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Active disturbance rejection heading control of USV based on parameter tuning via an improved pigeon-inspired optimization

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2025年第2期47卷 304-315页

作者： Liu, Yuhang Wei, Chen Duan, Haibin Yuan, Wanmai Beihang Univ Sch Automat Sci & Elect Engn State Key Lab Virtual Real Technol & Syst Beijing 100083 Peoples R China China Elect Technol Grp Corp Informat Sci Acad Beijing Peoples R China

An improved active disturbance rejection control (ADRC) algorithm is proposed in this paper to enhance the heading control capabilities of unmanned surface vehicles (USVs) under wind and wave disturbances. The algorithm introduces two enhancements: parameter tuning and fitting, alongside the optimization of the nonlinear function in the ADRC algorithm. First, the parameter tuning employs an improved pigeon-inspired optimization (PIO) algorithm, which encompasses two strategies: the adaptive strategy and the wandering strategy. Parameter fitting ensures discretely optimized value transition into a continuous state, allowing dynamic parameter adjustments. Second, the optimization of the nonlinear function uses the D-value fitting method. Overall, the improved ADRC algorithm significantly enhances response speed to heading control commands for USVs, fortifying their resistance against wind and wave disturbances. Our proposed algorithm provides a new approach to achieve precise USV heading control.

关键词： Parameter tuning unmanned surface vehicle active disturbance rejection control pigeon-inspired optimization algorithm wind and wave disturbances

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A novel pigeon-inspired optimization with QUasi-Affine TRansformation evolutionary algorithm for DV-Hop in wireless sensor networks

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INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS 2020年第6期16卷 1-15页

作者： Sun, Xiao-Xue Pan, Jeng-Shyang Chu, Shu-Chuan Hu, Pei Tian, Ai-Qing Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao 266590 Peoples R China Flinders Univ S Australia Coll Sci & Engn Adelaide SA Australia Nanyang Inst Technol Sch Software Nanyang Peoples R China

In modern times, swarm intelligence has played an increasingly important role in finding an optimal solution within a search range. This study comes up with a novel solution algorithm named QUasi-Affine TRansformation-pigeon-inspired optimization algorithm, which uses an evolutionary matrix in QUasi-Affine TRansformation Evolutionary algorithm for the pigeon-inspired optimization algorithm that was designed using the homing behavior of pigeon. We abstract the pigeons into particles of no quality and improve the learning strategy of the particles. Having different update strategies, the particles get more scientific movement and space exploration on account of adopting the matrix of the QUasi-Affine TRansformation Evolutionary algorithm. It increases the versatility of the pigeon-inspired optimization algorithm and makes the pigeon-inspired optimization less simple. This new algorithm effectively improves the shortcoming that is liable to fall into local optimum. Under a number of benchmark functions, our algorithm exhibits good optimization performance. In wireless sensor networks, there are still some problems that need to be optimized, for example, the error of node positioning can be further reduced. Hence, we attempt to apply the proposed optimization algorithm in terms of positioning, that is, integrating the QUasi-Affine TRansformation-pigeon-inspired optimization algorithm into the Distance Vector-Hop algorithm. Simultaneously, the algorithm verifies its optimization ability by node location. According to the experimental results, they demonstrate that it is more outstanding than the pigeon-inspired optimization algorithm, the QUasi-Affine TRansformation Evolutionary algorithm, and particle swarm optimization algorithm. Furthermore, this algorithm shows up minor errors and embodies a much more accurate location.

关键词： pigeon-inspired optimization algorithm QUasi-Affine TRansformation evolutionary algorithm evolution matrix DV-Hop algorithm

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Wireless Sensor Network coverage optimization based on Yin-Yang pigeon-inspired optimization algorithmfor Internet of Things

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INTERNET OF THINGS 2022年 19卷

作者： Yin, Jianghao Deng, Na Zhang, Jindan Hubei Univ Technol Sch Comp Sci Wuhan Hubei Peoples R China Xianyang Vocat Tech Coll Xian Shanxi Peoples R China

As an important technology of Internet of Things (IoT), wireless sensor network (WSN) has the problem of low coverage caused by uneven nodes distribution. Aiming at the problem, a WSN coverage optimization method based on the Yin-Yang pigeon-inspired optimization algorithm (Yin-YangPIO) is proposed. Firstly, the good point set is introduced into initialization phase which makes pigeon population more evenly distributed in the solution space;then, YinYang-pair optimization algorithm (YYPO) and pigeon-inspired optimization algorithm (PIO) are combined, and different strategies are used in the map and compass operator and the landmark operator to improve the optimization ability;later on, the opposition-based learning is added to PIO to expand the search range;finally, several functions are selected to prove the optimization ability of the Yin-YangPIO. Through three sets of WSN coverage optimization experiments with different parameters, the effectiveness of the proposed method in WSN coverage optimization is demonstrated.

