Matlab ode solvers: changing state and specified time - matlab

I am solving a set of ODEs (dy/dt) at t=0, all initial conditions t=0 y_0=(0,0,0). Can I add some number to the y values at different times (e.g., at t=10, y1 should be added to that number; at t=20, y2 should be added to that number, etc.) and solve the equations?

Inserting large discontinuities in your ODE in the way you suggest (and the way illustrated by #macduff) can lead to less precision and longer computation times (especially with ode45 - ode15s might be a better option or at least make sure that your absolute and relative tolerances are suitable). You've effectively produced a very stiff system. If you want to add some number to the ODEs starting at a specific time keep in mind that the solver only evaluates these equations at specific points in time of its own choosing. (Don't be mislead by the fact that you can obtain fixed step-size outputs by specifying tspan as more than two elements – all of Matlab's solvers are variable step-size solvers and choose their true steps based on error criteria.)
A better option is to integrate the system piecewise and append the resultant outputs from each run together:
% t = 0 to t = 10, pass parameter a = 0 to add to ODEs
a = 0;
tspan = [0 10];
[T,Y] = ode45(#(t,y)myfun(t,y,a),tspan,y_0);
% t = 10 to t = 20, pass parameter a = 10 to add to ODEs
a = 10;
[t,y] = ode45(#(t,y)myfun(t,y,a),tspan+T(end),Y(end,:));
T = [T;t(2:end)];
Y = [Y;y(2:end,:)];
% t = 20 to t = 30, pass parameter a = 20 to add to ODEs
a = 20;
[t,y] = ode45(#(t,y)myfun(t,y,a),tspan+T(end),Y(end,:));
T = [T;t(2:end)];
Y = [Y;y(2:end,:)];
The Matlab editor may complain about the array T and Y not being preallocated and/or growing, but it's fine in this case as they're growing in large chunks only a few times. Alternatively, if you want fixed output step-sizes, you can do this:
dt = 0.01;
T = 0:dt:30;
Y = zeros(length(T),length(y_0));
% t = 0 to t = 10, pass parameter a = 0 to add to ODEs
a = 0;
[~,Y(1:10/dt+1,:)] = ode45(#(t,y)myfun(t,y,a),T(1:10/dt+1),y_0);
% t = 10 to t = 20, pass parameter a = 10 to add to ODEs
a = 10;
[~,Y(10/dt+1:20/dt+1,:)] = ode45(#(t,y)myfun(t,y,a),T(10/dt+1:20/dt+1),Y(10/dt+1,:));
% t = 20 to t = 30, pass parameter a = 20 to add to ODEs
a = 20;
[~,Y(20/dt+1:end,:)] = ode45(#(t,y)myfun(t,y,a),T(20/dt+1:end),Y(20/dt+1,:));
One could easily convert both of the above blocks of code to more compact for loops if desired.
In both cases your ODE function myfun incorporates the parameter a this way:
function ydot = myfun(t,y,a)
y(1) = ... % add a however you like
...

Ok, like Simon McKenzie says we really need more info on your urgent issue, but I think I can help. From what you've given us, I'll assume you have a function myfun that you pass to something like ode45
y_0 = [0,0,0];
% here Tfinal is the time in seconds that you want to simulate to
% and specify the tspan so that you will get a solution at each whole
% number of seconds, I believe
[t,y] = ode45(#myfun,[0:0.1:Tfinal],y_0);
Somewhere you have defined your function, here I'll call it myfun
function dy = myfun(t,y)
% here let's check to see if it's time to add your offsets
% still, you may want to put a little fudge factor for the time, but
% you'll have to experiment, I'll set it up though
EPS = 1e-12;
if( t < 10 + EPS || t > 10 - EPS )
y(1) = y(1) + 10;
.
.
.
.
% this only makes sense if you then use y(1) in the compuatation
Otherwise, just add the offset to the returned solution vector, i.e.
idx10 = find( t == 10 ); % should be there since it's in the tspan
y(idx10:end) = y(idx10:end) + 10; % I guess you add it from that point on?

