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Create Analytical Models for Simscape

This example finds parameterized analytical expressions to model the displacement of a joint for a trivial cantilever truss structure in both static and frequency domains for use in Simscape.

The analytical expression for the static case is exact. The expression for the frequency response function is an approximate reduced-order version of the actual frequency response.

This example uses the following Symbolic Math Toolbox capablities:

Define Parameters of the Model

The goal of this example is to analytically relate the displacement, d, to the uniform cross-section area parameter, A, for a particular bar in the cantilever truss structure. Here, d results from a load at the right upper joint of the cantilever. The truss is attached to the wall at the left most joints.

The truss bars are characterized by a linear elastic material with uniform density. Other than A, all other parameters including cross-section areas (uniform) of the other bars are fixed. The displacement of the tip is obtained using small (linear) displacement assumptions.

First, define the following fixed parameters of the truss.

The length and height of the truss and the number of top horizonal truss bars:

L = 1;
H = 0.25;
N = 2;

Density and modulus of elasticity of the truss bar material:

rhoval = 1e2;
Eval = 1e7;

Area of the truss bar cross-section:

AFixed = 1;

Then, define the local stiffness matrix of a planar truss element.

Compute the local stiffness matrix , k, and express it as a symbolic function. The columns of the stiffness matrix correspond to [x_node1,y_node1,x_node2,y_node2];

syms A E l t real
G = [cos(t) sin(t) 0 0; 0 0 cos(t) sin(t)];
kk = A*E/l*[1 -1;-1 1];
k = simplify(G'*kk*G);
localStiffnessFn = symfun(k,[t,l,E])
localStiffnessFn(t, l, E) = 

Similarly, compute the mass matrix

syms rho
mm = A*rho*l/6*[2 1;1 2];
m = simplify(G'*mm*G);
localMassFn = symfun(m,[t,l,rho]);

Now, define the bars of truss as a matrix, bars. The number of rows is the number of bars of the truss. The columns of bars are as follows:

  • Length(l)

  • Angle with repsect to the horizontal axis(t)

  • Starting joint

  • Ending joint

bars = zeros(2*N+2*(N-1),4);

Define upper and diagonal bars. Note that the bar of interest is the (N+1)th bar which is the first diagonal bar from the left.

for n = 1:N
    lelem = L/N;
    bars(n,:) = [lelem,0,n,n+1];
    lelem = sqrt((L/N)^2+H^2);
    bars(N+n,:) = [lelem,atan2(H,L/N),N+1+n,n+1];
end

Define lower and vertical bars.

for n = 1:N-1
    lelem = L/N;
    bars(2*N+n,:) = [lelem,0,N+1+n,N+1+n+1];
    lelem = H;
    bars(2*N+N-1+n,:) = [lelem,pi/2,N+1+n+1,n+1];
end

Assemble Bars into Symbolic Global Matrices

The number of degrees of freedom (DoF) in this system is

numDofs = 2*2*(N+1)-2
numDofs = 10

The system mass matrix, M, and system stiffness matrix, K, are symbolic matrices. Assemble local element matrices (also symbolic) from each bar into the corresponding system matrix.

K = sym(zeros(numDofs,numDofs));
M = sym(zeros(numDofs,numDofs));
for j=1:size(bars,1)
    % Construct stiffness and mass matrices for bar, j.
    lelem = bars(j,1); telem = bars(j,2);
    kelem = localStiffnessFn(telem,lelem,Eval);
    melem = localMassFn(telem,lelem,rhoval);
    n1 = bars(j,3); n2 = bars(j,4);
    % For bars that are not within the parameterized area A, set stiffness
    % and mass matrices to numeric values. Note that although the values
    % kelem and melem do not have symbolic parameters, they are still
    % symbolic objects(symbolic numbers).
    if j ~= N+1
        kelem = subs(kelem,A,AFixed);
        melem = subs(melem,A,AFixed);
    end
    % Arrange indices.
    ix = [n1*2-1,2*n1,n2*2-1,n2*2];
    % Embed local elements into system matrices.
    K(ix,ix) = K(ix,ix) + kelem;
    M(ix,ix) = M(ix,ix) + melem;
end

Eliminate degrees of freedom attached to the wall.

wallDofs = [1,2,2*(N+1)+1,2*(N+1)+2]; % DoFs to eliminate
K(wallDofs,:) = [];
K(:,wallDofs) = [];
M(wallDofs,:) = [];
M(:,wallDofs) = [];

F is the load vector that has a load specified in the negative Y direction at the upper rightmost joint.

