航空发动机在起动电机起动过程中负载特性会随着转速而变化，同时起动环境的差异以及起动电机自身参数的变化均给起动控制带来了难度。为了解决传统控制策略在处理不确定性问题时的不足，提出了一种基于随机进化灰狼优化算法的分数阶自抗扰控制器(REGWO-FO-ADRC)：利用自抗扰控制，增强起动过程中系统的抗扰动能力；结合分数阶控制，抑制由带宽上限引起的观测器估计误差，保证控制品质；设计基于随机进化的灰狼优化算法，对分数阶控制器的控制参数进行在线自整定；用可变的进化速率描述种群更新过程，增加过程中的随机性，提高全局搜索能力和收敛速度。仿真实验表明，设计的控制器能够有效抑制诸多不确定性对系统的影响，改善航空发动机的起动性能。

During the starting process of aero-engine starter motor, the load characteristics change with the speed. At the same time, the difference of starting environment and the change of motor parameters bring difficulties to the start-up control. In order to overcome the shortcomings of traditional control strategies in dealing with these uncertainties, a fractional order active disturbance rejection controller based on random evolution grey wolf optimization (REGWO-FO-ADRC) is proposed. Through the active disturbance rejection control (ADRC), anti-disturbance capability of the system in the starting process is enhanced. The fractional order (FO) control is adopted to suppress the observer estimation error caused by the upper limit of bandwidth and ensure the control quality. The random evolution grey wolf optimization (REGWO) algorithm is designed to realize online self-adaption of the control parameters of the FO controller. The variable evolutionary rate is used to describe the update process of population, which enriches the randomness and improves the global search ability and convergence speed. Simulation results show that the designed controller can effectively suppress the influence of many uncertainties on the system and improve the starting performance of aero-engine.

国家自然科学基金项目(51876089)