Related

Why do these linear inequality constraints work in Matlab but not Octave?

I have the following script performing a nonlinear optimization (NLP), which works in Matlab and hits MaxFunctionEvaluations after about 5 minutes on my machine:
% Generate sample consumption data (4 weeks)
x = 0:pi/8:21*pi-1e-1; %figure; plot(x, 120+5*sin(0.2*x).*exp(-2e-2*x) + 10*exp(-x))
y = 120 + 5*sin(0.2*x).*exp(-2e-2*x) + 10*exp(-x);
consumptionPerWeek = (y + [0; 11; -30; 4.5]).'; % in 168x4 format
consumptionPerHour = reshape(consumptionPerWeek, [], 1);
hoursPerWeek = 168;
hoursTotal = numel(consumptionPerHour);
daysTotal = hoursTotal/24;
weeksTotal = ceil(daysTotal/7);
%% Perform some simple calculations
q_M_mean = mean(consumptionPerHour);
dvsScalingPerWeek = mean(consumptionPerWeek)/q_M_mean;
%% Assumptions about reactor, hard-coded
V_liq = 5701.0; % m^3, main reactor; from other script
initialValue = 4.9298; % kg/m^3; from other script
substrates_FM_year = [676.5362; 451.0241];
total_DVS_year = [179.9586; 20.8867];
mean_DVS_conc = 178.1238; %kg/m^3
% Product yields (m^3 per ton DVS)
Y_M = 420;
Y_N = 389;
%% Test DVS model
DVS_hour = sum(total_DVS_year)/hoursTotal; % t/h
k_1 = 0.25; % 1/d
parameters = [k_1; Y_M; Y_N; V_liq];
%% Build reference and initial values for optimization
% Distribute feed according to demand (-24%/+26% around mean)
feedInitialMatrix = DVS_hour*ones(hoursPerWeek, 1)*dvsScalingPerWeek;
% Calculate states with reference feed (improved initials)
feedInitialVector = reshape(feedInitialMatrix, [], 1);
feedInitialVector = feedInitialVector(1:hoursTotal);
resultsRef = reactorModel1(feedInitialVector, initialValue, parameters, ...
mean_DVS_conc);
V_M_PS = 0 + cumsum(resultsRef(:,2)/24 - consumptionPerHour);
neededMStorage0 = max(V_M_PS) - min(V_M_PS);
%% Setup optimization problem (NLP): feed optimization with virtual product storage
% Objective function 1: Standard deviation of theoretical product storage volume
objFun1 = #(feedVector) objFunScalar(feedVector, initialValue, parameters, ...
mean_DVS_conc, consumptionPerHour);
% Bounds (lb <= x <= ub), i.e., decision variables can only range between 0 and 0.9*dailyDvsAmount
upperfeedLimitSlot = 0.90; % Limit DVS feed amount per *slot*
upperfeedLimitDay = 1.80; % Limit DVS feed amount per *day*
upperfeedLimitWeek = 1.37; % Limit DVS feed amount per *week*
lowerBound_nlp = zeros(1, hoursTotal);
upperBound_nlp = upperfeedLimitSlot*24*DVS_hour.*ones(1, hoursTotal);
% Equality Constraint 1: feed amount mean = constant
A_eq1_nlp = ones(1, hoursTotal);
b_eq1_nlp = DVS_hour*hoursTotal;
% Inequality Constraint 1: Limit max. daily amount
A_nlp1 = zeros(daysTotal, hoursTotal);
for dI = 1:daysTotal
A_nlp1(dI, (24*dI)-(24-1):(24*dI)) = 1;
end
b_nlp1 = upperfeedLimitDay*24*DVS_hour*ones(daysTotal, 1);
% Inequality Constraint 2: Limit max. weekly amount
A_nlp2 = zeros(weeksTotal, hoursTotal);
for wIi = 1:weeksTotal
A_nlp2(wIi, (168*wIi)-(168-1):(168*wIi)) = 1;
end
b_nlp2 = upperfeedLimitWeek*168*DVS_hour*ones(weeksTotal, 1);
% Summarize all inequality constraints
A_nlp = [A_nlp1; A_nlp2]; %sparse([A_nlp1; A_nlp2]);
b_nlp = [b_nlp1; b_nlp2]; %sparse([b_nlp1; b_nlp2]);
try
% Solver: fmincon (Matlab Optimization Toolbox) --> SQP-algorithm = best
optionen_GB = optimoptions('fmincon', 'Display', 'iter', 'FunctionTolerance', 1e-5, ...
'StepTolerance', 1e-4, 'MaxIterations', 2*hoursTotal, ...
'MaxFunctionEvaluations', 100*hoursTotal, 'HonorBounds', true, 'Algorithm', 'sqp');
catch
optionen_GB = optimset('Display', 'iter', 'TolFun', 1e-5, 'TolX', 1e-4, ...
'MaxIter', 2*hoursTotal, 'MaxFunEvals', 100*hoursTotal, 'Algorithm', 'sqp');
end
%% Solve gradient-based NLP
tic; [feedOpt, fval] = fmincon(#(feedVector) objFun1(feedVector), ...
feedInitialVector, A_nlp, b_nlp, A_eq1_nlp, b_eq1_nlp, lowerBound_nlp, upperBound_nlp, ...
[], optionen_GB); toc