F = zeros(size(K,1),1);
F(2*N) = -1;

To extract the Y displacement at the upper rightmost joint, create the following selection vector.

c = zeros(1,size(K,1));
c(2*N) = 1;

Create Simscape Equations from Exact Symbolic Solution for Static Case

Plot the analytical solution. Here, K\F represents the displacements at all joints, and c selects the particular displacements. First, print symbolic solution representing numeric values using 16 digits for compactness.

d_Static = simplify(c*(K\F));
vpa(d_Static,16)
ans = 

fplot(d_Static,[AFixed/10,10*AFixed]);
hold on;
xlabel('Cross-section, A');
ylabel('Displacement');
title('')

Convert the result to the Simscape equation, ssEq, to use inside a Simscape block. To display the resulting Simscape equation, remove the semicolon.

syms d
ssEq = simscapeEquation(d,d_Static)
ssEq = 
'd == (A*(-5.0e-7)-sqrt(5.0)*A^2*1.0e-7-A^2*cos(9.272952180016122e-1)*1.2e-6-A^2*cos(1.854590436003224)*2.75e-7-A^2*9.25e-7-sqrt(5.0)*A*7.5e-7-sqrt(5.0)*A*cos(9.272952180016122e-1)*6.5e-7-sqrt(5.0)*A^2*cos(9.272952180016122e-1)*1.0e-7-5.0e-7)/(A*(A-A*cos(1.854590436003224)-sqrt(5.0)*cos(9.272952180016122e-1)*2.0+sqrt(5.0)*2.0));'

Compare Numeric and Symbolic Static Solutions

Compare the exact analytical solution and a numeric solution over a range of A parameter values. The values form a sequence from AFixed to 5*AFixed in increments of 1.

AParamValues = AFixed*(1:5)';
d_NumericArray = zeros(size(AParamValues));
for k=1:numel(AParamValues)
    beginDim = (k-1)*size(K,1)+1;
    endDim = k*size(K,1);
    % Create a numeric stiffness matrix and extract the numeric solution.
    KNumeric = double(subs(K,A,AParamValues(k)));
    d_NumericArray(k) = c*(KNumeric\F);
end

Compute the symbolic values over Aparamrange. For this, call the subs function, and then convert the result to double-precision numbers using double.

d_SymArray = double(subs(d_Static,A,AParamValues));

To visualize the data, use a table.

T = table(AParamValues,d_SymArray,d_NumericArray)
T=5x3 table
    AParamValues    d_SymArray     d_NumericArray
    ____________    ___________    ______________

    1                -1.784e-06     -1.784e-06   
    2               -1.6443e-06    -1.6443e-06   
    3               -1.5977e-06    -1.5977e-06   
    4               -1.5744e-06    -1.5744e-06   
    5               -1.5604e-06    -1.5604e-06   

Approximate Parametric Symbolic Solution for Frequency Response

A parametric representation for the frequency response H(s,A) can be efficient for quickly examining the effects of the parameter A without resorting to potentially expensive numeric calculations for each new value of A. However, finding an exact closed-form solution for a large system with additional parameters is often impossible. Therefore, you typically approximate a solution for such systems. This example approximates the parametric frequency response near the zero frequency as follows:

  • Find the Pade approximation of the transfer function around the frequency s = 0 using the first three moments of the series. The idea is that given a transfer function, you can compute the Pade approximation moments as , where correspond to the power series terms . For this example, compute HApprox(s,A) as the sum of the first three terms. This approach is a very basic technique applicable for reducing the order of the transfer function. Other, more robust techniques discussed in the literature are outside the scope of this example.

