文件列表:

dc_kalman (0, 2019-02-21)
dc_kalman\kalman_3_way.mdl (113932, 2010-07-16)
dc_kalman\kalman_est.mdl (92923, 2010-07-16)
dc_kalman\license.txt (1563, 2016-09-01)

% Copyright 2010 The MathWorks, Inc. % roger.aarenstrup@mathworks.com % % This is a state-space DC motor model of a % Maxon RE25 10 Watt, precious metal brushes, 118743 % This model also have a weak connection to a load. % % A kalman filter is designed to estimate internal states in % the model. PLEASE OBSERVE THAT THE INTENTION WITH THIS IS % TO SHOW DIFFERENT IMPLEMENTATIONS OF KALMAN FILTER IN SIMULINK. % FOR CONTROL DESIGN AN OBSERVER SHOULD BE USED INSTEAD WITH % REQUIREMENTS TO HAVE A HIGHER BANDWITH THAT THE OVERALL CONTROL % LOOP FOR EXAMPLE. IN % [http://www.mathworks.com/matlabcentral/fileexchange/12137-pid-and-state-feedback-control-of-dc-motors] % THERE IS AN EXAMPLE OF OBSERVER DESIGN. % For a theoretic background about Kalman filters, please see: % % 1. The doc page for the kalman function % 2. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.79.6578&rep=rep1&type=pdf % 3. http://www.negenborn.net/kal_loc/thesis.pdf % 4. http://sciyo.com/download/pdf/pdfs_id/10594 % 5. http://www.cs.unc.edu/~tracker/media/pdf/SIGGRAPH2001_CoursePack_08.pdf % 6. ... %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % The full dc motor model % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Vin is the input voltage to the motor % i is the motor current % th_m is the rotor angle, theta % dth_m is the rotor angular velocity sometimes called omega % th_l is the load angle % dth_l is the load angular velocity % Controller Sample Time Ts = 100e-6; % PARAMETERS DC MOTOR Rm = 2.06; % Motor resistance (ohm) Lm = 0.000238; % motor inductance (Henrys) Kb = 1/((406*2*pi)/60); % Back EMF constant (Volt-sec/Rad) Kt = 0.0235; % Torque constand (Nm/A) Jm = 1.07e-6; % Rotor inertia (Kg m^2) bm = 12e-7; % MEchanical damping (linear model of % friction: bm * dth) % PARAMETERS LOAD Jl = 10.07e-6; % Load inertia (10 times the rotor) bl = 12e-6; % Load damping (friction) Ks = 100; % Spring constant for connection rotor/load b = 0.0001; % Spring damping for connection rotor/load %% SYSTEM MATRICES % % States: [i dth_m th_m dth_l th_l]' % Input: Vin the motor voltage % Outputs: same as states % Afull = [-Rm/Lm -Kb/Lm 0 0 0; Kt/Jm -(bm+b)/Jm -Ks/Jm b/Jm Ks/Jm; 0 1 0 0 0; 0 b/Jl Ks/Jl -(b+bl)/Jl -Ks/Jl; 0 0 0 1 0]; Bfull = [1/Lm 0 0 0 0]'; Cfull = [1 0 0 0 0 0 1 0 0 0; 0 0 1 0 0; 0 0 0 1 0; 0 0 0 0 1]; Dfull = [0 0 0 0 0]'; Cfull2 = [0 0 1 0 0]; Dfull2 = 0'; sys_full = ss(Afull, Bfull, Cfull2, Dfull2); %% Discrete design % First sample the continuous system dsys = c2d(sys_full, Ts, 'zoh'); %% % Manual implementatino of the kalman filter in the simulink models % use the following A = dsys.a; B = dsys.b; C = dsys.c; Q = 1.001; % Process noise variance R = 2.003; % Sensor noise variance %% Kalman filter design in control system toolbox % This example is very similar to the example in the documentation % for the kalman function. Plant = ss(A,[B B], C, 0, Ts,'inputname',{'u' 'w'},'outputname','y'); Q = 1.001; % Process noise variance R = 2.003; % Sensor noise variance [kalmf,L,Pp,M,Z] = kalman(Plant, Q, R, 'delayed');

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