Self-tuning Weighted Measurement Fusion Predictive Control
For the multisensor discrete linear time- invariant stochastic control system, based on state space model, under the linear minimum variance optimal information fusion criterion, the multisensor weighted measurement fusion predictive control algorithm is presented. When the noise statistics information is unknown, the measurement function can be dealt with in a unified way to form a new tracking system by least square method, and a multisensor self-tuning weighted measurement fusion predictive control algorithm is presented. The algorithm applies measurement fusion Kalman filter to predictive control and it avoids the complex Diophantine equation, so it can obviously reduce the computational burden. Comparing to the single sensor case, the performance of the predictive control is improved. A simulation example shows the effectiveness and correctness.
Predictive Control Weighted measurement Fusion Self-tuning
Li Yun Hao Gang Zhao Ming Xing Zong-xin Cui Chong-xin Zhang Yu-ru
Harbin University of Commerce, School of Computer and Information Engineering, Heilongjiang, Harbin, Heilongjiang University, Electronic Engineering Institute, Heilongjiang, Harbin, 150080 , China Harbin University of Commerce, Science and Technology Agency, Heilongjiang Harbin 150028, China Department of Electrical and Information Engineering, College of Heilongjiang Science and Techniques
国际会议
The 31st Chinese Control Conference(第三十一届中国控制会议)
合肥
英文
4166-4171
2012-07-01（万方平台首次上网日期，不代表论文的发表时间）