%% Rerun model and calculate virtual storage volume with optimized input
resultsOpt = reactorModel1(feedOpt, initialValue, parameters, mean_DVS_conc);
q_M_Opt = resultsOpt(:,2)/24;
V_M_PS_opt = 0 + cumsum(q_M_Opt - consumptionPerHour);
neededMStorageOpt = max(V_M_PS_opt) - min(V_M_PS_opt);
sprintf('Needed product storage before optimization: %.2f m^3, \nafterwards: %.2f m^3. Reduction = %.1f %%', ...
neededMStorage0, neededMStorageOpt, (1 - neededMStorageOpt/neededMStorage0)*100)
%% Objective as separate function
function prodStorageStd = objFunScalar(dvs_feed, initialValues, parameters, mean_DVS_conc, ...
MConsumptionPerHour)
resultsAlgb = reactorModel1(dvs_feed(:, 1), initialValues, parameters, mean_DVS_conc);
q_M_prod = resultsAlgb(:,2)/24;
V_M_PS1 = 0 + cumsum(q_M_prod - MConsumptionPerHour);
prodStorageStd = std(V_M_PS1);
end
The external function reads like this:
function resultsArray = reactorModel1(D_feed, initialValue, parameters, D_in)
% Simulate production per hour with algebraic reactor model
% Feed is solved via a for-loop
hoursTotal = length(D_feed);
k_1 = parameters(1);
Y_M = parameters(2);
Y_N = parameters(3);
V_liq = parameters(4);
resultsArray = zeros(hoursTotal, 3);
t = 1/24;
liquid_feed = D_feed/(D_in*1e-3); % m^3/h
initialValue4Model0 = (initialValue*(V_liq - liquid_feed(1))*1e-3 ...
+ D_feed(1))*1e3/V_liq; % kg/m^3
resultsArray(1, 1) = initialValue4Model0*exp(-k_1*t);
% Simple for-loop with feed as vector per hour
for pHour = 2:hoursTotal
initialValue4Model = (resultsArray(pHour-1, 1)*(V_liq - liquid_feed(pHour))*1e-3 ...
+ D_feed(pHour))*1e3/V_liq; % kg/m^3
resultsArray(pHour, 1) = initialValue4Model*exp(-k_1*t);
end
resultsArray(:, 2) = V_liq*Y_M*k_1*resultsArray(:, 1)*1e-3; % m^3/d
resultsArray(:, 3) = V_liq*Y_N*k_1*resultsArray(:, 1)*1e-3; % m^3/d
end
When I execute the very same script in Octave (ver 5.1.0 with optim 1.6.0), I get:
error: linear inequality constraints: wrong dimensions
When in fact, the following line (executed from the command prompt)
sum(A_nlp*feedInitialVector <= b_nlp)
gives 32 on both Octave and Matlab, thus showing that dimensions are correct.
Is this a bug? Or is Octave treating linear (in)equality constraints somehow different than Matlab?
(Also, if you have tips on how to speed up this script, they would come in handy.)
I've debugged this a bit for you to get you started.
First enable debugging on error:
debug_on_error(1)
Then find the installation folder of optim, and have a look at file /private/__linear_constraint_dimensions__.m within.
*(I found this by doing a grep operation for the exact error you were getting, and found the relevant file. There is another one outside the private folder, you may want to look at that too.)
If you look at the lines trigerring the errors, you will notice, e.g. that an error is triggered if rm != o.np, where [rm, cm] = size(f.imc)
Now run your script and let it enter debug mode on error. You will see that:
debug> [rm, cm] = size(f.imc)
rm = 32
cm = 672
debug> o.np
ans = 672
debug> rm != o.np
ans = 1 % I.e. boolean test succeeds and triggers error
I have no idea what these are, presumably r and c reflect rows and columns, but in any case, you will see that it appears you are trying to match rows with columns and vice versa.
In other words, it looks like you may have passed your inputs in a transposed fashion at some point.
In any case, if this isn't exactly what's happening, this should be a decent starting point for you to figure the exact bug out.
I don't know why matlab "works". Maybe there's a bug in your code and matlab works despite it (for better or worse).
Or there might be a bug in optim transposing inputs by accident (or, at least, in a manner that is incompatible to matlab).
If you feel after your debugging adventures that it's a bug in the optim package, feel free to file a bug report :)

