I have algorithm I have estimated the data pretty well , but I 'm not able to forecast it .

I have estimated my data through a fucnction that contains Kalmen filter algorthim and it estimated the data well and I have tried to use forecast function and i found those errors

Error: File: forecast.m Line: 23 Column: 33

Character vector is not terminated properly.

my data

function h = GetSonar()

%

%

persistent sonarAlt % SonartAlt.mat

persistent k firstRun

if isempty(firstRun)

load SonarAlt

k = 1;

firstRun = 1;

end

h = sonarAlt(k);

k = k + 1;

the full code is for kalman

function [pos vel Px] = DvKalman(z)

%

%

persistent A H Q R

persistent x P

persistent firstRun

if isempty(firstRun)

firstRun = 1;

dt = 1; % sec, for sonar

A = [ 1 dt ;

0 1 ];

H = [1 0];

Q = [ 1 0 ;

0 3 ];

R = 10;

x = [ 0 20 ]';

P = 5*eye(2);

end

% Kalman filter algorithm

xp = A*x;

Pp = A*P*A' + Q;

K = Pp*H'*inv(H*Pp*H' + R);

x = xp + K*(z - H*xp);

P = Pp - K*H*Pp;

pos = x(1);

vel = x(2);

Px = P ;

clear all

Nsamples = 1500;

Xsaved = zeros(Nsamples, 2);

Zsaved = zeros(Nsamples, 1);

Psaved = zeros(Nsamples, 2);

for k=1:Nsamples

z = GetSonar();

[pos vel P] = DvKalman1(z);

Xsaved(k, :) = [pos vel];

Zsaved(k) = z;

Psaved(k,1:2) = [P(1,1) P(2,2)];

end

dt = 0.02;

t = 0:dt:Nsamples*dt-dt;

fig = figure ;

left_color = [0 0 0]; % black

right_color= [0 0 1]; % blue

set(fig,'defaultAxesColorOrder',[left_color; right_color]);

hold on

yyaxis left

plot(t, Zsaved(:), 'r.','markersize',20)

plot(t, Xsaved(:, 1),'k-','linewidth',4)

ylabel('position (m)')

yyaxis right

plot(t, Xsaved(:, 2),'b-','linewidth',4)

xlabel('time (s)'); ylabel('velocity (m/s)')

title('Position and Velocity from Noisy Sonar Measurements')

legend('Position, Raw Measurements','Position from Kalman Filter',...

'Velocity from Kalman Filter')

set(gca,'fontsize',18); grid on

set(gcf, 'Position', [10, 100, 1200, 500])

%% Extra things to plot

figure

plot(t,Psaved(:,1),'k')

figure

plot(t,Psaved(:,2),'b')

figure

t=0:dt:(Nsamples-1)*dt-dt;

vel_est = diff(Zsaved)/dt;

plot(t,vel_est,'r.','markersize',20)

hold on

vel_est2 = diff(Xsaved(:,1))/dt;

plot(t,vel_est2,'k-','linewidth',2)

plot(t,Xsaved(1:(end-1),2),'b','linewidth',4)

grid

xlabel('time (s)'); ylabel('velocity (m/s)')

legend('Finite Difference of Measurement',...

'Finite Difference of Estimated Position',...

'Kalman Filter Velocity Estimate');

set(gca,'fontsize',18); grid on

and forecast fuction

function YP = forecast(model,data,K, Init)

%FORECAST Forecast a time series K steps into the future

%

% YF = FORECAST(MODEL,DATA,K)

%

% DATA: Existing data up to time N, an IDDATA object.

% MODEL: The model as any IDMODEL object, IDPOLY, IDSS, IDARX or IDGREY.

% K: The time horizon of forecasting, a positive integer with the number of

samples

% YF: The forecasted output after time N, an IDDATA object with output

% only, covering the time span N+1:N+K.

%

% YF = FORECAST(MODEL,DATA,K, INIT)

% wehere INIT is 'z' or 'e' allows specification of initial conditions (at

% time = Data.SamplingInstants(1)).

%

% See also idmodel/predict, which computes a fixed horizon prediction

% along the existing data record.

[N, ny] = size(data); % assume data is iddata

Mss = idss(model);

ord = size(pvget(Mss,'A'),1);

if ord>N

error('Forecast:TooFewSamples','The data should contain at least %d

samples.',ord)

end

if nargin<4, Init = 'e'; end

yp = zeros(K,ny);

mp = getPredictor(Mss);

[Ap,Bp,Cp] = ssdata(mp);

if Init=='z'

xt = ltitr(Ap, Bp, data.y); % use zero init

x0 = xt(end,:);

else % Init == 'e'

[A1,B1,C1,D1,K1] = ssdata(Mss);

x00 =

x0est(data.y,A1,B1,C1,D1,K1,size(C1,1),size(B1,2),250e3,eye(size(C1,1)));

x0 = ltitr(Ap,Bp,data.y,x00); x0 = x0(end,:);

end

u = [data.y(end,:); zeros(1,ny)];

for ct = 1:K

xt = ltitr(Ap, Bp, u, x0);

x0 = xt(end,:);

yp(ct,:) = (Cp*x0.').';

u = [yp(ct,:); zeros(1,ny)];

end

YP = data; YP.y = yp; YP.u = []; YP.Name = '';

YP.UserData = []; YP.Notes = '';

YP.Tstart = data.Ts*(N+1);

%------local function -----------------------------------

function mp = getPredictor(sysd)

[A,B,C,D,K] = ssdata(sysd);

Ts = sysd.Ts;

Ny = size(D,1);

mp = idss(A-K*C, [K B-K*D], C, [zeros(Ny), D], zeros(size(A,1),Ny), 'Ts',

Ts);

mp.InputDelay = [zeros(Ny,1); sysd.InputDelay];

Answer 1

[Rik]

As the error message is telling you, the problem is here;

 error('Forecast:TooFewSamples','The data should contain at least %d 
samples.',ord)

There is a new line, but that results in an improper termination of the char vector. You should use the edit below.

error('Forecast:TooFewSamples','The data should contain at least %d samples.',ord)

[Ahmed Gharieb naga]

thanks to anwer but it did not solve the main problem (how i could I forecast my model ) i want to 200 steps into future

[Ahmed Gharieb naga]

Kf = 200;

yf = forecast(DvKalman(z),Xsaved,Kf);

I got that error

Warning: The "ssdata" command now returns the sample time "Ts" as its fifth output argument rather than the idss model

matrix "K". Use the "idssdata" command to fetch the state-space matrices of an idss model.

> In ctrlMsgUtils.warning (line 25)

In DynamicSystem/ssdata (line 44)

In forecast>getPredictor (line 51)

In forecast (line 28)

Error using -

Arrays have incompatible sizes for this operation.

Error in forecast>getPredictor (line 54)

mp = idss(A-K*C, [K B-K*D], C, [zeros(Ny), D], zeros(size(A,1),Ny), 'Ts',Ts);

Error in forecast (line 28)

mp = getPredictor(Mss);

Related documentation

[Rik]

Why are you not formatting your code as code? This makes your post hard to read, and makes it harder to run in the forum interface.

What have you tried so far to solve this problem? I don't have experience with the functions you're using, so I would have to do the same: start by reading the documentation and google for solutions. What did you find? Why didn't those results solve your problem?

[Ahmed Gharieb naga]

yeah I got yf = zero

i have used idss command to identify the space state system I'm still trying sir thanks