On-Line Parameter Estimation Issues for the NASA IFCS F-15 Fault Tolerant Systems
Mario G. Perhinschi, Marco Lando, Luca Massotti, Giampiero Campa Marcello R. Napolitano, Mario Luca Fravolini *Department of Mechanical and Aerospace Engineering, West Virginia University, Morgantown, WV 26506/6106, -Department of Aeronautical Engineering, Polytechnic of Torino, 10129 Torino, Italy. +Department of Electronic and Information Engineering, University of Perugia, 06100 Perugia, Italy.
Sensor and Actuator/Surface Failure Detection Based on the Spectral Norm of an Innovation Matrix
The problem of changes detection in dynamical properties of signals and systems appears in many problems of signal processing, navigation and control (Basseville & Benveniste, 1986; Benveniste et al., 1987; Gadzhiev, 1992; Chen & Patton, 1999; Chan et al., 1999; Hajiyev & Caliskan, 2003; Vaswani, 2004; Tykierko, 2008; Li & Jaimoukha, 2009). Abnormal measurements, sudden shifts appearing in the measuring channel, faultiness of measuring devices, changes in statistical characteristics of noises of an object or of measurements, malfunctions in the computer, and also a sharp change in the trajectory of a monitoring process, etc. should be enumerated among these changes. In real situations of exploiting an object, the problem occurs of operative detection of such changes in order to subsequently correct estimators or to make timely decisions on the necessity and character of control actions with respect to the process of technical exploitation of the object. Under this process, different methods of control and diagnostics are used. Many fault detection methods have been developed to detect and identify sensor and actuator faults by using analytical redundancy (Zhang & Li, 1997; Rago et al., 1998; Maybeck, 1999; Larson et al., 2002; Lee & Lyou, 2002). In (Larson et al., 2002) an analytical redundancy-based approach for detecting and isolating sensor, actuator, and component (i.e., plant) faults in complex dynamical systems, such as aircraft and spacecraft is developed. The method is based on the use of constrained Kalman filters, which are able to detect and isolate such faults by exploiting functional relationships that exist among various subsets of available actuator input and sensor output data. A statistical change detection technique based on a modification of the standard generalized likelihood ratio (GLR) statistic is used to detect faults in real time. The GLR test requires the statistical characteristics of the system to be known before and after the fault occurs. As this information is usually not available after the fault, the method has limited applications in practice. An integrated robust fault detection and isolation (FDI) and fault tolerant control (FTC) scheme for a fault in actuators or sensors of linear stochastic systems subjected to unknown inputs (disturbances) is presented in (Lee & Lyou, 2002). The FDI modules is constructed using banks of robust two-stage Kalman filters, which simultaneously estimate the state and the fault bias, and generate residual 10
Aircraft parameter estimation in frequency domain
The algorithms for the equation error and output error methods, the two basic procedures for the extraction of aircraft parameters from flight data, are formulated in the frequency domain. The output error method includes the maximum likelihood estimation technique. This is further extended to those cases where the measured data is in the form of frequency response curves and the model of an aircraft includes elastic degrees of freedom and unsteady aerodynamics. Then the generalized maximum likelihood method, which can be applied to the identification of an aircraft subjected to external disturbances, is introduced. Next, all estimation methods mentioned are discussed with emphasis on advantages of the frequency domain analysis. The paper concludes with an example using real flight data.