研究输入信号和干扰信号均可预见情况下的多输入多输出随机系统最优控制器设计，提出 最优控制器由最优输入信号发生器和最优调节器两部分组成，并首次指出最优输入信号发生器为一逆卡尔曼滤波器。

By introducing information fusion techniques into optimal preview control, a new theory of Information Fusion Optimal Preview Control (IFOPC) for nonlinear discrete system is proposed in this paper. All information on control system which includes ideal control strategy, expected objective trajectory and dynamics of system, is regarded as measure information of control system. Therefore, the problem of optimal control can be transferred into the problem of information fusion estimate. It should be pointed out that the IFOPC consists of optimal co-state generator and optimal regulator, in which the optimal co-state generator is just a extended Kalman filter. Firstly, the information fusion estimate theorems are described in brief. Secondly, an algorithm of IFOPC is deduced in detail. Thirdly, some key techniques of IFOPC are discussed, which include the selection of preview step number and the determination of Kalman filter initial values. Finally, the validity of the proposed control strategy is confirmed by the simulation of manipulator shift control. The simulation results show good tracking performance of the IFOPC based nonlinear system.

An extended federated filtering (EFF) algorithm is developed which can be applied to integrated navigation system and integrated attitude determination system. According to both information fusion theories and information conversation principle, it is proposed that information allocation is the inverse process of information fusion in EFF algorithm. The information allocation theorem is given and proved, which lays a theoretical foundation for EFF. Taking a certain satellite integrated attitude determination system as an example, the realization of EFF is discussed in detail. The theoretical analysis and simulation results show that EFF algorithm is less computation than present federated filtering algorithm, and EFF can guarantee both local optimum and global optimum, while present federated filtering can only guarantee global optimum.