  • Speed up computations by usinf variable-precision arithmetic (vpa).

  • To further speed up calculations, approximate the inner term of each moment term in terms of a Taylor series expansion of A around AFixed.

Use vpa to speed up calculations.

digits(32);
K = vpa(K);
M = vpa(M);

Compute the LU-decomposition of K.

[LK,UK] = lu(K);

Create helper variables and functions.

iK = @(x) UK\(LK\x);
iK_M = @(x) -iK(M*x);
momentPre = iK(F);

Define frequency series corresponding to the first three moments. Here, s is the frequency.

syms s
sPowers = [1 s^2 s^4];

Set the first moment which is the same as d_Static that you computed previously.

moments = d_Static;

Compute the remaining moments.

for p=2:numel(sPowers)
    momentPre = iK_M(momentPre);
    for pp=1:numel(momentPre)
        momentPre(pp) = taylor(momentPre(pp),A,AFixed,'Order',3);
    end
    moment = c*momentPre;
    moments = [moments;moment];
end

Combine the moment terms to create the approximate analytical frequency response Happrox.

HApprox = sPowers*moments;

Display moments. Because the expressions are large, you can display them only partially.

momentString = arrayfun(@char,moments,'UniformOutput',false);
freqString = arrayfun(@char,sPowers.','UniformOutput',false);
table(freqString,momentString,'VariableNames',{'FreqPowers','Moments'})
ans=3x2 table
    FreqPowers                                                                                                                                                                                               Moments                                                                                                                                                                                            
    __________    ______________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________

    '1'           '-(20*A + 4*5^(1/2)*A^2 + 48*A^2*cos(8352332796509007/9007199254740992) + 11*A^2*cos(8352332796509007/4503599627370496) + 37*A^2 + 30*5^(1/2)*A + 26*5^(1/2)*A*cos(8352332796509007/9007199254740992) + 4*5^(1/2)*A^2*cos(8352332796509007/9007199254740992) + 20)/(40000000*A*(A - A*cos(8352332796509007/4503599627370496) - 2*5^(1/2)*cos(8352332796509007/9007199254740992) + 2*5^(1/2)))'
    's^2'         '0.00000000001931549865390535481955339404344*(A - 1)^2 - 0.000000000016488909600194499035418898203187*A + 0.000000000045018038433136039672236177596169'                                                                                                                                                                                                                                       
    's^4'         '0.0000000000000005984738130764688358429817612765*A - 0.00000000000000082093440076592193426384675490731*(A - 1)^2 - 0.0000000000000011768131579530973008654036198437'                                                                                                                                                                                                                         

Convert the frequency response to a MATLAB function containing numeric values for A and s. The resulting MATLAB function does not require Symbolic Math Toolbox.

HApproxFun = matlabFunction(HApprox,'vars',[A,s]);

Compare Purely Numeric and Symbolically Derived Frequency Responses

Vary frequency from 0 to 1 in logspace, and then convert the range to radians.

freq = 2*pi*logspace(0,1);

Compute the numeric values of the frequency response for A = AFixed*perturbFactor, that is, at a small perturbation around A.

perturbFactor = 1 + 0.25;
KFixed = double(subs(K,A,AFixed*perturbFactor));
MFixed = double(subs(M,A,AFixed*perturbFactor));
H_Numeric = zeros(size(freq));
for k=1:numel(freq)
    sVal = 1i*freq(k);
    H_Numeric(k) = c*((sVal^2*MFixed + KFixed)\F);
end

Compute the symbolic values of the frequency response over the frequency range and A = perturbFactor*AFixed.

H_Symbolic = HApproxFun(AFixed*perturbFactor,freq*1i);

Plot the results

figure
loglog(freq/(2*pi),abs(H_Symbolic),freq/(2*pi),abs(H_Numeric),'k*');
xlabel('Frequency');
ylabel('Frequency Response');
legend('Symbolic','Numeric');

The analytical and numeric curves are close in the chosen interval, but, in general, the curves diverge for frequencies outside the neighborhood of s = 0. Also for values of A far from AFixed, the Taylor approximation introduces larger errors.

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