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SVD Control

Table of Contents

1 Gravimeter - Simscape Model

1.1 Introduction

gravimeter_model.png

Figure 1: Model of the gravimeter

1.2 Simscape Model - Parameters

open('gravimeter.slx')

Parameters 

l  = 1.0; % Length of the mass [m]
la = 0.5; % Position of Act. [m]

h  = 3.4; % Height of the mass [m]
ha = 1.7; % Position of Act. [m]

m = 400; % Mass [kg]
I = 115; % Inertia [kg m^2]

k = 15e3; % Actuator Stiffness [N/m]
c = 0.03; % Actuator Damping [N/(m/s)]

deq = 0.2; % Length of the actuators [m]

g = 0; % Gravity [m/s2]

1.3 System Identification - Without Gravity

%% Name of the Simulink File
mdl = 'gravimeter';

%% Input/Output definition
clear io; io_i = 1;
io(io_i) = linio([mdl, '/F1'], 1, 'openinput');  io_i = io_i + 1;
io(io_i) = linio([mdl, '/F2'], 1, 'openinput');  io_i = io_i + 1;
io(io_i) = linio([mdl, '/F3'], 1, 'openinput');  io_i = io_i + 1;
io(io_i) = linio([mdl, '/Acc_side'], 1, 'openoutput'); io_i = io_i + 1;
io(io_i) = linio([mdl, '/Acc_side'], 2, 'openoutput'); io_i = io_i + 1;
io(io_i) = linio([mdl, '/Acc_top'], 1, 'openoutput'); io_i = io_i + 1;
io(io_i) = linio([mdl, '/Acc_top'], 2, 'openoutput'); io_i = io_i + 1;

G = linearize(mdl, io);
G.InputName  = {'F1', 'F2', 'F3'};
G.OutputName = {'Ax1', 'Az1', 'Ax2', 'Az2'};

pole(G)
ans =
      -0.000473481142385795 +      21.7596190728632i
      -0.000473481142385795 -      21.7596190728632i
      -7.49842879459172e-05 +       8.6593576906982i
      -7.49842879459172e-05 -       8.6593576906982i
       -5.1538686792578e-06 +      2.27025295182756i
       -5.1538686792578e-06 -      2.27025295182756i

The plant as 6 states as expected (2 translations + 1 rotation) 

size(G)

State-space model with 4 outputs, 3 inputs, and 6 states.

open_loop_tf.png

Figure 2: Open Loop Transfer Function from 3 Actuators to 4 Accelerometers

1.4 System Identification - With Gravity

g = 9.80665; % Gravity [m/s2]

Gg = linearize(mdl, io);
Gg.InputName  = {'F1', 'F2', 'F3'};
Gg.OutputName = {'Ax1', 'Az1', 'Ax2', 'Az2'};

We can now see that the system is unstable due to gravity. 

pole(Gg)
ans =
          -10.9848275341252 +                     0i
           10.9838836405201 +                     0i
      -7.49855379478109e-05 +      8.65962885770051i
      -7.49855379478109e-05 -      8.65962885770051i
      -6.68819548733559e-06 +     0.832960422243848i
      -6.68819548733559e-06 -     0.832960422243848i

open_loop_tf_g.png

Figure 3: Open Loop Transfer Function from 3 Actuators to 4 Accelerometers with an without gravity

1.5 Analytical Model

1.5.1 Parameters

Bode options. 

