基于BRGWO算法和滤波Smith预估器的气弹系统时滞控制  

Delayed control of an aeroelastic system based on the BRGWO algorithm and a filtered Smith predictor

作　　者：李迺璐 范瑞杰 骆紫薇 曹智广 LI Nailu;FAN Ruijie;LUO Ziwei;CAO Zhiguang(College of Electrical,Energy and Power Engineering,Yangzhou University,Yangzhou 225100,China)

机构地区：[1]扬州大学电气与能源动力工程学院,江苏扬州225100

出　　处：《振动与冲击》2023年第4期219-228,共10页Journal of Vibration and Shock

基　　金：国家自然科学基金项目(52205132);江苏省自然科学基金项目(BK20180891);国家重点研发计划项目(2019YFE0192600)。

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。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),该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。The actuator and closed-loop loop of aeroelastic control systems 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 the aeroelastic control systems. However, this problemis rarely considered in previous studies. To solve the problem of delayed aeroelastic control of an airfoil, an optimal aeroelastic control technique was designed based on the BRGWO algorithm and a modified filtered Smith predictor. First, an aeroelastic controller was proposed using the improved Smith predictor based on a second order filter. Then, a novel bidirectional random grey wolf optimization(BRGWO) algorithm was designed to improve the global search of optimal controller parameters under time delay. The BRGWO algorithm was proposed to improve the preying strategy of the wolf in different levels, so as to increase the change of leaving non-optimum zoneand avoid local optimum. The stability of the closed-loop system was proved theoretically by the small gain theorem. Simulation results show that the proposed controller performs better on delayed control compensation and aeroelastic control performance, compared to conventional intelligent optimization algorithms(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 the aeroelastic system is maintained with strong rubustness on various cases, such as uncertain time delay, uncertain wind velocity, varying stiffness and load disturbance.

关 键 词：气弹系统 时滞控制 滤波Smith预估器 双向随机灰狼优化算法(BRGWO) 多工况 

分 类 号：TK83[动力工程及工程热物理—流体机械及工程]