Please wait a minute...
工程设计学报
Chin J Eng Design  2023, Vol. 30 Issue (3): 271-280    DOI: 10.3785/j.issn.1006-754X.2023.00.040
Theory and Method of Mechanical Design     
Research on adaptive sliding mode control of tower crane based on improved fruit fly optimization algorithm
Yumin HE(),Ying HAN,Jing ZHOU
School of Mechanical and Electrical Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China
Download: HTML     PDF(3089KB)
Export: BibTeX | EndNote (RIS)      

Abstract  

In view of the direct measurement difficulties of load swing angle of tower crane under some working conditions, obvious chattering of the system sliding mode controller and complicated adjustment of controller parameters, an adaptive sliding mode control method for tower crane based on improved fruit fly optimization algorithm was proposed. Firstly, based on the Lagrange equation, the dynamics model of the tower crane single pendulum system was obtained. Then, a linear extended state observer was designed to observe the load swing state of tower crane, and the observation results were fed back to the adaptive sliding mode controller. When constructing sliding mode surface, the hyperbolic tangent function was used instead of the common symbol function to increase its continuity and reduce chattering. Finally, the optimization strategy and search radius of the fruit fly optimization algorithm were improved, and the parameters of the adaptive sliding mode controller were optimized. The results showed that the designed linear extended state observer could track and observe the load swing angle of the tower crane with fast convergence speed and tracking error less than 1.3%. The adaptive sliding mode controller optimized by the improved fruit fly optimization algorithm not only had a good suppression effect on the load swing of tower crane, but also had strong anti-interference and robustness. The proposed control method can effectively avoid safety hazards caused by tower crane load swing while achieving precise positioning, ensuring the safety of workers and the smooth progress of the project.

Key words： tower crane      load swing      extended state observer      fruit fly optimization algorithm      adaptive sliding mode control     
Received: 25 November 2022      Published: 06 July 2023
CLC:  TH 113  
Service
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
RSS
Articles by authors
Yumin HE
Ying HAN
Jing ZHOU
Cite this article:

Yumin HE,Ying HAN,Jing ZHOU. Research on adaptive sliding mode control of tower crane based on improved fruit fly optimization algorithm. Chin J Eng Design, 2023, 30(3): 271-280.

URL:

https://www.zjujournals.com/gcsjxb/10.3785/j.issn.1006-754X.2023.00.040     OR     https://www.zjujournals.com/gcsjxb/Y2023/V30/I3/271


基于改进果蝇优化算法的塔机自适应滑模控制研究

针对部分工况下塔机负载摆角直接测量困难、系统滑模控制器抖振明显以及控制器参数调节复杂等问题，提出了一种基于改进果蝇优化算法的塔机自适应滑模控制方法。首先，根据拉格朗日方程，得到了塔机单摆系统的动力学模型。然后，设计了线性扩张状态观测器，用于观测塔机负载摆动状态，并将观测结果反馈到自适应滑模控制器中；在构造滑模面时，采用双曲正切函数代替常用的符号函数以增加其连续性，从而减小抖振。最后，改进了果蝇优化算法的寻优策略及搜索半径，并对自适应滑模控制器的参数进行了优化。结果表明，所设计的线性扩张状态观测器跟踪观测塔机负载摆角的收敛速度较快且跟踪误差小于1.3%；经改进果蝇优化算法优化后的自适应滑模控制器不仅对负载摆动有较好的抑制作用，而且具有较强的抗干扰性和鲁棒性。所提出的控制方法可在实现精确定位的同时有效避免塔机负载摆动带来的安全隐患，保障工人的安全和工程的顺利开展。