P = bodeoptions;
P.FreqUnits = 'Hz';
P.MagUnits = 'abs';
P.MagScale = 'log';
P.Grid = 'on';
P.PhaseWrapping = 'on';
P.Title.FontSize = 14;
P.XLabel.FontSize = 14;
P.YLabel.FontSize = 14;
P.TickLabel.FontSize = 12;
P.Xlim = [1e-1,1e2];
P.MagLowerLimMode = 'manual';
P.MagLowerLim= 1e-3;
%P.PhaseVisible = 'off';

Frequency vector. 

w = 2*pi*logspace(-1,2,1000); % [rad/s]

1.5.2 generation of the state space model

M = [m 0 0
     0 m 0
     0 0 I];

%Jacobian of the bottom sensor
Js1 = [1 0  h/2
       0 1 -l/2];
%Jacobian of the top sensor
Js2 = [1 0 -h/2
       0 1  0];

%Jacobian of the actuators
Ja = [1 0  ha   % Left horizontal actuator
      0 1 -la   % Left vertical actuator
      0 1  la]; % Right vertical actuator
Jta = Ja';
K = k*Jta*Ja;
C = c*Jta*Ja;

E = [1 0 0
     0 1 0
     0 0 1]; %projecting ground motion in the directions of the legs

AA = [zeros(3) eye(3)
      -M\K -M\C];

BB = [zeros(3,6)
      M\Jta M\(k*Jta*E)];

% BB = [zeros(3,3)
%     M\Jta ];
%
% CC = [Ja zeros(3)];
% DD = zeros(3,3);

CC = [[Js1;Js2] zeros(4,3);
      zeros(2,6)
      (Js1+Js2)./2 zeros(2,3)
      (Js1-Js2)./2 zeros(2,3)
      (Js1-Js2)./(2*h) zeros(2,3)];

DD = [zeros(4,6)
      zeros(2,3) eye(2,3)
      zeros(6,6)];

system_dec = ss(AA,BB,CC,DD);
  • Input = three actuators and three ground motions
  • Output = the bottom sensor; the top sensor; the ground motion; the half sum; the half difference; the rotation
size(system_dec)

State-space model with 12 outputs, 6 inputs, and 6 states.

