Delayed control of an aeroelastic system based on the BRGWO algorithm and a filtered Smith predictor
LI Nailu,FAN Ruijie,LUO Ziwei,CAO Zhiguang
Author information+
College of Electrical, Energy and Power Engineering, Yangzhou University, Yangzhou 225100, China
{{custom_zuoZheDiZhi}}
{{custom_authorNodes}}
{{custom_bio.content}}
{{custom_bio.content}}
{{custom_authorNodes}}
Show less
文章历史+
收稿日期
修回日期
出版日期
2021-10-19
2022-03-17
2023-02-28
发布日期
2023-02-28
摘要
气弹控制系统的驱动器、闭环信号回路在实际中会存在时滞环节,由于气弹敏感性和环境复杂性,时滞会引起控制信号迟延并导致气弹控制极速恶化、甚至造成系统失稳，该问题以往研究较少。本文针对翼型时滞气弹控制问题,设计了一种基于BRGWO算法和改进型滤波Smith的最优气弹控制方法。首先，引入二阶滤波器改进Smith预估器，设计了翼型气弹控制器；然后，创新设计了一种双向随机灰狼优化算法(bidirectional random grey wolf optimization ，BGWO) ，提高了时滞下气弹控制参数的全局寻优能力，该算法改进了不同等级灰狼的狩猎策略，提高跳出非理想值机率、避免陷入局部最优。利用最小增益原理，在理论上证明了闭环系统稳定性。仿真结果表明，对比传统智能优化算法（如遗传算法、灰狼优化算法）和多种已有控制器（经典Smith、PI-PD型Smith和传统滤波Smith预估器），本文方法具有更强的时滞补偿能力和更优的气弹控制性能，在不确定时滞、不确定风速、刚度变化和驱动干扰等算例下，保持了优良的时滞气弹控制效果，具有较强的鲁棒性。
Abstract
The actuator and closed-loop loop of aeroelastic control system will have time delay in practice. Due to the aeroelastic sensitivity and the environmental complexity, the delayed control signal is easy to lead to the rapid deterioration and even instability of aeroelastic control system. However, this problem is rarely considered in previous studies. To solve the problem of delayed aeroelastic control of the airfoil，an optimal aeroelastic control technique is designed based on bidirectional random grey wolf optimization (BRGWO) algorithm and modified filtered Smith predictor. First, an aeroelastic controller is proposed using improved the Smith predictor based on a second order filter. Then, a novel bidirectional random grey wolf optimization (BRGWO) algorithm is designed to improve the global search of optimal controller parameters under time delay. The BRGWO algorithm is proposed to improve the preying strategy of the wolf in different level, so as to increase the change of leaving non-optimum zone and avoid the local optimum. The stability of the closed-loop system is proved theoretically by small gain theorem. The simulation results show that proposed controller performs better on delayed control compensation and aeroelastic control performance, compared to conventional intelligent optimization algorithm (genetic algorithm, grey wolf optimization) and published methods (classical Smith predictor，PI-PD Smith predictor, traditional filtered Smith predictor). The satisfied delayed control performance of aeroelastic system is maintained with strong rubustness on various cases, such as uncertain time delay, uncertain wind velocity, varying stiffness and load disturbance.
LI Nailu,FAN Ruijie,LUO Ziwei,CAO Zhiguang.
Delayed control of an aeroelastic system based on the BRGWO algorithm and a filtered Smith predictor[J]. Journal of Vibration and Shock, 2023, 42(4): 219-228
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] Poirel DC, Price S J . Structurally nonlinear fluttering airfoil in turbulent flow[J]. AIAA Journal, 2001, 39:1960-1968.
[2] Al-Thehabey OY . Dynamic instability analysis of aeroelastic systems with application to aircraft wings[J]. Physics of Fluids, 2021, 33(9): 094110.
[3] 杨洪磊，张明明，徐建中. 大型风电机组叶片不同方位角下载荷智能控制效果分析[J]. 工程热物理学报，2018，39(02):32-40.
YANG Hong-lei，Zhang Ming-ming，XU Jian-zhong. Characteristics Research and Mechanism Analysis of Large Scale Wind Turbine Blades in Azimuth by Using DTEF[J]. Journal of Engineering Thermophysics，2018，39(02):32-40.
[4] Zhuang C，Yang G，Zhu Y. Effect of morphed trailing-edge flap on aerodynamic load control for a wind turbine blade section[J]. Renewable Energy，2020，148: 964-974.
[5] Liu T，Vibration and aeroelastic control of wind turbine blade based on B-L aerodynamic model and LQR controller[J]. Journal of Vibroengineering，2017，19(2):1074-1089.
[6] 穆安乐，张广兴，李迺璐. 基于分布式襟翼风力机桨叶的模型预测振动控制[J]. 振动与冲击，2018，37(14):79-85.
MU An-le，ZHANG Guang-xing X，LI Nai-lu. Model predictive flow control of wind turbine blades based on distributed flaps[J]. Journal of Vibration and Shock，2018， 37(14):79-85.
