Abstract
This paper deals with fault estimation problem for a class of nonlinear system with parameter uncertainties subjecting to Bernoulli‐distributed white sequences with known conditional probabilities. In order to reflect the reality more closely, parameter uncertainties are considered in both the state parameter matrix and the output parameter matrix. Compared with existing observer‐based fault estimation approaches, the proposed iterative learning observer considers the state error information and fault estimating information from the previous iteration to improve the fault estimation performance in the current iteration. Simultaneously, the stability and convergence of the designed observer are achieved by employing the Lyapunov stability theory. On the other hand, a novel optimal function using expectation is presented to ensure the uniform convergence of the fault estimation scheme, thus reducing the impact of randomly occurring parameter uncertainties. Finally, linear matrix inequality (LMI) is employed to obtain the solutions of sufficient condition for further improvement of iterative learning law performance. The results are suitable for the systems with time‐varying uncertainties as well as constant uncertainties. Additionally, a numerical example is given to demonstrate the effectiveness of the proposed design scheme.
Highlights
With the ever-increasing demand on reliability, safety, and maintainability, researches on fault diagnosis [1,2,3,4] and fault-tolerant control [5,6,7] have received more attention in both academic and industrial areas
This paper presents an iterative learning scheme-based fault estimation design for nonlinear systems with randomly occurring parameter uncertainties
(3) The proposed method inherits the advantages of a conventional iterative learning scheme, and linear matrix inequality (LMI) is used to improve the performance of fault estimation due to the accurate system model
Summary
With the ever-increasing demand on reliability, safety, and maintainability, researches on fault diagnosis [1,2,3,4] and fault-tolerant control [5,6,7] have received more attention in both academic and industrial areas. The main purpose of this paper to consider the randomly occurring uncertainties in fault estimation problems for a class of nonlinear systems Motivated by these considerations, this paper presents an iterative learning scheme-based fault estimation design for nonlinear systems with randomly occurring parameter uncertainties. Unlike the conventional iterative learning schemebased fault estimation methods, this technical note designed a novel optimal function using expectation to deal with the randomly occurring parameter uncertainties. (3) The proposed method inherits the advantages of a conventional iterative learning scheme, and LMI is used to improve the performance of fault estimation due to the accurate system model As a result, it can reduce the computing complexity and enormously increase the efficiency and veracity of this method.
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