1.5.3 Comparison with the Simscape Model

gravimeter_analytical_system_open_loop_models.png

Figure 4: Comparison of the analytical and the Simscape models

1.5.4 Analysis

% figure
% bode(system_dec,P);
% return

%% svd decomposition
% system_dec_freq = freqresp(system_dec,w);
% S = zeros(3,length(w));
% for m = 1:length(w)
%     S(:,m) = svd(system_dec_freq(1:4,1:3,m));
% end
% figure
% loglog(w./(2*pi), S);hold on;
% % loglog(w./(2*pi), abs(Val(1,:)),w./(2*pi), abs(Val(2,:)),w./(2*pi), abs(Val(3,:)));
% xlabel('Frequency [Hz]');ylabel('Singular Value [-]');
% legend('\sigma_1','\sigma_2','\sigma_3');%,'\sigma_4','\sigma_5','\sigma_6');
% ylim([1e-8 1e-2]);
%
% %condition number
% figure
% loglog(w./(2*pi), S(1,:)./S(3,:));hold on;
% % loglog(w./(2*pi), abs(Val(1,:)),w./(2*pi), abs(Val(2,:)),w./(2*pi), abs(Val(3,:)));
% xlabel('Frequency [Hz]');ylabel('Condition number [-]');
% % legend('\sigma_1','\sigma_2','\sigma_3');%,'\sigma_4','\sigma_5','\sigma_6');
%
% %performance indicator
% system_dec_svd = freqresp(system_dec(1:4,1:3),2*pi*10);
% [U,S,V] = svd(system_dec_svd);
% H_svd_OL = -eye(3,4);%-[zpk(-2*pi*10,-2*pi*40,40/10) 0 0 0; 0 10*zpk(-2*pi*40,-2*pi*200,40/200) 0 0; 0 0 zpk(-2*pi*2,-2*pi*10,10/2) 0];% - eye(3,4);%
% H_svd = pinv(V')*H_svd_OL*pinv(U);
% % system_dec_control_svd_ = feedback(system_dec,g*pinv(V')*H*pinv(U));
%
% OL_dec = g_svd*H_svd*system_dec(1:4,1:3);
% OL_freq = freqresp(OL_dec,w); % OL = G*H
% CL_system = feedback(eye(3),-g_svd*H_svd*system_dec(1:4,1:3));
% CL_freq = freqresp(CL_system,w); % CL = (1+G*H)^-1
% % CL_system_2 = feedback(system_dec,H);
% % CL_freq_2 = freqresp(CL_system_2,w); % CL = G/(1+G*H)
% for i = 1:size(w,2)
%     OL(:,i) = svd(OL_freq(:,:,i));
%     CL (:,i) = svd(CL_freq(:,:,i));
%     %CL2 (:,i) = svd(CL_freq_2(:,:,i));
% end
%
% un = ones(1,length(w));
% figure
% loglog(w./(2*pi),OL(3,:)+1,'k',w./(2*pi),OL(3,:)-1,'b',w./(2*pi),1./CL(1,:),'r--',w./(2*pi),un,'k:');hold on;%
% % loglog(w./(2*pi), 1./(CL(2,:)),w./(2*pi), 1./(CL(3,:)));
% % semilogx(w./(2*pi), 1./(CL2(1,:)),w./(2*pi), 1./(CL2(2,:)),w./(2*pi), 1./(CL2(3,:)));
% xlabel('Frequency [Hz]');ylabel('Singular Value [-]');
% legend('GH \sigma_{inf} +1 ','GH \sigma_{inf} -1','S 1/\sigma_{sup}');%,'\lambda_1','\lambda_2','\lambda_3');
%
% figure
% loglog(w./(2*pi),OL(1,:)+1,'k',w./(2*pi),OL(1,:)-1,'b',w./(2*pi),1./CL(3,:),'r--',w./(2*pi),un,'k:');hold on;%
% % loglog(w./(2*pi), 1./(CL(2,:)),w./(2*pi), 1./(CL(3,:)));
% % semilogx(w./(2*pi), 1./(CL2(1,:)),w./(2*pi), 1./(CL2(2,:)),w./(2*pi), 1./(CL2(3,:)));
% xlabel('Frequency [Hz]');ylabel('Singular Value [-]');
% legend('GH \sigma_{sup} +1 ','GH \sigma_{sup} -1','S 1/\sigma_{inf}');%,'\lambda_1','\lambda_2','\lambda_3');

1.5.5 Control Section

system_dec_10Hz = freqresp(system_dec,2*pi*10);
system_dec_0Hz = freqresp(system_dec,0);

system_decReal_10Hz = pinv(align(system_dec_10Hz));
[Ureal,Sreal,Vreal] = svd(system_decReal_10Hz(1:4,1:3));
normalizationMatrixReal = abs(pinv(Ureal)*system_dec_0Hz(1:4,1:3)*pinv(Vreal'));

[U,S,V] = svd(system_dec_10Hz(1:4,1:3));
normalizationMatrix = abs(pinv(U)*system_dec_0Hz(1:4,1:3)*pinv(V'));

