一种改进PSO-ARMA半球谐振陀螺温度误差建模方法

为减小半球谐振陀螺(HRG)在温度效应下产生的漂移，建立了温度漂移补偿模型，对与温度有关的确定性漂移进行了补偿。提出了一种改进PSO-ARMA建模方法，对不确定性漂移进行了补偿。改进的PSO-ARMA建模方法将惯性权值递减策略引入到反向学习粒子群优化(PSO)算法中，提高算法跳出局部、快速收敛的能力，在建模时利用改进的PSO算法对ARMA参数寻优，以提高模型的精度。利用半球谐振陀螺升温实验数据进行了检验，经该模型补偿后，陀螺输出精度可达0.07°/h，且较传统ARMA建模方法精度提高了一倍。 

An improved PSO-ARMA method for temperature error modeling of hemispherical resonator gyroscope

To reduce the drift of hemispherical resonator gyroscope (HRG) caused by temperature effect, a temperature drift compensation model is established, which compensates the deterministic drift related to temperature. An improved PSO-ARMA modeling method is proposed to compensate the uncertain drift. This method introduces the decreasing inertia weight strategy into the opposition-based learning particle swarm optimization (PSO) algorithm, improving the algorithm's ability to jump out of the local and converge fast. In the modeling process, the improved PSO algorithm is used to optimize the ARMA parameters, thus improving the model accuracy. The experimental data of HRG temperature rise are used for verification. After the model compensation, the output accuracy of HRG can reach 0.07°/h, which is twice as high as that of traditional ARMA modeling method.