2015 Articles
Observers for Bilinear State-Space Models by Interaction Matrices
This paper formulates a bilinear observer for a bilinear state-space model. Relationship between the bilinear observer gains and the interaction matrices are established and used in the design of such observer gains from input-output data. In the absence of noise, the question of whether a deadbeat bilinear observer exists that would cause the state estimation error to converge to zero identically in a finite number of time steps is addressed. In the presence of noise, an optimal bilinear observer that minimizes the state estimation error in the same manner that a Kalman filter does for a linear system is presented. Numerical results illustrate both the theoretical and computational aspects of the proposed algorithms.
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More About This Work
- Academic Units
- Mechanical Engineering
- Published Here
- August 21, 2015
Notes
This work was presented at the 16th Yale Workshop on Adaptive and Learning Systems, New Haven, CT, 2013. Citation: Minh Q. Phan, Francesco Vicario, Richard W. Longman, and Raimondo Betti, "Observers for Bilinear State-Space Models by Interaction Matrices," 16th Yale Workshop on Adaptive and Learning Systems, New Haven, CT, 2013. This work was later expanded and published as a journal paper. Citation: Minh Q. Phan, Francesco Vicario, Richard W. Longman, and Raimondo Betti, "Optimal bilinear observers for bilinear state-space models by interaction matrices," International Journal of Control, vol. 88, no. 8, 2015, pp. 1504-1522. DOI:10.1080/00207179.2015.1007530 http://www.tandfonline.com/doi/abs/10.1080/00207179.2015.1007530