H_dec = ([zpk(-2*pi*5,-2*pi*30,30/5) 0 0 0
          0 zpk(-2*pi*4,-2*pi*20,20/4) 0 0
          0 0 0 zpk(-2*pi,-2*pi*10,10)]);
H_cen_OL = [zpk(-2*pi,-2*pi*10,10) 0 0; 0 zpk(-2*pi,-2*pi*10,10) 0;
            0 0 zpk(-2*pi*5,-2*pi*30,30/5)];
H_cen = pinv(Jta)*H_cen_OL*pinv([Js1; Js2]);
% H_svd_OL = -[1/normalizationMatrix(1,1) 0 0 0
%     0 1/normalizationMatrix(2,2) 0 0
%     0 0 1/normalizationMatrix(3,3) 0];
% H_svd_OL_real = -[1/normalizationMatrixReal(1,1) 0 0 0
%     0 1/normalizationMatrixReal(2,2) 0 0
%     0 0 1/normalizationMatrixReal(3,3) 0];
H_svd_OL = -[1/normalizationMatrix(1,1)*zpk(-2*pi*10,-2*pi*60,60/10) 0 0 0
             0 1/normalizationMatrix(2,2)*zpk(-2*pi*5,-2*pi*30,30/5) 0 0
             0 0 1/normalizationMatrix(3,3)*zpk(-2*pi*2,-2*pi*10,10/2) 0];
H_svd_OL_real = -[1/normalizationMatrixReal(1,1)*zpk(-2*pi*10,-2*pi*60,60/10) 0 0 0
                  0 1/normalizationMatrixReal(2,2)*zpk(-2*pi*5,-2*pi*30,30/5) 0 0
                  0 0 1/normalizationMatrixReal(3,3)*zpk(-2*pi*2,-2*pi*10,10/2) 0];
% H_svd_OL_real = -[zpk(-2*pi*10,-2*pi*40,40/10) 0 0 0; 0 10*zpk(-2*pi*10,-2*pi*100,100/10) 0 0; 0 0 zpk(-2*pi*2,-2*pi*10,10/2) 0];%-eye(3,4);
% H_svd_OL = -[zpk(-2*pi*10,-2*pi*40,40/10) 0 0 0; 0 zpk(-2*pi*4,-2*pi*20,4/20) 0 0; 0 0 zpk(-2*pi*2,-2*pi*10,10/2) 0];% - eye(3,4);%
H_svd = pinv(V')*H_svd_OL*pinv(U);
H_svd_real = pinv(Vreal')*H_svd_OL_real*pinv(Ureal);

OL_dec = g*H_dec*system_dec(1:4,1:3);
OL_cen = g*H_cen_OL*pinv([Js1; Js2])*system_dec(1:4,1:3)*pinv(Jta);
OL_svd = 100*H_svd_OL*pinv(U)*system_dec(1:4,1:3)*pinv(V');
OL_svd_real = 100*H_svd_OL_real*pinv(Ureal)*system_dec(1:4,1:3)*pinv(Vreal');

% figure
% bode(OL_dec,w,P);title('OL Decentralized');
% figure
% bode(OL_cen,w,P);title('OL Centralized');

figure
bode(g*system_dec(1:4,1:3),w,P);
title('gain * Plant');

figure
bode(OL_svd,OL_svd_real,w,P);
title('OL SVD');
legend('SVD of Complex plant','SVD of real approximation of the complex plant')

figure
bode(system_dec(1:4,1:3),pinv(U)*system_dec(1:4,1:3)*pinv(V'),P);

CL_dec = feedback(system_dec,g*H_dec,[1 2 3],[1 2 3 4]);
CL_cen = feedback(system_dec,g*H_cen,[1 2 3],[1 2 3 4]);
CL_svd = feedback(system_dec,100*H_svd,[1 2 3],[1 2 3 4]);
CL_svd_real = feedback(system_dec,100*H_svd_real,[1 2 3],[1 2 3 4]);

pzmap_testCL(system_dec,H_dec,g,[1 2 3],[1 2 3 4])
title('Decentralized control');

pzmap_testCL(system_dec,H_cen,g,[1 2 3],[1 2 3 4])
title('Centralized control');

pzmap_testCL(system_dec,H_svd,100,[1 2 3],[1 2 3 4])
title('SVD control');

pzmap_testCL(system_dec,H_svd_real,100,[1 2 3],[1 2 3 4])
title('Real approximation SVD control');

P.Ylim = [1e-8 1e-3];
figure
bodemag(system_dec(1:4,1:3),CL_dec(1:4,1:3),CL_cen(1:4,1:3),CL_svd(1:4,1:3),CL_svd_real(1:4,1:3),P);
title('Motion/actuator')
legend('Control OFF','Decentralized control','Centralized control','SVD control','SVD control real appr.');