关键词： 塔机,  负载摆动,  扩张状态观测器,  果蝇优化算法,  自适应滑模控制 
Fig.1 Simplified model of tower crane single pendulum system
Fig.2 Overall structure diagram of tower crane anti-swing control system
Fig.3 FOA optimization principle
Fig.4 The first group of linear extended state observer tracking observation simulation results
Fig.5 The second group of linear extended state observer tracking observation simulation results
Fig.6 Comparison of control effect simulation results of adaptive sliding mode controller before and after optimization
性能参数优化前优化后
FOAIMFOA
小车到达位置/m1.101.091.08
小车到达位置用时/s7.366.475.50
负载最大摆角/(°)1.501.000.82
负载残余摆角/(°)0.080.050.02
残余摆角收敛用时/s1.120.900.59
Table 1 Comparison of control performance of adaptive sliding mode controller before and after optimization
Fig.7 Comparison of control effect experimental results of adaptive sliding mode controller before and after optimization
Fig.8 Comparison of control effects of adaptive sliding mode controller under different working conditions
Fig.9 Control effect of adaptive sliding mode controller under interference condition
[1]   王祥，陈志梅，邵雪卷，等.基于滑模自抗扰控制的变绳长塔机防摆控制［J］.机电工程，2023，40（3）：444-451. doi：10.3969/j.issn.1001-4551.2023.03.017
WANG X， CHEN Z M， SHAO X J， et al. Anti-sway control for changing rope length tower crane based on sliding mode active disturbance rejection control［J］. Journal of Mechanical & Electrical Engineering， 2023， 40（3）： 444-451.
doi: 10.3969/j.issn.1001-4551.2023.03.017
[2]   陆峰，张义星，石怀涛，等.基于能量法的多自由度欠驱动塔机防摆控制［J］.控制工程，2023，30（4）：605-612.
LU F， ZHANG Y X， SHI H T， et al. Anti-swing control of underactuated tower crane with multiple degrees of freedom based on energy method ［J］. Control Engineering of China， 2023， 30（4）： 605-612.
[3]   ZHANG M H， ZHANG Y F， JI B， et al. Adaptive sway reduction for tower crane systems with varying cable lengths［J］. Automation in Construction， 2020， 119： 103342.
[4]   LIU X N， CAI T. Variable coefficient active disturbance rejection control for crane system［C］// Proceedings of the 38th Chinese Control Conference. Guangzhou， Jul. 27-30， 2019.
[5]   JIANG W G， DING L Y， ZHOU C. Digital twin： stability analysis for tower crane hoisting safety with a scale model［J］. Automation in Construction， 2022， 138： 104257.
[6]   LU B， FANG Y C， SUN N， et al. Anti swing control of offshore boom cranes with ship roll disturbances［J］. IEEE Transactions on Control Systems Technology， 2018， 26（2）： 740-747.
[7]   QIAN D W， DING H， LEE S G， et al. Suppression of chaotic behaviors in a complex biological system by disturbance observer-based derivative-integral terminal sliding mode［J］. IEEE/CAA Journal of Automatica Sinica， 2020， 7（1）： 126-135.
[8]   ZHAO X S， HUANG J. Distributed-mass payload dynamics and control of dual cranes undergoing planar motions［J］. Mechanical Systems and Signal Processing， 2019， 126： 636-648.
[9]   SUN N， LIANG D K， WU Y M， et al. Adaptive control for pneumatic artificial muscle systems with parametric uncertainties and unidirectional input constraints［J］. IEEE Transactions on Industrial Informatics， 2020， 16（2）： 969-979.
[10]   LIU Z Q， SUN N， WU Y M， et al. Nonlinear sliding mode tracking control of underactuated tower cranes［J］. International Journal of Control， Automation and Systems， 2021， 19（2）： 1065-1077.
[11]   WU T S， KARKOUB M， YU W S， et al. Anti-sway tracking control of tower cranes with delayed uncertainty using a robust adaptive fuzzy control［J］. Fuzzy Sets and Systems， 2016， 290： 118-137.
[12]   SUN N， WU Y M， CHEN H， et al. Anti-swing cargo transportation of underactuated tower crane systems by a nonlinear controller embedded with an integral term［J］. IEEE Transactions on Automation Science and Engineering， 2019， 16（3）： 1387-1398.
