Decomposition‐based multiinnovation gradient identification algorithms for a special bilinear system based on its input‐output representation
Copyright policy )Decomposition‐based multiinnovation gradient identification algorithms for a special bilinear system based on its input‐output representation
SummaryThis article considers the parameter estimation for a special bilinear system with colored noise. Its input‐output representation is derived by eliminating the state variables in the bilinear system. Based on the input‐output representation of the bilinear system, a multiinnovation generalized extended stochastic gradient (MI‐GESG) algorithm is proposed by using the multiinnovation identification theory. Furthermore, a decomposition‐based multiinnovation (ie, hierarchical multiinnovation) generalized extended stochastic gradient identification (H‐MI‐GESG) algorithm is derived to enhance the parameter estimation accuracy by using the hierarchical identification principle, and a GESG algorithm is presented for comparison. Compared with the existing identification algorithms for the bilinear system, the proposed MI‐GESG and H‐MI‐GESG algorithms can generate more accurate parameter estimation. Finally, a simulation example is provided to verify the effectiveness of the proposed algorithms.
- Qingdao University China (People's Republic of)
- College of Electrical and Mechanical Engineering Pakistan
hierarchical identification, Identification in stochastic control theory, multiinnovation, Nonlinear systems in control theory, bilinear system, stochastic gradient, parameter estimation
hierarchical identification, Identification in stochastic control theory, multiinnovation, Nonlinear systems in control theory, bilinear system, stochastic gradient, parameter estimation
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