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.