Setup adjustment scheme for multivariate process considering colored observation noises

For the finite horizon multivariate process with setup error, the optimal adjustment scheme is developed to minimize the total process quality loss with quadratic cost and AutoRegressive observation noises. Based on the state-space process control model, the optimal adjustment scheme is derived by Kalman filter on line estimation and linear quadratic Gaussian (LQG) theory. A simulation case is presented to illustrate the implementation method of the optimal adjustment policy. The optimal adjustment scheme is compared with quality adjustment policy with white noise observation noises by simulations. The results show that the proposed adjustment solution is more effective than other to reduce the total quality loss of the process.