Most control methods for dynamic systems with state space models require knowledge of all state variables. When measurements do not complete control system is complemented by the observer for full state estimation. The most commonly accepted observer form is a Kalman filter but under certain conditions Kalman filter is sensitive to errors and even gives an unstable solution. Therefore there is a need to develop a new alternative ways to design of observers which would be more robust against changes in the model parameters and noise properties.
It is proposed to conduct research and development dealing with new methods for the state evaluation using for this not a local measurements as in Kalman filter but the data set on a sliding interval. The method in this case will be based on variational approach. Previous studies have shown its efficiency and capacity to work. Besides theoretical developments it is planned to demonstrate its capabilities for different applications.
1. Development and research of combinatorial method guaranteed evaluating the solutions of the linear overdetermined system equations with inaccurate parameters
2. Variation method of the linear dynamic systems state evaluation using data on a sliding interval
3. Development of variational method for state estimation of nonlinear systems
4. The use of alternative methods of state estimation to the problems of determining the spacecraft attitude parameters
5. Advanced methods of guaranteed estimation and it’s application to networked state filtering control systems to determine cyber attack in input
6. Search for other applications of guaranteed evaluation methods as an alternative Kalman filter in finding uncertain parameters
7. Application of combinatorial evaluation method in problems of system identification
8. Application of combinatorial methods in spacecraft attitude parameters estimation on the base of star sensors