李冀, 邓传涛, 余城, 胡国友, 冷新龙, 贺红林. UAV发射平台调平系统的NSSCA-PID控制方法[J]. 南昌航空大学学报(自然科学版), 2020, 34(2): 79-84. DOI: 10.3969/j.issn.2096-8566.2020.02.012
引用本文: 李冀, 邓传涛, 余城, 胡国友, 冷新龙, 贺红林. UAV发射平台调平系统的NSSCA-PID控制方法[J]. 南昌航空大学学报(自然科学版), 2020, 34(2): 79-84. DOI: 10.3969/j.issn.2096-8566.2020.02.012
Ji LI, Chuan-tao DENG, Cheng YU, Guo-you HU, Xin-long LENG, Hong-lin HE. NSSCA-PID Control for Leveling System of UAV Launching Platform[J]. Journal of nanchang hangkong university(Natural science edition), 2020, 34(2): 79-84. DOI: 10.3969/j.issn.2096-8566.2020.02.012
Citation: Ji LI, Chuan-tao DENG, Cheng YU, Guo-you HU, Xin-long LENG, Hong-lin HE. NSSCA-PID Control for Leveling System of UAV Launching Platform[J]. Journal of nanchang hangkong university(Natural science edition), 2020, 34(2): 79-84. DOI: 10.3969/j.issn.2096-8566.2020.02.012

UAV发射平台调平系统的NSSCA-PID控制方法

NSSCA-PID Control for Leveling System of UAV Launching Platform

  • 摘要: PID控制器是目前实现UAV(Unmanned Aerial Vehicle)发射平台在复杂环境下的自适应调平的主要手段,其控制性能取决于参数整定的品质。基于经典的正弦余弦算法架构,提出了一种嵌入边界缓冲策略的邻域搜索正弦余弦算法(Neighborhood Searching Sine Cosine Algorithm, NSSCA)用于整定PID控制器参数。以单位阶跃信号作为调平系统输入,邻域搜索正弦余弦算法优化PID控制调平系统的上升时间为0.04 s,调整时间为0.106 s,最大超调量为5.44%,表明邻域搜索正弦余弦算法对PID控制器参数的整定效果优于Z-N法、遗传算法、灰狼优化算法和经典正弦余弦算法。

     

    Abstract: PID controller is an efficient approach to realize the adaptive leveling for UAV(Unmanned Aerial Vehicle) launching platform in complex environment, the performance of which is relied on the quality of parameter tuning. Aiming at tuning parameters of PID controller, a Neighborhood Searching Sine Cosine Algorithm (NSSCA) embedded by boundary buffering is proposed in the context of classical Sine Cosine Algorithm (SCA) in this paper. With unit step signal as the input, the rising time, adjustment time and the maximum overshooting of the leveling system under the control of Neighborhood Searching Sine Cosine Algorithm optimized PID are 0.04 s, 0.106 s and 5.44%, respectively. It indicates the parameters of PID controller tuned by Neighborhood Searching Sine Cosine Algorithm achieve a more satisfied performance than that tuned by Z-N method, Generic Algorithm, Grey Wolf Optimization and classical Sine Cosine Algorithm.

     

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