Data-driven nonlinear system actuator fault factor identification technology
A non-linear system, fault factor technology, applied in general control systems, control/regulation systems, testing/monitoring control systems, etc., can solve the problems of poor generality, poor generality, mass production experience and process knowledge of artificial intelligence methods
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[0085] In order to better illustrate the purpose and advantages of the present invention, the content of the invention will be further described in detail in conjunction with the embodiments and corresponding drawings.
[0086] The present invention aims at the problem of online estimation of actuator fault factors of nonlinear systems with dynamic actuator faults. Under the data-driven framework, a state observer is designed based on the Kalman filter to estimate the system state, and based on the filter Using the estimated bias, a data-driven identification technique for actuator failure factors in nonlinear systems is proposed.
[0087] see figure 1 As shown, a data-driven nonlinear system actuator failure factor identification technology disclosed in this embodiment includes the following steps:
[0088] Step S1: Establish a nonlinear system.
[0089] Consider a discrete-time nonlinear system as follows:
[0090] y(k+1)=f(y(k),...,y(k-n y ),u(k),…,u(k-n u )) (1)
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