Call for Paper 25 May, 2024. Please submit your manuscript via online system or email at

ISSN E 2409-2770
ISSN P 2521-2419

By the Design and Implementation of Modified Kalman Filter for LPV Systems

Vol. 3, Issue 4, PP. 26-31, April 2016


Keywords: Kalman Filter, Linear Parameter Varying, Linear Quardratic Regulator

Download PDF

Linear Parameter Varying (LPV) system is an important class of system, as it covers many physical systems. In this paper, the routine Kalman filtering scheme derivations are entertained to modify for generalized LPV systems. The original system is unstable, for controlling purpose a state-feedback controller is employed. For simulation purpose, a real time case study of Boeing-747 model is adopted. The results comprehend attractive features for modified Kalman filtering scheme.

  1. Muhammad Kamran Shereen,, Electrical Engineering Department University of Engineering and Technology Peshawar, Pakistan.
  2. Muhammad Iftikhar Khan, , Assistant Professor in Electrical Engineering Department University of Engineering and Technology Peshawar, Pakistan.
  3. Naeem Khan, , Assistant Professor in Electrical Engineering Department  University of Engineering and Technology Peshawar, Pakistan.
  4. Wasi Ullah, , Postgraduate Student in Electrical Engineering Department University of Engineering and Technology Peshawar, Pakistan.

Muhammad Kamran Shereen Muhammad Iftikhar Khan Naeem Khan Wasi Ullah

[1]     Halim Alwi, Christopher Edwards, and Andres Marcos.Fault reconstruction using a LPV sliding mode   observer for a class of LPV systems. Journal of the Franklin Institute,  349(1):510–530, June 2012.

[2]     S. Armeni, A. Casavola and E. Mosca. Robust fault detection and isolation for LPV systems under a sensitivity constraint. International Journal of Adaptive Control and Signal Processing, 23(7):55–72, 2009.

[3]     G. I. Bara, J. D. Famularo and G. F. Kratz. State estimation For vaffine LPV system. Centre de Recherche en Automatique  de Nancy UPRES A 7039 ENSEM2.

[4]     A. Casavola, D. Famularo, G. Franze and M. Sorbara. A fault-detection,filterdesign method for linear parameter- varying systems. Proceedings of the Institution of Mechanical Engineers. Part I: Journal of Systems and  Control Engineering, 221(6)(11):865–874, 2007.


[5]     S. H. Cha, M. Rotkowitz and B. D. O. Anderson. Gain scheduling using Time-Varying Kalman Filter for a class of LPV System. Proceeding of the 17th World Congress The International Federation of Automatic Control Seoul,  Korea, 2008.

[6]     Denis Efimov, Tarek Rassi, Wilfrid Perruquetti and Ali Zolghadri. Estimation and Control of Discrete-Time LPV Systems Using Interval Observers. IEEE Transactions on Automatic Control, (24):5036–5041, December 2013.

[7]     Joseph F., Kasper Jr. and Arthur Gelb. Applied Optimal estimation. 65. The M.I.T.Press, TASC, 45(16), 1974.

[8]     P. Gahinet, A. Nemirovski, A. Laub and M. Chilali. LMI Control Toolbox User Guide, MathWorks,Inc. (3), 1995

[9]     S. Grenaille, D. Henry and A. Zolghadri. A method for designing fault diagnosis filters for LPV polytopic systems.Journal of Control Science and Engineering, 2008.

[10]  D. Henry, A. Falcoz and A. Zolghadri. Structured H1=HLPV filter for fault diagnosis. Some new results Proceedings of the IFAC Symposium SAFE PROCESS, 2009.

[11]  Naeem Khan, Sajjad Fekriand  and Dawei Gu. A Sub-Optimal Kalman Filtering for Discrete-Time LTI Systems with Loss of Data. In the 7th IFAC Conference onIntelligent Control Automation and Robotics, 2010.

[12]  A. Marcos and G. J. Balas. Development of linear-parameter-varying models for aircraft. AIAA Journal of Guidance, Control and Dynamics, 2(2):218–228, march 2004.

[13]  G. L. Plett. Extended Kalman Filtering for Battery Management Systems of LiPB-Based HEV Battery Packs. Part 1. Journal of Power Sources, 134(22): 252–261, 2004a.

[14]  G. L. Plett. Extended Kalman Filtering for BatteryManagement Systems of LiPB-Based HEV Battery Packs. Part 2. Journal of Power Sources, 134(23): 262– 276, 2004b

[15]  P. L. Santos, J. A. Ramos and J. L. Martinsde, Identification of Linear Parameter Varying Systems Using an iterative Determination Stochastic Subspace Approach European Control Conference, 1(4):2-7, July 2007.

[16]  M. Sato.  Filter design for LPV systems using quardratically parameter dependent Lyapunov function. Automatica, 42(11)(12):2017-2023, 2006

[17]  S. Shamma and M. Athans. Gain scheduling possible hazards and potential remedies. IEEE Control System Magazine, (21):101-107, June 1992

[18]  D. Simon. Optimal State Estimation Kalman, H1 and Nonlinear  Approaches. John Wiley & Sons, Inc., 2006.

[19]  I. Szaszi, A. Marcos, G. J. Balas  and J. Bokor. Linear Parameter Varying detection filter design for a Boeing 747-100/200 aircraft . Journal of Guidance Control and Dynamics, 28(3)(8):461-470, 2005.

[20]  G. Welch and G. Bishop. An Introduction to the Kalman filter. ACM, Inc, SIGGRAPH, 2001.

[21]  G. Woloodkin, G. J. Balas and  W. L. Garrard. Application of Parameter Dependent Robust Control Synthesis to Turbofan  Engines. Journal of Guidance,Control and Dynaomics, 22(25); 262-276, November 1999.

Wanli Liu, Zhengfu Bian, Zhengfu Liu and Qiuzhao Zhang. Evaluation of a cubature Kalman Filtering-Based Phase University method for Differential Interferograms with High Noise in Coal Mining Area, 6 July 2015, 16336- 16357.