[13]   肖仁，吴定会.机械臂固定时间观测器和自适应滑模控制方法的设计［J］.机械科学与技术，2020，39（5）：714-720. doi：10.13433/j.cnki.1003-8728.20190212
XIAO R， WU D H. Design of fixed time observer and adaptive synovial control method for manipulator［J］. Mechanical Science and Technology for Aerospace Engineering， 2020， 39（5）： 714-720.
doi: 10.13433/j.cnki.1003-8728.20190212
[14]   XIAO B， YIN S. Exponential tracking control of robotic manipulators with uncertain dynamics and kinematics［J］. IEEE Transactions on Industrial Informatics， 2019， 15（2）： 689-698.
[15]   赵兴强，刘振，高存臣.机械臂自适应神经网络滑模控制器设计［J/OL］.控制工程：1-6［2022-11-15］..
ZHAO X Q， LIU Z， GAO C C. Adaptive neural network-based sliding mode controller design for manipulator systems［J/OL］. Control Engineering of China：1-6［2022-11-15］. .
[16]   DUMA R， TRUSCA M， DOBRA P. Tuning and implementation of PID controllers using rapid control prototyping［J］. Control Engineering and Applied Informatics， 2011， 13（4）： 64-73.
[17]   ZHOU Y Q， MIAO F H， LUO Q F. Symbiotic organisms search algorithm for optimal evolutionary controller tuning of fractional fuzzy controllers［J］. Applied Soft Computing， 2019， 77： 497-508.
[18]   VERMA B， PADHY P. Robust fine tuning of optimal PID controller with guaranteed robustness［J］. IEEE Transactions on Industrial Electronics， 2020， 67（6）： 4911-4920.
[19]   CHEN H P， BOWELS S， ZHANG B， et al. Controller parameter optimization for complex industrial system with uncertainties［J］. Measurement & Control， 2019， 52（7/8）： 888-895.
[20]   EL-GENDY E M， SAAFAN M M， ELKSAS M S， et al. Applying hybrid genetic： PSO technique for tuning an adaptive PID controller used in a chemical process［J］. Soft Computing， 2020， 24（5）： 3455-3474.
[21]   凤丽洲，王友卫，韩琳琳，等.双重驱动的果蝇优化算法及其在PID控制器中的应用［J］.控制与决策，2021，36（9）：2225-2233. doi：10.13195/j.kzyjc.2020.0046
FENG L Z， WANG Y W， HAN L L， et al. Double drive fruit fly optimization algorithm and its application in PID controller［J］. Control and Decision， 2021， 36（9）： 2225-2233.
doi: 10.13195/j.kzyjc.2020.0046
[22]   赵晓军，刘成忠，胡小兵.基于果蝇优化算法的PID控制器设计与应用［J］.中南大学学报（自然科学版），2016，47（11）：3729-3734. doi：10.11817/j.issn.1672-7207.2016.11.016
ZHAO X J， LIU C Z， HU X B. Design and application of PID controller based on fruit fly optimization algorithm［J］. Journal of Central South University （Science and Technology）， 2016， 47（11）： 3729-3734.
doi: 10.11817/j.issn.1672-7207.2016.11.016
[23]   ZHANG M H， ZHANG Y F， CHENG X G. An enhanced coupling PD with sliding mode control method for underactuated double-pendulum overhead crane systems［J］. International Journal of Control， Automation and Systems， 2019， 17（6）： 1579-1588.
[24]   OUYANG H M， TIAN Z， YU L L， et al. Motion planning approach for payload swing reduction in tower cranes with double-pendulum effect［J］. Journal of the Franklin Institute， 2020， 357（13）： 8299-8320.
[25]   周欢，王坚强，王丹丹.基于Hurwicz的概率不确定的灰色随机多准则决策方法［J］.控制与决策，2015，30（3）：556-560. doi：10.13195/j.kzyjc.2014.0060
ZHOU H， WANG J Q， WANG D D. Grey stochastic multi-criteria decision-making approach based on Hurwicz with uncertain probability［J］. Control and Decision， 2015， 30（3）： 556-560.
doi: 10.13195/j.kzyjc.2014.0060
[1] HU Kun, JIANG Qing-nan, JI Chen-guang, YANG Jian, LI Fei. Model reference sliding mode control of magnetic levitation system based on improved ESO[J]. Chin J Eng Design, 2022, 29(1): 82-91.
[2] HAN Ding, DING Jun. Investigation on PMSM speed regulation system based on TLESO/HLESO/RLESO[J]. Chin J Eng Design, 2018, 25(1): 94-102.
[3] JIA Zhao, MA Song-Ling, LU Ji-Jun, WANG Liang-Liang. Tower crane hook localization algorithm based on the ZigBee[J]. Chin J Eng Design, 2013, 20(4): 348-352.