Input equations into Matlab for Simulink Function

I am currently working on an assignment where I need to create two different controllers in Matlab/Simulink for a robotic exoskeleton leg. The idea behind this is to compare both of them and see which controller is better at assisting a human wearing it. I am having a lot of trouble putting specific equations into a Matlab function block to then run in Simulink to get results for an AFO (adaptive frequency oscillator). The link has the equations I'm trying to put in and the following is the code I have so far:
function [pos_AFO, vel_AFO, acc_AFO, offset, omega, phi, ampl, phi1] = LHip(theta, eps, nu, dt, AFO_on)
t = 0;
% syms j
% M = 6;
% j = sym('j', [1 M]);
if t == 0
omega = 3*pi/2;
theta = 0;
phi = pi/2;
ampl = 0;
else
omega = omega*(t-1) + dt*(eps*offset*cos(phi1));
theta = theta*(t-1) + dt*(nu*offset);
phi = phi*(t-1) + dt*(omega + eps*offset*cos(phi*core(t-1)));
phi1 = phi*(t-1) + dt*(omega + eps*offset*cos(phi*core(t-1)));
ampl = ampl*(t-1) + dt*(nu*offset*sin(phi));
offset = theta - theta*(t-1) - sym(ampl*sin(phi), [1 M]);
end
pos_AFO = (theta*(t-1) + symsum(ampl*(t-1)*sin(phi* (t-1))))*AFO_on; %symsum needs input argument for index M and range
vel_AFO = diff(pos_AFO)*AFO_on;
acc_AFO = diff(vel_AFO)*AFO_on;
end
https://www.pastepic.xyz/image/pg4mP
Essentially, I don't know how to do the subscripts, sigma, or the (t+1) function. Any help is appreciated as this is due next week
You are looking to find the result of an adaptive process therefore your algorithm needs to consider time as it progresses. There is no (t-1) operator as such. It is just a mathematical notation telling you that you need to reuse an old value to calculate a new value.
omega_old=0;
theta_old=0;
% initialize the rest of your variables
for [t=1:N]
omega[t] = omega_old + % here is the rest of your omega calculation
theta[t] = theta_old + % ...
% more code .....
% remember your old values for next iteration
omega_old = omega[t];
theta_old = theta[t];
end
I think you forgot to apply the modulo operation to phi judging by the original formula you linked. As a general rule, design your code in small pieces, make sure the output of each piece makes sense and then combine all pieces and make sure the overall result is correct.

