Research Article  Open Access
Jianwei Liu, Bin Jiang, Ke Zhang, "Fault Diagnosis for Linear Discrete Systems Based on an Adaptive Observer", Mathematical Problems in Engineering, vol. 2013, Article ID 343524, 5 pages, 2013. https://doi.org/10.1155/2013/343524
Fault Diagnosis for Linear Discrete Systems Based on an Adaptive Observer
Abstract
This paper presents a fault diagnosis algorithm to estimate the fault for a class of linear discrete systems based on an adaptive fault estimation observer. And observer gain matrix and adaptive adjusting rule of the fault estimator are designed. Furthermore, the adaptive regulating algorithm can guarantee the firstorder difference of a Lyapunov discrete function to be negative, so that the observer is ensured to be stable and fault estimation errors are convergent. Finally, simulation results of an aircraft F16 illustrate the advantages of the theoretic results that are obtained in this paper.
1. Introduction
As the scale and complexity of modern control systems are increasing, the requirements on system reliability are also increasing. Therefore, the design and analysis of fault detection and diagnosis (FDD) algorithms have received considerable attention during the past three decades. The development of FDD has been addressed by more and more authors and fruitful results have been obtained; see [1–3].
The observer technology is one of the important methods for FDD and faulttolerant control [4, 5]. Commonly used observerbased fault estimation methods include sliding mode observers [6], unknown input observers [7], filtering methods [8], neural networks observers [9], and adaptive observers [10, 11]. But most of them focus on the continuous systems, and only few results have been reported on the fault estimation design in discretetime systems. The discretetime is widely used in practical implementations, for example, computer control systems, networked control systems, and so forth [12]. Reference [13] designed an FD filter for a class of linear discretetime systems in a networked environment. Reference [14] used an adaptive fault observer to deal with discretetime systems, but it did not involve the issue of fault estimation. Reference [15] also dealt with discretetime nonlinear systems, but its inequality functions were complex and it was difficult to get solutions. On the other hand, aircraft flight control systems are good examples for applications of fault accommodation/active reliable control, and the fault observers have been employed to detect faults [11].
In this paper, a novel discretetime actuator fault estimation scheme is proposed to deal with abrupt actuator failures. The proposed actuator fault estimation scheme is then applied by using the Lyapunov method. Simulation results of a numerical example are also given.
The paper is organized as follows. Section 2 describes the mathematical preliminaries and problem formulation. In Section 3, concerning the theoretical results of the proposed fault diagnosis scheme, a fault estimation scheme is proposed to deal with the actuator failures of discretetime system. In Section 4, an example of aircraft flight control system is given to illustrate the performance of the proposed scheme. The concluding remarks are given in Section 5.
2. System Description and Preliminaries
Consider a discretetime linear system where is the state, is the control input, with is the function to model the actuator faults, and is the measurable output. Matrices , , , and are real matrices of appropriate dimensions. Matrix is of full column rank; that is, rank .
For estimating the actuator fault , an adaptive observer is constructed as follows: where is the observer state vector, is the observer output vector, is the estimate of the actuator fault , is the observer gain to be designed.
Denoting that then the estimation error dynamics is modeled as follows:
3. Main Results
3.1. Modified Adaptive Fault Estimation Algorithm
Theorem 1. For constant actuator fault , if there exist matrices and and positive scalars and defined such that the following condition holds: then the algorithm and the fault estimation can realize estimation error of both the state and fault uniformly bounded for the entire time period.
Proof. From system (4), one gets , so the algorithm (7) becomes Then, Because is constant, and ; then one gets Based on (4) and (11), we can obtain the following augmented system: Consider the following Lyapunov function: Then, Denoting that , then , and is defined as (6).
Remark 2. The pair is observable, and define the observe matrix . So, it gets that the rank of is . When is full column rank,we have
At the first step, the online fault estimation is as follows:
And at the th step, fault estimation is as (8).
If is not full column rank, then more system outputs and observer estimations should be used to get the state estimation.
When the output satisfies Lipschitz condition, define , and define and ; then one can get
Then, fault estimation is .
Remark 3. Equation (6) is a nonlinear matrix inequality about matrices and , and it is not easy to be calculated, so let . And after we choose , the solution can be found easily.
Remark 4. If the fault is not constant but is a linear function, such as , then Consider the same Lyapunov function . We can also get The proof is smilar to that of Theorem 1 and it is omitted here for brevity.
4. Simulation Results
In this section, the fault estimation algorithm is applied to a model of the vertical dynamics of an F16 aircraft. The model is taken from [16]. The signals and their generation in the simulations are summarized in Table 1, where size means the variance for the inputs and constant magnitude for the faults, respectively.

We have the following numerical values in (1): By solving conditions in Theorem 1, one can obtain the following solutions after iterations: = [0.0001 0 0; 0 0.0001 0; 0 0 0.0002; 0.0001 0.0001 0; 0 0.0001 0.0001]; = 1, = 0.1. Then, one can take the learning rate = [189923 −147.6 646.2 2443 1727.8; −147.6 100.6 1.31 −0.58 1.24; 646.2 1.31 9365 −465.8 −1.27; 2443 −0.589 −465.8 71.3 −5.26; 1727.8 1.24 −1.27 −5.26 87.5].
In this simulation, it is assumed that three kinds of actuator faults are, respectively, created as The fault estimation result is shown in Figure 1, while Figure 2 illustrates the estimation of , and Figure 3 illustrates the estimation of .
5. Conclusions
In this paper, a fault estimation algorithm is established for linear discrete systems with actuator faults. The algorithm can enhance the performance of fault estimation. And simulation results show that using the algorithm, the accuracy of fault estimation can be improved evidently. Extension of the proposed fault estimation method to more general nonlinear systems is an interesting issue, which will be investigated in our future research work.
Acknowledgments
This work is supported by the National Natural Science Foundation of China (nos. 61273171, 61304112, and 61104020), the Natural Science Foundation of Jiangsu Province (BK20131364), and the Doctoral Fund of Ministry of Education of China (no. 20113218110011).
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Copyright © 2013 Jianwei Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.