关键词： Wireless sensor network pigeon-inspired optimization algorithm Yin-Yang-pair optimization algorithm Opposition-based learning Internet of Things

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A multi-strategy pigeon-inspired optimization approach to active disturbance rejection control parameters tuning for vertical take-off and landing fixed-wing UAV

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Chinese Journal of Aeronautics 2022年第7期35卷 19-30,I0001页

作者： Hangxuan HE Haibin DUAN State Key Laboratory of Virtual Reality Technology and Systems Beihang UniversityBeijing 100191China Peng Cheng Laboratory Shenzhen 518000China

In this *** Disturbance Rejection Control(ADRC)is utilized in the pitch control of a vertical take-off and landing fixed-wing Unmanned Aerial Vehicle(UAV)to address the problem of height fluctuation during the transition from hover to level *** the difficulty of parameter tuning of ADRC as well as the requirement of accuracy and rapidity of the controller,a Multi-Strategy pigeon-inspired optimization(MSPIO)algorithm is *** Swarm optimization(PSO),Genetic algorithm(GA),the basic pigeon-inspired optimization(PIO),and an improved PIO algorithm CMPIO are *** addition,the optimized ADRC control system is compared with the pure Proportional-Integral-Derivative(PID)control system and the non-optimized ADRC control *** effectiveness of the designed control strategy for forward transition is verified and the faster convergence speed and better exploitation ability of the proposed MSPIO algorithm are confirmed by simulation results.

关键词： Active Disturbance Rejection Control(ADRC) pigeon-inspired optimization algorithm Transition mode Unmanned Aerial Vehicle(UAV) Vertical take-off and landing

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A Cauchy mutant pigeon-inspired optimization-based multi-unmanned aerial vehicle path planning method

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MEASUREMENT & CONTROL 2020年第1-2期53卷 83-92页

作者： Wang, Bo Hang Wang, Dao Bo Ali, Zain Anwar Nanjing Univ Aeronaut & Astronaut Coll Automat Engn Nanjing 210016 Peoples R China

To improve the performance of multi-unmanned aerial vehicle path planning in plateau narrow area, a control strategy based on Cauchy mutant pigeon-inspired optimization algorithm is proposed in this article. The Cauchy mutation operator is chosen to improve the pigeon-inspired optimization algorithm by comparing and analyzing the changing trend of fitness function of the local optimum position and the global optimum position when dealing with unmanned aerial vehicle path planning problems. The plateau topography model and plateau wind field model are established. Furthermore, a variety of control constrains of unmanned aerial vehicles are summarized and modeled. By combining with relative positions and total flight duration, a cooperative path planning strategy for unmanned aerial vehicle group is put forward. Finally, the simulation results show that the proposed Cauchy mutant pigeon-inspired optimization method gives better robustness and cooperative path planning strategy which are effective and advanced as compared with traditional pigeon-inspired optimization algorithm.

关键词： Unmanned aerial vehicle path planning pigeon-inspired optimization algorithm Cauchy mutant

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Coverage Path Planning of UAV Based on Linear Programming-Fuzzy C-Means with pigeon-inspired optimization

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DRONES 2024年第2期8卷 50-50页

作者： Jiang, Yan Bai, Tingting Wang, Daobo Wang, Yin Nanjing Univ Aeronaut & Astronaut Coll Automat Engn Nanjing 210000 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Astronaut Nanjing 210000 Peoples R China