Solving differential equation for a single time in loop with matlab

I have a Mechanical system with following equation:
xdot = Ax+ Bu
I want to solve this equation in a loop because in every step I need to update u but solvers like ode45 or lsim solving the differential equation for a time interval.
for i = 1:10001
if x(i,:)>= Sin1 & x(i,:)<=Sout2
U(i,:) = Ueq - (K*(S/Alpha))
else
U(i,:) = Ueq - (K*S)
end
% [y(i,:),t,x(i+1,:)]=lsim(sys,U(i,:),(time=i/1000),x(i,:));
or %[t,x] = ode45(#(t,x)furuta(t,x,A,B,U),(time=i/1000),x)
end
Do I have another ways to solve this equation in a loop for a single time(Not single time step).
There are a number of methods for updating and storing data across function calls.
For the ODE suite, I've come to like what is called "closures" for doing that.
A closure is basically a nested function accessing or modifying a variable from its parent function.
The code below makes use of this feature by wrapping the right-hand side function passed to ode45 and the 'OutputFcn' in a parent function called odeClosure().
You'll notice that I am using logical-indexing instead of an if-statement.
Vectors in if-statements will only be true if all elements are true and vice-versa for false.
Therefore, I create a logical array and use it to make the denominator either 1 or Alpha depending on the signal value for each row of x/U.
The 'OutputFcn' storeU() is called after a successful time step by ode45.
The function grows the U storage array and updates it appropriately.
The array U will have the same number of columns as the number of solution points requested by tspan (12 in this made-up example).
If a successful full step leaps over any requested points, the function is called with intermediate all requested times and their associated solution values (so x may be rectangular and not just a vector); this is why I used bsxfun in storeU and not in rhs.
Example function:
function [sol,U] = odeClosure()
% Initilize
% N = 10 ;
A = [ 0,0,1.0000,0; 0,0,0,1.0000;0,1.3975,-3.7330,-0.0010;0,21.0605,-6.4748,-0.0149];
B = [0;0;0.6199;1.0752 ] ;
x0 = [11;11;0;0];
K = 100;
S = [-0.2930;4.5262;-0.5085;1.2232];
Alpha = 0.2 ;
Ueq = [0;-25.0509;6.3149;-4.5085];
U = Ueq;
Sin1 = [-0.0172;-4.0974;-0.0517;-0.2993];
Sout2 = [0.0172 ; 4.0974; 0.0517; 0.2993];
% Solve
options = odeset('OutputFcn', #(t,x,flag) storeU(t,x,flag));
sol = ode45(#(t,x) rhs(t,x),[0,0.01:0.01:0.10,5],x0,options);
function xdot = rhs(~,x)
between = (x >= Sin1) & (x <= Sout2);
uwork = Ueq - K*S./(1 + (Alpha-1).*between);
xdot = A*x + B.*uwork;
end
function status = storeU(t,x,flag)
if isempty(flag)
% grow array
nAdd = length(t) ;
iCol = size(U,2) + (1:nAdd);
U(:,iCol) = 0 ;
% update U
between = bsxfun(#ge,x,Sin1) & bsxfun(#le,x,Sout2);
U(:,iCol) = Ueq(:,ones(1,nAdd)) - K*S./(1 + (Alpha-1).*between);
end
status = 0;
end
end

