The Modified State Prediction Algorithm Based on KF
The state prediction based on Kalman filter (KF) for linear stochastic discrete-time system is investigated. Predicting future states by using the information of available measurements is an effective method to solve time delay problems; it not only helps the system operator to perform security analysis but also allows more time for operator to take better decision in case of emergency. In addition it can make the system real time monitoring, control, and robust. KF is useful not only for state estimation but also for state prediction. However, the accuracy of prediction degrades notably once a filter uses a much longer future prediction. In this paper, a confidence interval (CI) is proposed to overcome the problem. The advantages of CI are that it provides information about states coverage, which is useful for treatment-plan evaluation, and it can be directly used to specify the margin to accommodate prediction errors. Meanwhile, the CI of prediction errors can be used to correct the predictive state, and thereby it improves the prediction accuracy. Simulations are provided to demonstrate the effectiveness of the proposed method.
Kalman filter State Prediction Confidence Interval Bonferroni Interval
Zhen Luo Huajing Fang
Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan Department of Control Science and Engineering, Huazhong University of Science and Technology,Wuhan 4
国际会议
The 31st Chinese Control Conference(第三十一届中国控制会议)
合肥
英文
4075-4078
2012-07-01（万方平台首次上网日期，不代表论文的发表时间）