摘要
提出了一种基于椭球定界的微机电系统(MEMS)陀螺模型辨识与误差补偿方法。首先,建立了随机漂移的自回归模型,并针对模型随时间变化的特征,引入具有递推特性的定界椭球自适应约束最小二乘法(BEACON),实现模型参数的动态辨识,提高建模精度;然后,提出一种未知但有界(UBB)噪声条件下的定界椭球自适应状态估计(BEASE)算法,用于角速率的估计;采用新的加权策略和优化准则进行量测阶段的更新,并推导了此框架下的状态可行集更新过程及其最优参数求解方法。将该方法应用于MEMS陀螺信号的处理,验证了其有效性和改进性能。
A novel model identification and error compensation technique for micro-electronic-mechanical system （MEMS） gyroscope based on ellipsoida/ bounding algorithm is proposed. Firstly, an autoregressive model for stochastic drift is established. For the time-varying characteristics of the MEMS gyroscope error model, a real-time recursive method called bounding ellipsoidal adaptive constrained least-squares （BEACON） algorithm is adopted to realize the dynamic parameter identification and improve the modeling accuracy. Then, a bounding ellipsoidal adaptive state estimation （BEASE） algorithm with unknown but bounded （UBB） disturbances is proposed to estimate the regular rate. The new weighting strategy and optimization criterion are used at observation updating stage. The updating process of the feasible state set and the selection method of the optimal parameters are reduced under this framework. The method is applied to process the MEMS gyroscope signals and the experiment results to verify the efficiency and improved performance of the proposed method.
出处
《宇航学报》
EI
CAS
CSCD
北大核心
2017年第11期1219-1225,共7页
Journal of Astronautics
基金
国家自热科学基金(61503390
61503392)
关键词
MEMS陀螺仪
参数辨识
状态估计
最优定界椭球算法
有界噪声
MEMS gyroscope
Parameter identification
State estimation
Optima/ bounding ellipsoid algorithm
Bounded noise
