Adaptative high-gain extended Kalman filter and applications
Adaptative high-gain extended Kalman filter and applications
Adaptative high-gain extended Kalman filter and applications
You also want an ePaper? Increase the reach of your titles
YUMPU automatically turns print PDFs into web optimized ePapers that Google loves.
tel-00559107, version 1 - 24 Jan 2011<br />
REFERENCES<br />
[78] P. Krishnamurthy <strong>and</strong> F. Khorrami. Dynamic <strong>high</strong>-<strong>gain</strong> scaling: State <strong>and</strong> output<br />
feedback with application to systems with iss appended dynamics driven by all states.<br />
IEEE Transactions on Automatic Control, 49(12):2219–2239, 2004. 25<br />
[79] P. Krishnamurthy, F. Khorrami, <strong>and</strong> R. S. Ch<strong>and</strong>ra. Global <strong>high</strong>-<strong>gain</strong> based observer<br />
<strong>and</strong> backstepping controler for generalized output-feedback canonical form. IEEE<br />
Transactions on Automatic Control, 48(12), 2003. 25<br />
[80] H. J. Kushner. Approximations to optimal nonlinear <strong>filter</strong>s. IEEE Transactions on<br />
Automatic Control AC, 12(5), 1967. 7<br />
[81] G. F. Lawler. Introduction to Stochastic Processes, Second Edition. Chapman <strong>and</strong><br />
Hall/CRC, 2006. 162<br />
[82] D. S. Lemons <strong>and</strong> A. Gythiel. Paul langevin’s 1908 paper “on the theory of brownian<br />
motion”. American Journal of Physics, 65(11), 1997. 162<br />
[83] V. Lippiello, B. Siciliano, <strong>and</strong> L. Villani. Adaptive <strong>extended</strong> kalman <strong>filter</strong>ing for visual<br />
motion estimation of 3d objects. Control Engineering Practices, 15:123–134, 2007. 32<br />
[84] F. L. Liu, M. Farza, M. M’Saad, <strong>and</strong> H. Hammouri. High <strong>gain</strong> observer based on<br />
coupled structures. In Conference on Systems <strong>and</strong> Control, Marrakech, Morocco, 2007.<br />
93<br />
[85] L. Ljung <strong>and</strong> T. Söderström. Theory <strong>and</strong> Practice of Recursive Identification. The MIT<br />
Press, 1983. 83<br />
[86] D. G. Luenberger. Observers for multivariable systems. IEEE Transactions on Automatic<br />
Control, 11:190–197, 1966. 5<br />
[87] P. Martin <strong>and</strong> P. Rouchon. Two remarks on induction motors. In Symposium on<br />
Control, Optimization <strong>and</strong> Supervision, 1996. 57<br />
[88] The Mathworks, www.mathworks.com. Optimization Toolbox for Use with Matlab,<br />
User’s Guide. 83<br />
[89] P. S. Maybeck. Stochastic Models, Estimation, <strong>and</strong> Control, volume 1. Academic Press,<br />
1979. 30<br />
[90] P. S. Maybeck. Stochastic Models, Estimation, <strong>and</strong> Control, volume 2. Academic Press,<br />
1982. 30, 32<br />
[91] O. Mazenc <strong>and</strong> B. Olivier. Interval observers for planar systems with complex poles.<br />
In European Control Conference, 2009. 4<br />
[92] R. K. Mehra. Approaches to adaptive <strong>filter</strong>ing. IEEE Transactions on Automatic<br />
Control, 17(5):693– 698, 1972. 30, 32<br />
[93] S. Mehta <strong>and</strong> J. Chiasson. Nonlinear control of a series dc motor: Theory <strong>and</strong> experiment.<br />
IEEE Transactions on Industrial Electronics, 45(1), 1998. 57, 58<br />
170