摘要
针对生产阶段有限的多变量自相关生产制造过程,研究了过程动态噪声为有色噪声、调整花费成本为二次型函数情形下的过程调整策略问题.在建立过程状态空间方程的基础上,利用卡尔曼滤波方法在线估计过程的状态变量,根据随机二次型最优控制理论,得到了使过程质量损失最小的最优调整策略.通过算例解释了最优调整策略的实现方法,并进行了仿真验证.结果表明,提出的调整策略与过程噪声为白噪声时的调整策略相比,能更有效地减少过程的总体质量损失.
For the finite horizon multivariate autocorrelation manufacturing process, the optimal adjustment scheme is developed to minimize the total process quality loss with quadratic adjust- ment cost and process dynamic noises with AutoRegressive model. Based on the state-space process control model, the optimal adjustment scheme was derived by Kalman filter on line esti- mation and linear quadratic Gaussian（LQG） theory. A simulation case was presented to illustrate the implementation method of the optimal adjustment policy. The optimal adiustment scheme was compared with quality adjustment policy with white noise by simulations. The results show that the pro- posed adjustment solution is more effective than other to reduce the total quality loss of the process.
出处
《工程设计学报》
CSCD
北大核心
2013年第6期529-533,共5页
Chinese Journal of Engineering Design
基金
国家自然科学基金资助项目(70931004
71071107)
国家杰出青年科学基金资助项目(71225006)
关键词
多变量过程
统计过程控制
状态空间模型
卡尔曼滤波
最优调整
multivariate process
statistical process adjustment
state-space model
Kalman filter
optimal adjustment