[7] Yuan Y，Chen X，Tang J， Multivariable robust blade pitch control design to reject periodic loads on wind turbines[J]. Renewable Energy，2020，146: 329-341.
[8] Li N L，Mu A L，Yang X Y， ect. A novel composite adaptive flap controller design by a high-efficient modified differential evolution identification approach[J]. ISA Transactions，2018，76:197-215.
[9] 桂航，钟绍华，张艺腾. 基于整车十自由度模型的商用车驾驶室半主动悬置系统研究[J]. 振动与冲击， 2021， 40(18): 258-264.
GUI Hang，Zhong Shao-hua，ZHANG Yi-teng. Analysis of the semi-active suspension system of a commercial vehicle cab based on a ten degrees of freedom model [J]. Journal of Vibration and Shock， 2021, 40 (18): 258-264.
[10] Bettayeb M，Mansouri R，Al-Saggaf U，Mehedi IM. Smith Predictor Based Fractional-Order-Filter PID Controllers Design for Long Time Delay Systems [J]. Asian Journal of Control，2017，19(2):587-598.
[11] 陈士安，祖广浩，姚明，张晓娜. 磁流变半主动悬架的泰勒级数-LQG时滞补偿控制方法[J]. 振动与冲击， 2017，36(08):190-198.
CHEN Shi-an，ZU Guang-hao，YAO Ming，ZHANG Xiao-na. Taylor series-LQG control for time delay compensation of magneto-rheological semi-active suspension [J]. Journal of Vibration and Shock，2017, 36 (08): 190-198.
[12] 范剑超，韩敏. 基于动态邻域微粒群的Smith预估双控制器设计[J]. 控制与决策，2012，27(7):1027-1031.
FAN Jian-chao，HAN Min，Smith predictive double controllers design based on dynamic neighbor particle swarm optimization algorithm[J]. Control and Decision，2012，27(7):1027-1031.
[13] Feliu-Batlle V，Rivas-Perez R. Control of the temperature in a petroleum refinery heating furnace based on a robust modified Smith predictor[J]. ISA Transactions，2021，112:251-270.
[14] 宋仁杰，王云宽，范国梁.一种改进的Smith预估控制器[J]. 控制工程，2007，14:88-90.
Song R J，Wang Y K，Fan G L. Design of a Modified Smith Predictive Controller[J]. Control Engineering of China， 2007，14:88-90.
[15] Franklin T S， Santo T L M. Robust filtered Smith predictor for processes with time-varying delay: A simplified stability approach[J]. European Journal of Control，2020， 56:38-50.
[16] Bazhanov V L， Smith predictor in digital feedback control systems[J]. Automation and Remote Control，2010， 71(8):1695-1740.
[17] Sanz R，Garcia P， Albertos P. A generalized Smith predictor for unstable time-delay SISO systems[J]， ISA Transactions，2018，72:197-204.
[18] Ozbek N S， Eker I. Design of an optimal fractional fuzzy gain-scheduled Smith predictor for a time-delay process with experimental application[J]. ISA Transaction，2020，97:14-35.
[19] Gonzalez I B， Perez R R， Batlle V F， et al. Fuzzy Gain Scheduled Smith Predictor for Temperature Control in an Industrial Steel Slab Reheating Furnace[J]. IEEE Latin America Transactions，2016，14(11):4439-4447.
[20] Mehta U，Rojas R. Smith predictor based sliding mode control for a class of unstable processes[J]. Transactions of the Institute of Measurement and Control，2017， 39(5):706-714.
[21] Bouyedda H，Ladaci S，Sedraoui M，Lashab M. Identification and control design for a class of non-minimum phase dead-time systems based on fractional-order Smith predictor and genetic algorithm technique[J]. International Journal of Dynamics and Control，2019， 7:914-925.
[22] Mirjalili S M， Andrew L. Grey wolf optimizer[J]. Advances in Engineering Software，2014，69:46-61.
[23] Li N L， Balas M J， Yang H， Jiang W. Numerical Investigation on the selection of the system outputs for feedback vibration control of a smart blade section[J]. Journal of Vibration and Acoustics-Transactions of the ASME，2016，138(3):1-11.
[24] Strganac T W, Ko J, Thompson D E, Kurdila A J. Identification and Control of Limit Cycle Oscillations in Aeroelastic Systems [J]. Journal of guidance, control, and dynamics, 2000, 23(6):127-1133.
[25] Smith O J. A controller to overcome dead time[J]. ISA Journal，1959，6(2):28-33.
[26] 李迺璐，徐燕，徐庆，葛强. 基于DE优化系统辨识的风力机叶片自校正PID振动控制[J]. 振动与冲击， 2018， 37(06):195-201.
LI Nai-lu，XU Yan，XU Qing，GE Qiang. Vibration control of wind turbine blades based on the self-tuning PID control and differential evolution algorithm [J]. Journal of Vibration and Shock， 2018, 37 (06): 195-201
PDF(2796 KB)
