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.
李迺璐，范瑞杰，骆紫薇，曹智广. 基于BRGWO算法和滤波Smith预估器的气弹系统时滞控制[J]. 振动与冲击, 2023, 42(4): 219-228.
LI Nailu,FAN Ruijie,LUO Ziwei,CAO Zhiguang. Delayed control of an aeroelastic system based on the BRGWO algorithm and a filtered Smith predictor. JOURNAL OF VIBRATION AND SHOCK, 2023, 42(4): 219-228.
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