separate 'entangled' vectors in Matlab

I have a set of three vectors (stored into a 3xN matrix) which are 'entangled' (e.g. some value in the second row should be in the third row and vice versa). This 'entanglement' is based on looking at the figure in which alpha2 is plotted. To separate the vector I use a difference based approach where I calculate the difference of one value with respect the three next values (e.g. comparing (1,i) with (:,i+1)). Then I take the minimum and store that. The method works to separate two of the three vectors, but not for the last.
I was wondering if you guys can share your ideas with me how to solve this problem (if possible). I have added my coded below.
Thanks in advance!
Problem in figures:
clear all; close all; clc;
%%
alpha2 = [-23.32 -23.05 -22.24 -20.91 -19.06 -16.70 -13.83 -10.49 -6.70;
-0.46 -0.33 0.19 2.38 5.44 9.36 14.15 19.80 26.32;
-1.58 -1.13 0.06 0.70 1.61 2.78 4.23 5.99 8.09];
%%% Original
figure()
hold on
plot(alpha2(1,:))
plot(alpha2(2,:))
plot(alpha2(3,:))
%%% Store start values
store1(1,1) = alpha2(1,1);
store2(1,1) = alpha2(2,1);
store3(1,1) = alpha2(3,1);
for i=1:size(alpha2,2)-1
for j=1:size(alpha2,1)
Alpha1(j,i) = abs(store1(1,i)-alpha2(j,i+1));
Alpha2(j,i) = abs(store2(1,i)-alpha2(j,i+1));
Alpha3(j,i) = abs(store3(1,i)-alpha2(j,i+1));
[~, I] = min(Alpha1(:,i));
store1(1,i+1) = alpha2(I,i+1);
[~, I] = min(Alpha2(:,i));
store2(1,i+1) = alpha2(I,i+1);
[~, I] = min(Alpha3(:,i));
store3(1,i+1) = alpha2(I,i+1);
end
end
%%% Plot to see if separation worked
figure()
hold on
plot(store1)
plot(store2)
plot(store3)
Solution using extrapolation via polyfit:
The idea is pretty simple: Iterate over all positions i and use polyfit to fit polynomials of degree d to the d+1 values from F(:,i-(d+1)) up to F(:,i). Use those polynomials to extrapolate the function values F(:,i+1). Then compute the permutation of the real values F(:,i+1) that fits those extrapolations best. This should work quite well, if there are only a few functions involved. There is certainly some room for improvement, but for your simple setting it should suffice.
function F = untangle(F, maxExtrapolationDegree)
%// UNTANGLE(F) untangles the functions F(i,:) via extrapolation.
if nargin<2
maxExtrapolationDegree = 4;
end
extrapolate = #(f) polyval(polyfit(1:length(f),f,length(f)-1),length(f)+1);
extrapolateAll = #(F) cellfun(extrapolate, num2cell(F,2));
fitCriterion = #(X,Y) norm(X(:)-Y(:),1);
nFuncs = size(F,1);
nPoints = size(F,2);
swaps = perms(1:nFuncs);
errorOfFit = zeros(1,size(swaps,1));
for i = 1:nPoints-1
nextValues = extrapolateAll(F(:,max(1,i-(maxExtrapolationDegree+1)):i));
for j = 1:size(swaps,1)
errorOfFit(j) = fitCriterion(nextValues, F(swaps(j,:),i+1));
end
[~,j_bestSwap] = min(errorOfFit);
F(:,i+1) = F(swaps(j_bestSwap,:),i+1);
end
Initial solution: (not that pretty - Skip this part)
This is a similar solution that tries to minimize the sum of the derivatives up to some degree of the vector valued function F = #(j) alpha2(:,j). It does so by stepping through the positions i and checks all possible permutations of the coordinates of i to get a minimal seminorm of the function F(1:i).
(I'm actually wondering right now if there is any canonical mathematical way to define the seminorm so we get our expected results... I initially was going for the H^1 and H^2 seminorms, but they didn't quite work...)
function F = untangle(F)
nFuncs = size(F,1);
nPoints = size(F,2);
seminorm = #(x,i) sum(sum(abs(diff(x(:,1:i),1,2)))) + ...
sum(sum(abs(diff(x(:,1:i),2,2)))) + ...
sum(sum(abs(diff(x(:,1:i),3,2)))) + ...
sum(sum(abs(diff(x(:,1:i),4,2))));
doSwap = #(x,swap,i) [x(:,1:i-1), x(swap,i:end)];
swaps = perms(1:nFuncs);
normOfSwap = zeros(1,size(swaps,1));
for i = 2:nPoints
for j = 1:size(swaps,1)
normOfSwap(j) = seminorm(doSwap(F,swaps(j,:),i),i);
end
[~,j_bestSwap] = min(normOfSwap);
F = doSwap(F,swaps(j_bestSwap,:),i);
end
Usage:
The command alpha2 = untangle(alpha2); will untangle your functions:
It should even work for more complicated data, like these shuffled sine-waves:
nPoints = 100;
nFuncs = 5;
t = linspace(0, 2*pi, nPoints);
F = bsxfun(#(a,b) sin(a*b), (1:nFuncs).', t);
for i = 1:nPoints
F(:,i) = F(randperm(nFuncs),i);
end
Remark: I guess if you already know that your functions will be quadratic or some other special form, RANSAC would be a better idea for larger number of functions. This could also be useful if the functions are not given with the same x-value spacing.

