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Showing papers by "Ron J. Patton published in 1987"


Journal ArticleDOI
TL;DR: In this article, an extension of the idea of using analytical redundancy to design a match between m components of the observation error space instead of using state estimates has been presented, which eliminates the need for state-space computations, thus producing an effective real-time fault monitor for fly-by-wire aircraft.
Abstract: A new method of analyzing faults in the m measurements of an nth-order system is presented. The proposed approach uses the estimation error space of each observer in a bank of observers to detect and isolate sensor faults. The designs are applied to a nonlinear model of an unmanned aircraft that has been described in previous publications. The reconfigurability of the aircraft sensor system is demonstrated, and the results show rapid recovery from a faulty sensor. The use of the observation error eliminates the need for state-space computations, thus producing an effective real-time fault monitor for fly-by-wire aircraft. N an earlier paper,1 a comparison of two techniques of instrument fault diagnosis (IFD) was made. This work is an extension of Patton and Willcox's idea of using analytical redundancy to design a match between m components of the observation error space instead of using state estimates di- rectly as discussed by Clark,3'4 Clark and Setzer,5 Frank and Keller,6 and Watanabe and Himmelblau.7 IFD in dynamic systems has received a significant amount of attention recently.2"11 Most methods described in the liter- ature discuss the analytical redundancy approach in prefer- ence to the use of redundant hardware. Analytical redundancy provides redundant (estimate) information from different measurements of a process, usually with observer or Kalman filter schemes. The commonly discussed state estimate solu- tion to IFD is based on the principle of generating estimates of part or all of the system state vector from subsets of the measurements, which when compared with similar estimates from other observers can be used to monitor the health of an instrument. The problem with the state estimate solution to IFD arises as the observer requires a good linear model of the process, and it must also be assumed that the disturbances on the system are well modeled or else have an insignificant effect on plant parameter variations. These limitations cause the state estimate approach to be inadequate for many real en- gineering applications. Sensitivity to input-induced parameter variations causes uncertain errors between redundant state estimate vectors, and in an IFD scheme these errors could cause false signaling of an instrument fault. It becomes clear that the bandwidth of uncertain signals should be estimated prior to the IFD system design. The use of frequency domain sensitivity information in this way enables a robust approach to the observer design to be made. The conjecture used is that the "innovations" or prediction error signals contain all the information concerning the parameter variations of the pro- cess being identified and controlled. Attention is thus turned toward the use of an innovations-based approach to system fault diagnosis that has wide potential applications. By using a weighting of the measurement estimation error as a parity

76 citations


Book ChapterDOI
01 Jan 1987
TL;DR: Given the linear state-space representation of a dynamic system, a method for computing allowable eigenvector sub-spaces using Singular Value Decomposition for both real and complex eigenvalues is illustrated and conclusions can be drawn about the robustness of general control system design using eigenstructure assignment.
Abstract: Given the linear state-space representation of a dynamic system, a method for computing allowable eigenvector sub-spaces using Singular Value Decomposition for both real and complex eigenvalues is illustrated. Once these spaces have been determined, it is possible to assign the eigenvectors in two ways, one which performs desirable weightings of the system states for each mode permitting system decoupling and the second which assigns eigenvectors iteratively to make the corresponding eigenvalues as insensitive to perturbations in the system matrices as possible. A computational procedure for each of these techniques is descrioed. The work is illustrated using the stability augmentation system control design problem for the lateral motion model of a non-linear aircraft system. The modal requirements for this problem are well known and this enables a comparison of the two methods to be made. Conclusions can then be drawn about the robustness of general control system design using eigenstructure assignment.

3 citations


Book ChapterDOI
01 Jan 1987
TL;DR: The design of a multivariable sliding mode controller for an unmanned aircraft is described and an emphasis on robust eigenstructure assignment is given and the linear and non-linear model responses are compared.
Abstract: The design of a multivariable sliding mode controller for an unmanned aircraft is described and an emphasis on robust eigenstructure assignment is given. The linear and non-linear model responses are compared when subjected to the same variable structure control design. It is shown that, after sliding has commenced the response of the objective system is tracked by the corresponding nonlinear system. A measure of sensitivity is defined as the proximity of a sub-space of the actual system response from the designed objective.

1 citations