文件列表:

kalmanfuzzy\Controller.m (621, 2000-12-20)
kalmanfuzzy\Kalman.m (997, 2003-09-27)
kalmanfuzzy\KalmanTS.m (1345, 2006-04-30)
kalmanfuzzy\Member.m (214, 2000-12-20)
kalmanfuzzy\ModelLinear.m (1367, 2000-12-20)
kalmanfuzzy\ModelNonlinear.m (1457, 2000-12-20)
kalmanfuzzy\TruckTrailer.m (6435, 2006-06-15)
kalmanfuzzy\ARESolutions.m (958, 2005-09-30)
kalmanfuzzy\FuzzyModel.asv (1276, 2005-09-30)
kalmanfuzzy\Thumbs.db (6656, 2005-12-13)
kalmanfuzzy\FuzzyModel.m.bak (1145, 2006-04-28)
kalmanfuzzy\FuzzyModel.m (1076, 2006-06-15)
kalmanfuzzy\TruckTrailer.asv (5824, 2006-06-15)
kalmanfuzzy\xhat11.txt (968, 2008-04-10)
kalmanfuzzy\xhat12.txt (968, 2008-04-10)
kalmanfuzzy\xhat21.txt (968, 2008-04-10)
kalmanfuzzy\xhat22.txt (968, 2008-04-10)
kalmanfuzzy\xhat31.txt (968, 2008-04-10)
kalmanfuzzy\xhat32.txt (968, 2008-04-10)
kalmanfuzzy (0, 2006-04-26)

KALMAN FILTERING FOR FUZZY DYNAMIC SYSTEMS December 21, 2000 Dan Simon 332 Stilwell Hall Department of Electrical Engineering Cleveland State University 1960 East 24th Street Cleveland, OH 44115 web: http://csaxp.csuohio.edu/~simon/ email: simon@csvax.csuohio.edu A Kalman filter can be used to estimate the states of a nonlinear dynamic system that is approximated with a Takagi-Sugeno (T-S) model. A backing up truck-trailer example is used to illustrate the effectiveness of the proposed state estimator. This file describes the m-files that were downloaded along with this readme file. The m-files can be run in the MATLAB environment. M-files are written in a very high-level language that can be easily read, almost like pseudo code. The files included with this download are as follows. ARESolutions.m - This gives the algebraic Ricatti solutions for the optimal controllers and the Kalman filters for each local T-S model. Controller.m - This computes the global optimal control for the T-S model, which approximates the nonlinear truck-trailer system. FuzzyModel.m - This computes the T-S model system matrices and the current membership function values. Kalman.m - This computes the optimal state estimate of the linear time-varying T-S model. KalmanTS.m - This runs a monte-carlo simulation of the steady state Kalman filter for the T-S model. Member.m - This plots the membership grade functions for the T-S model. ModelLinear.m - This runs one time step of the linear time-varying T-S model. ModelNonlinear.m - This runs one time step of the nonlinear model. TruckTrailer.m - This simulates the truck-trailer system, the optimal control, and the state estimate. Look in the m-files themselves for more information. In order to run these m-files, run MATLAB and make sure that the location of the files on your hard drive is part of your MATLAB path. (For example, if you downloaded the files to the c:\kalmanfuzzy directory on your hard drive, type "path(path, 'c:\kalmanfuzzy');" at MATLAB's command prompt.) After downloading these files you can set (for example) sigx=[.05 .05 .25] and sigy=[.2 .2 1]. This sets the standard deviation of the process noise (sigx) and the measurement noise (sigy). Then type "TruckTrailer(sigx,sigy,0,1)" at MATLAB's command prompt. (The 0 means simulate the steady state Kalman filter, and the 1 means plot the results.) If you instead type "TruckTrailer(sigx,sigy,1,1)" then you will be simulating the time-varying Kalman filter, which theoretically performs better but requires more computational effort. You can type "KalmanTS(sigX,sigY,0)" to see monte-carlo performance results for the steady state Kalman filter. Type "KalmanTS(sigX,sigY,1)" to see monte-carlo performance results for the time-varying optimal Kalman filter. Feel free to contact me at simon@csvax.csuohio.edu with any comments or questions.

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