Solving for Fisher Kolmagorov Partial Differential Equation

I have been trying to solve the non dimensional Fisher Kolmagorov equation in Matlab. I am getting a graph which doesn't look at all like it should. Also, I'm getting the equation independent of value of s (the source term in the pdepe solver). No matter what value of s I put in the graph remains the same.
function FK
m = 0;
x = linspace(0,1,100);
t = linspace(0,1,100);
u = pdepe(m,#FKpde,#FKic,#FKbc,x,t);
[X,T] = meshgrid(x,t);
%ANALYTICAL SOLUTION
% a=(sqrt(2))-1;
% q=2;
% s=2/q;
% b= q /((2*(q+2))^0.5);
% c= (q+4)/((2*(q+2))^0.5);
% zeta= X-c*T;
%y = 1/((1+(a*(exp(b*zeta))))^s);
%r=(y(:,:)-u(:,:))./y(:,:); % relative error in numerical and analytical value
figure;
plot(x,u(10,:),'o',x,u(40,:),'o',x,u(60,:),'o',x,u(end,:),'o')
title('Numerical Solutions at different times');
legend('tn=1','tn=5','tn=30','tn=10','ta=20','ta=600','ta=800','ta=1000',0);
xlabel('Distance x');
ylabel('u(x,t)');
% --------------------------------------------------------------------------
function [c,f,s] = FKpde(x,t,u,DuDx)
c = 1;
f = DuDx;
s =u*(1-u);
% --------------------------------------------------------------------------
function u0 = FKic(x)
u0 = 10^(-4);
% --------------------------------------------------------------------------
function [pl,ql,pr,qr] = FKbc(xl,ul,xr,ur,t)
pl = ul-1;
ql = 0;
pr = ur;
qr = 0;
Should maybe be a comment, but putting it as an answer for better formatting. Your analytic solution, which I assume you're using to compare with the numerical answer to say that the graph does not look as it should, does not appear to respect the initial conditions or boundary conditions you are feeding pdepe, so I'd start there if trying to figure out why u does not look like y
The initial and boundary conditions you are setting are:
u(0, t) = 1
u(1, t) = 0
u(x, 0) = 1e-4
Setting aside that the boundary and initial conditions are in conflict, the analytic solution you suggest in the code has
u(0, t) = 1/((1+exp(-b*ct)))
u(1, t) = 1/((1+exp(b*(1-ct)))
u(x, 0) = 1/((1+exp(b*x))
So it seems to me the numerical and analytic solutions should be expected to be different, and the differences you observe are probably due to the IC/BC setup. I suspect that pdepe is probably solving the equation you are giving it.
On increasing the length scale and time scale, I get the answers I want. The problem was to solve for different times, and see the wave propogate. For small lenghts, I could only see part of that wave.