ding, Yong Lan, Dapeng Taherkordi, Amir Guilin Univ Elect Technol Sch Informat & Commun Guilin Peoples R China Guilin Univ Elect Technol Sch Artificial Intelligence Guilin Peoples R China Guilin Univ Elect Technol Sch Comp Sci & Informat Secur Guilin Peoples R China Univ Oslo Dept Informat Oslo Norway

The rapid development of the Internet of Vehicles (IoV) along with the emergence of intelligent applications have put forward higher requirements for massive task offloading. Even though Mobile Edge Computing (MEC) can diminish network transmission delay and ease network congestion, the constrained heterogeneous resources of a single edge server and the highly dynamic topology of vehicular edge networks may compromise the efficiency of task offloading, including latency and energy consumption. Vehicular edge networks are also vulnerable to malicious outside attacks. In this paper, we propose a new blockchain-enabled digital twin vehicular edge network (DTVEN) where digital twin (DT) is exploited to monitor network communication, computation, and caching (3C) resources management in real time to provide rich data for offloading decision-making, and blockchain is utilized to secure fair and decentralized offloading transactions among DTs. To ensure 3C resources sharing across edge servers, we design a DT-assisted edge cooperation scheme, which makes full use of edge resources in vehicular networks. Furthermore, a DT-based smart contract is built to achieve a quick and effective consensus process. Then, we apply a task offloading algorithm based on an improved cuckoo algorithm (ICA) and a resource allocation scheme based on greedy strategy to minimize network cost by comprehensively taking into account latency and energy consumption. Numerical results demonstrate that our proposed scheme outperforms the existing schemes in terms of network cost.

关键词： Digital twin Blockchain Edge cooperation improved cuckoo algorithm

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A hybrid adaptive-prediction maximum power point tracking method for the smart city with massive photovoltaic

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ENERGY REPORTS 2022年 8卷 240-251页

作者： Deng, Wenyang Chen, Chuyue Zhang, Yongjun Cheng, Runting Dai, Siting Zhou, Xingyue South China Univ Technol Sch Elect Power Guangzhou 514000 Peoples R China City Univ Macau Fac Finance Taipa 999078 Macao Peoples R China

The partial shadow condition seriously affects the efficiency of the photovoltaic system in the modern city with dense built and other occlusions. From this, the characteristic curve of the photovoltage system shows multi-peak, which further increases the difficulty of getting photovoltaic systems to operate at maximum efficiency. As an efficient technique, the intelligent optimized maximum power point tracking method relies on initialization information and is difficult to balance the tracking performance. Therefore, a hybrid adaptive-prediction maximum power tracking method is proposed in this paper. Firstly, the neighborhood range of the maximum power points is located by the fuzzy predicted mechanism at the upper layer. Secondly, on the bottom layer, based on improving the cuckoo search algorithm, the proposed method uses an interpolation function fitting curve to guide the particles to converge accurately on the bottom layer. At the same time, the output voltage of the system under an open loop is directly controlled by the duty cycle of the control signal, which improves the universality of the method. Finally, the simulation results show that the proposed method is superior to other advanced methods in tracking speed and with smaller power oscillations and comparable tracking accuracy, for which the proposed method is suitable for the city with complex environments and dense buildings. (c) 2022 The Authors. Published by Elsevier Ltd. This is an open access article under theCCBYlicense (http://***/licenses/by/4.0/).

关键词： Partial shadow conditions Intelligent optimization algorithm Adaptive prediction improved cuckoo algorithm

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Short Term Photovoltaic Output Prediction Based on Singular Spectrum Analysis 3

Short Term Photovoltaic Output Prediction Based on Singular ...

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3rd Asia Energy and Electrical Engineering Symposium (AEEES)

作者： Jin, Yan Zhang, Ruiping Lanzhoujiaotong Univ Sch Automat & Elect Engn Lanzhou Peoples R China

ISBN: (纸本)9781665425513

Aiming at the intermittent and fluctuating characteristics of photovoltaic power generation, a short-term photovoltaic forecasting model based on singular spectrum analysis (SSA) and improved cuckoo algorithm (ICS) and extreme learning machine (ELM) is proposed. This method combines the techniques of SSA, correlation analysis and sensitivity analysis, which can effectively improve the prediction accuracy of the model. The time series of PV output is decomposed into trend series, oscillation series and noise series by using SSA technology, and then the sensitivity between PV output and meteorological factors is analyzed. According to the results of sensitivity analysis and benchmark values, the trend series and oscillation series of the forecast day are modified respectively. Then, the fuzzy information granulation is used to effectively mine the noise components, and the minimum, average and maximum values of each window are extracted. The improved cuckoo algorithm (ICS) is used to optimize the noise component parameters of the forecast model. Finally, the photovoltaic output prediction results are obtained by superposition of the correction results and the prediction results. Taking the actual data of a certain area as an example, and through the prediction error analysis, it is proved that the proposed method has higher prediction accuracy.

关键词： singular spectrum analysis meteorological factors fuzzy information granulation improved cuckoo algorithm short-term photovoltaic output forecast

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AUV underwater 3D path planning based on particle swarm optimization-adaptive step-size cuckoo search algorithm 22

AUV underwater 3D path planning based on particle swarm opti...

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Proceedings of the 2022 11th International Conference on Networks, Communication and Computing

作者： Lei Wang Jinghang Li Junyan Qi Junyi He Henan Polytechnic University China

ISBN: (纸本)9781450398039

Aiming at the problems of unreachable search target, weak path finding and obstacle avoidance ability, and slow algorithm convergence speed when dealing with the 3D path planning of autonomous underwater vehicles (AUV) in the traditional cuckoo algorithm in complex waters, an AUV path planning algorithm PSO-ASCS (Particle Swarm Optimization-Adaptive Step-size cuckoo Search algorithm) is proposed, which combines the improved Adaptive Step-size cuckoo Search and Particle Swarm Optimization. This research uses the idea of spatial layering to establish a three-dimensional model of complex waters to conduct path planning and obstacle avoidance experiments on the PSO-ASCS algorithm; The PSO-ASCS algorithm is tested and compared with the adaptive step size cuckoo search algorithm, standard cuckoo algorithm and particle swarm optimization by constructing a fitness function considering the three factors of path length, path smoothness and path hazard. Experiments show that the improved algorithm has strong global search ability and optimization performance, and the algorithm converges well, so that the AUV has the ability of efficient obstacle avoidance and path planning.

关键词： particle swarm optimization Autonomous underwater vehicle improved cuckoo algorithm 3D path planning

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