in: Self-organization: Theories and Methods, WenJun Zhang, Editor, NOVA Science Publishers Inc. , New York, pp.201-224, 2013
In this chapter a Robust Self-Adaptive Kalman Filter (RSAKF) algorithm with the filter gain correction is developed for the case of sensor/actuator malfunctions. The proposed RSAKF utilizes timevariable factors in order to reduce the effect of the faults on the estimation procedure. In this sense, the procedures with single and multiple factors for the adaptation of the filter are presented. In the first case, the filter is adapted by using single adaptive factor as a corrective term on the filter gain while in the second one, an adaptive matrix built of multiple adaptive factors is used to fix the relevant term of the
Kalman gain matrix, individually. After chosing the efficent method of adaptation, an overall concept for the RSAKF is proposed. In this concept, the filter detects the type of the fault, either in the sensors or actuators, and after the fault isolation it applies the required adaptation process such that the estimation characteristic is not deteriorated. Effectiveness of the proposed filters is investigated via simulations for the state estimation problem of an UAV. The results of the presented algorithms are compared for different types of sensor/actuator faults and in this context recommendations about their utilization are given.