基于状态空间模型的广义预测控制算法的控制精度和稳定性取决于系统的输出预

Control accuracy and stability control algorithm generalized state space model based predictive output depends on the prediction accuracy of the system. If a single sensor system, can only get part of the information, when the sensor is subjected to interference or failure, it will seriously affect the accuracy of the estimation system and even cause paralysis; therefore, many modern, high-level, complex systems require the use of multiple sensors to compensate for the lack of a single sensor system. <br>Application of Kalman filtering method, based on the Riccati equation, for a system with correlated noises, under minimum variance linear fusion criterion proposed two weighting matrix sensor information fusion k - step ahead predictor steady state optimal Kalman, gives the optimal weights array specific formula and the minimum prediction error covariance matrix of the fusion. With the single sensor case, can improve the accuracy of the predictor. A simulation example for a tracking system shows its effectiveness.

The control accuracy and stability of the generalized prediction control algorithm based on the state space model depend on the output prediction accuracy of the system.<br>If it is a single sensor system, only part of the information can be obtained, when the sensor is disturbed or failure, it will seriously affect the accuracy of the estimate or even cause the system to crash; Based on the Riccati equation, based on the Riccati equation, the two-pass sensor is proposed under the linear minimum variance fusion criterion, and the two-pass sensor is presented according to the matrix-weighted information fusion advanced k-step steady-state optimal Kalman forecaster, and the formula of optimal weighted array and minimum fusion prediction error variance array is given. Compared with single sensor cases, the accuracy of the forecaster can be improved. A simulation example of a tracking system illustrates its effectiveness.

The control accuracy and stability of GPC algorithm based on state space model depend on the output prediction accuracy of the system. If it is a single sensor system, only part of the information can be obtained. When the sensor is disturbed or fails, it will seriously affect the estimation accuracy and even cause system paralysis. Therefore, many modern, advanced and complex systems need to Multiple sensors are used to make up for a single sensor system.<br>Based on Riccati equation and Kalman filtering method, for the system with correlated noise, under the linear minimum variance fusion criterion, a k-step steady-state optimal Kalman predictor with two sensors fused by matrix weighting information is proposed, and the specific calculation formulas of the optimal weighting matrix and the minimum fusion prediction error variance matrix are given. Compared with the case of single sensor, the accuracy of the predictor can be improved. A simulation example of tracking system shows its effectiveness.<br>