Abstract
This is the first paper to present such a digital redesign method for the (conventional) OKID system and apply this novel technique for nonlinear system identification. First, the Observer/Kalman filter Identification (OKID) method is used to obtain the lower-order state-space model for a stochastic chaos system. Then, a digital redesign approach with the high-gain property is applied to improve and replace the observer identified by OKID. Therefore, the proposed OKID combined with an observer-based digital redesign novel tracker not only suppresses the uncertainties and the nonlinear perturbations, but also improves more accurate observation parameters of OKID for complex Multi-Input Multi-Output systems. In this research, Chen’s stochastic chaotic system is used as an illustrative example to demonstrate the effectiveness and excellence of the proposed methodology.
Highlights
In the past decade, many system identification techniques have been developed and applied to identify a state-space model for modal parameter identification of large flexible space structures [1].Theoretically, for a nonlinear system with precise differential equations, a higher-order linear model always gives a better approximation than a lower-order one
The Observer/Kalman filter Identification (OKID) technique is an extension of an eigensystem realization algorithm (ERA) that permits efficient identification of large flexible structures [3]
Our research shows a novel low-order observer-based digital redesign tracker for the equivalent chaotic system, and that it is theoretically possible to asymptotically track the stochastic chaos system with zero error
Summary
Many system identification techniques have been developed and applied to identify a state-space model for modal parameter identification of large flexible space structures [1]. Based on the concepts of stochastic estimation and techniques of deterministic Markov parameter identification, OKID directly generates a local linear state-space model for the underlying nonlinear system. Algorithms 2017, 10, 25 minimize the performance index and obtain an optimal observer so that the estimated error compared with the original dynamics signal can be minimized and quickly convergent [8]. This is the first paper to present such a digital redesign method for the (conventional) OKID system. Our research shows a novel low-order observer-based digital redesign tracker for the equivalent chaotic system, and that it is theoretically possible to asymptotically track the stochastic chaos system with zero error
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