P.Ylim = [1e-5 1e1];
figure
bodemag(system_dec(1:4,4:6),CL_dec(1:4,4:6),CL_cen(1:4,4:6),CL_svd(1:4,4:6),CL_svd_real(1:4,4:6),P);
title('Transmissibility');
legend('Control OFF','Decentralized control','Centralized control','SVD control','SVD control real appr.');

figure
bodemag(system_dec([7 9],4:6),CL_dec([7 9],4:6),CL_cen([7 9],4:6),CL_svd([7 9],4:6),CL_svd_real([7 9],4:6),P);
title('Transmissibility from half sum and half difference in the X direction');
legend('Control OFF','Decentralized control','Centralized control','SVD control','SVD control real appr.');

figure
bodemag(system_dec([8 10],4:6),CL_dec([8 10],4:6),CL_cen([8 10],4:6),CL_svd([8 10],4:6),CL_svd_real([8 10],4:6),P);
title('Transmissibility from half sum and half difference in the Z direction');
legend('Control OFF','Decentralized control','Centralized control','SVD control','SVD control real appr.');

1.5.6 Greshgorin radius

system_dec_freq = freqresp(system_dec,w);
x1 = zeros(1,length(w));
z1 = zeros(1,length(w));
x2 = zeros(1,length(w));
S1 = zeros(1,length(w));
S2 = zeros(1,length(w));
S3 = zeros(1,length(w));

for t = 1:length(w)
    x1(t) = (abs(system_dec_freq(1,2,t))+abs(system_dec_freq(1,3,t)))/abs(system_dec_freq(1,1,t));
    z1(t) = (abs(system_dec_freq(2,1,t))+abs(system_dec_freq(2,3,t)))/abs(system_dec_freq(2,2,t));
    x2(t) = (abs(system_dec_freq(3,1,t))+abs(system_dec_freq(3,2,t)))/abs(system_dec_freq(3,3,t));
    system_svd = pinv(Ureal)*system_dec_freq(1:4,1:3,t)*pinv(Vreal');
    S1(t) = (abs(system_svd(1,2))+abs(system_svd(1,3)))/abs(system_svd(1,1));
    S2(t) = (abs(system_svd(2,1))+abs(system_svd(2,3)))/abs(system_svd(2,2));
    S2(t) = (abs(system_svd(3,1))+abs(system_svd(3,2)))/abs(system_svd(3,3));
end

limit = 0.5*ones(1,length(w));

figure
loglog(w./(2*pi),x1,w./(2*pi),z1,w./(2*pi),x2,w./(2*pi),limit,'--');
legend('x_1','z_1','x_2','Limit');
xlabel('Frequency [Hz]');
ylabel('Greshgorin radius [-]');

figure
loglog(w./(2*pi),S1,w./(2*pi),S2,w./(2*pi),S3,w./(2*pi),limit,'--');
legend('S1','S2','S3','Limit');
xlabel('Frequency [Hz]');
ylabel('Greshgorin radius [-]');
% set(gcf,'color','w')

1.5.7 Injecting ground motion in the system to have the output

Fr = logspace(-2,3,1e3);
w=2*pi*Fr*1i;
%fit of the ground motion data in m/s^2/rtHz
Fr_ground_x = [0.07 0.1 0.15 0.3 0.7 0.8 0.9 1.2 5 10];
n_ground_x1 = [4e-7 4e-7 2e-6 1e-6 5e-7 5e-7 5e-7 1e-6 1e-5 3.5e-5];
Fr_ground_v = [0.07 0.08 0.1 0.11 0.12 0.15 0.25 0.6 0.8 1 1.2 1.6 2 6 10];
n_ground_v1 = [7e-7 7e-7 7e-7 1e-6 1.2e-6 1.5e-6 1e-6 9e-7 7e-7 7e-7 7e-7 1e-6 2e-6 1e-5 3e-5];

n_ground_x = interp1(Fr_ground_x,n_ground_x1,Fr,'linear');
n_ground_v = interp1(Fr_ground_v,n_ground_v1,Fr,'linear');
% figure
% loglog(Fr,abs(n_ground_v),Fr_ground_v,n_ground_v1,'*');
% xlabel('Frequency [Hz]');ylabel('ASD [m/s^2 /rtHz]');
% return

