Journal of Systems Engineering and Electronics ›› 2012, Vol. 23 ›› Issue (3): 419-424.doi: 10.1109/JSEE.2012.00053

• CONTROL THEORY AND APPLICATION • Previous Articles     Next Articles

Iterative learning based fault detection and estimation in nonlinear systems

Wei Cao1,2,∗, Wang Cong1, and Ming Sun2   

  1. 1. College of Automation, Harbin Engineering University, Harbin 150001, P. R. China;
    2. College of Computer and Control Engineering, Qiqihar University, Qiqihar 161006, P. R. China
  • Online:2012-06-25 Published:2010-01-03


Aiming at a class of nonlinear systems that contains faults, a novel iterative learning scheme is applied to fault detection, and a novel algorithm of fault detection and estimation is proposed. This algorithm first constructs residual signals by the output of the practical system and the output of the designed fault tracking estimator, and then uses the residuals and the differencevalue signal of the adjacent two residuals to gradually revise the introduced virtual faults, which can cause the virtual faults to close to the practical faults in systems, thereby achieving the goal of fault detection for systems. This algorithm not only makes full use of the existing valid information of systems and has a faster tracking convergent speed than the proportional-type (P-type) algorithm, but also calculates more simply than the proportional-derivative-type (PD-type) algorithm and avoids the unstable effects of differential operations in the system. The final simulation results prove the validity of the proposed algorithm.