Multisensor information Fusion Predictive Control for time-varying systems

Aiming at the multisensor discrete-time linear time-varying stochastic controllable system in the linear minimum variance optimal information fusion criterion, based on state space model, a multisensor information fusion weighted by scalars predictive control algorithm for time-varying systems is presented. This algorithm combines the fusion Kalman filter with predictive control, and it solves the control problem of time-varying systems, furthermore it avoids the complex Diophantine equation and it can obviously reduce the computational burden. Comparing to the single sensor case, the accuracy of the predictive control for time-varying systems is evidently improved. A simulation example of the target tracking controllable system with three sensors shows its effectiveness and correctness.