In contrast to rotorcraft, fixed-wing unmanned aerial vehicles (UAVs) encounter a unique challenge in path planning due to the necessity of accounting for the turning radius constraint. This research focuses on coverage path planning, aiming to determine optimal trajectories for fixed-wing UAVs to thoroughly explore designated areas of interest. To address this challenge, the Linear Programming-Fuzzy C-Means with pigeon-inspired optimization algorithm (LP-FCMPIO) is proposed. Initially considering the turning radius constraint, a linear-programming-based model for fixed-wing UAV coverage path planning is established. Subsequently, to partition multiple areas effectively, an improved fuzzy clustering algorithm is introduced. Employing the pigeon-inspired optimization algorithm as the final step, an approximately optimal solution is sought. Simulation experiments demonstrate that the LP-FCMPIO, when compared to traditional FCM, achieves a more balanced clustering effect. Additionally, in contrast to traditional PIO, the planned flight paths display improved coverage of task areas, with an approximately 27.5% reduction in the number of large maneuvers. The experimental results provide validation for the effectiveness of the proposed algorithm.

关键词： fixed-wing UAV coverage path planning linear programming pigeon-inspired optimization algorithm

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Intelligent attitude planning algorithm based on the characteristics of low radar cross section characteristics of microsatellites under complex constraints

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART G-JOURNAL OF AEROSPACE ENGINEERING 2019年第1期233卷 4-21页

作者： Hua, Bing Liu, Rui-Peng Wu, Yun-Hua Xu, Da-Fu Nanjing Univ Aeronaut & Astronaut Coll Astronaut Microsatellite Engn Technol Res Ctr Nanjing Jiangsu Peoples R China Aerosp Syst Engn Shanghai Peoples R China

The attitude optimization problem of spacecraft under restricted conditions is an important issue of spacecraft planning control. This paper aims at on-orbit microsatellites, which is based on the directional characteristics of their own low radar cross section designs, maintaining low detection probabilities for ground, sea, and space-based detection systems, and simultaneously satisfying the constraint conditions of complex attitude constraints. In this paper, an improved pigeon-inspired optimization algorithm and a nonredundant attitude description method-modified Rodrigues parameters-are used to solve the problem of attitude optimal planning for satellites under complex constraints. This paper focuses on the core evolution mathematical model of the pigeon-inspired optimization algorithm based on the modified Rodrigues parameter, the iterative evolution process of the individual in the pigeon population, and the fitness function model of the individual at different positions. The comparison between the classical pigeon-inspired optimization algorithm and the improved pigeon-inspired optimization algorithm is made in the planned result and resource occupancy, respectively. The simulation results show that the improved algorithm has a faster convergence speed and a smoother optimization result than the classic pigeon-inspired optimization algorithm, where it greatly reduces the computational load and reduces the load of the control system, thus achieving an optimal algorithm.

关键词： Attitude planning complex constraint conditions low radar cross section characteristics pigeon-inspired optimization algorithm modified Rodrigues parameter

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Novel PIO algorithm with Multiple Selection Strategies for Many-Objective optimization Problems

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Complex System Modeling and Simulation 2021年第4期1卷 291-307页

作者： Zhihua Cui Lihong Zhao Youqian Zeng Yeqing Ren Wensheng Zhang Xiao-Zhi Gao School of Computer Science and Technology Taiyuan University of Science and TechnologyTaiyuan 030024China School of Cyberspace Security Beijing University of Posts and TelecommunicationsBeijing 100876China State Key Laboratory of Intelligent Control and Management of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Computing University of Eastern FinlandKuopioFI-70211Finland

With the increase of problem dimensions,most solutions of existing many-objective optimization algorithms are ***,the selection of individuals and the retention of elite individuals are *** algorithms cannot provide sufficient solution precision and guarantee the diversity and convergence of solution sets when solving practical many-objective industrial ***,this work proposes an improved many-objective pigeon-inspired optimization(ImMAPIO)algorithm with multiple selection strategies to solve many-objective optimization *** selection strategies integrating hypervolume,knee point,and vector angles are utilized to increase selection pressure to the true Pareto ***,the accuracy,convergence,and diversity of solutions are *** is applied to the DTLZ and WFG test functions with four to fifteen objectives and compared against NSGA-III,GrEA,MOEA/D,RVEA,and many-objective pigeon-inspired optimization *** results indicate the superiority of ImMAPIO on these test functions.

关键词： pigeon-inspired optimization algorithm many-objective optimization problem multiple selection strategy elite individual retention

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