%converting into PSD
n_ground_x = (n_ground_x).^2;
n_ground_v = (n_ground_v).^2;

%Injecting ground motion in the system and getting the outputs
system_dec_f = (freqresp(system_dec,abs(w)));
PHI = zeros(size(Fr,2),12,12);
for p = 1:size(Fr,2)
    Sw=zeros(6,6);
    Iact = zeros(3,3);
    Sw(4,4) = n_ground_x(p);
    Sw(5,5) = n_ground_v(p);
    Sw(6,6) = n_ground_v(p);
    Sw(1:3,1:3) = Iact;
    PHI(p,:,:) = (system_dec_f(:,:,p))*Sw(:,:)*(system_dec_f(:,:,p))';
end
x1 = PHI(:,1,1);
z1 = PHI(:,2,2);
x2 = PHI(:,3,3);
z2 = PHI(:,4,4);
wx = PHI(:,5,5);
wz = PHI(:,6,6);
x12 = PHI(:,1,3);
z12 = PHI(:,2,4);
PHIwx = PHI(:,1,5);
PHIwz = PHI(:,2,6);
xsum = PHI(:,7,7);
zsum = PHI(:,8,8);
xdelta = PHI(:,9,9);
zdelta = PHI(:,10,10);
rot = PHI(:,11,11);

2 Gravimeter - Functions

2.1 align

This Matlab function is accessible here. 

function [A] = align(V)
%A!ALIGN(V) returns a constat matrix A which is the real alignment of the
%INVERSE of the complex input matrix V
%from Mohit slides

    if (nargin ==0) || (nargin > 1)
        disp('usage: mat_inv_real = align(mat)')
        return
    end

    D = pinv(real(V'*V));
    A = D*real(V'*diag(exp(1i * angle(diag(V*D*V.'))/2)));


end

2.2 pzmap_testCL

This Matlab function is accessible here. 

function [] = pzmap_testCL(system,H,gain,feedin,feedout)
% evaluate and plot the pole-zero map for the closed loop system for
% different values of the gain

    [~, n] = size(gain);
    [m1, n1, ~] = size(H);
    [~,n2] = size(feedin);

    figure
    for i = 1:n
        %     if n1 == n2
        system_CL = feedback(system,gain(i)*H,feedin,feedout);

        [P,Z] = pzmap(system_CL);
        plot(real(P(:)),imag(P(:)),'x',real(Z(:)),imag(Z(:)),'o');hold on
        xlabel('Real axis (s^{-1})');ylabel('Imaginary Axis (s^{-1})');
        %         clear P Z
        %     else
        %         system_CL = feedback(system,gain(i)*H(:,1+(i-1)*m1:m1+(i-1)*m1),feedin,feedout);
        %
        %         [P,Z] = pzmap(system_CL);
        %         plot(real(P(:)),imag(P(:)),'x',real(Z(:)),imag(Z(:)),'o');hold on
        %         xlabel('Real axis (s^{-1})');ylabel('Imaginary Axis (s^{-1})');
        %         clear P Z
        %     end
    end
    str = {strcat('gain = ' , num2str(gain(1)))};  % at the end of first loop, z being loop output
    str = [str , strcat('gain = ' , num2str(gain(1)))]; % after 2nd loop
    for i = 2:n
        str = [str , strcat('gain = ' , num2str(gain(i)))]; % after 2nd loop
        str = [str , strcat('gain = ' , num2str(gain(i)))]; % after 2nd loop
    end
    legend(str{:})
end

3 Stewart Platform - Simscape Model

3.1 Jacobian

First, the position of the “joints” (points of force application) are estimated and the Jacobian computed. 

open('stewart_platform/drone_platform_jacobian.slx');

sim('drone_platform_jacobian');

Aa = [a1.Data(1,:);
      a2.Data(1,:);
      a3.Data(1,:);
      a4.Data(1,:);
      a5.Data(1,:);
      a6.Data(1,:)]';

Ab = [b1.Data(1,:);
      b2.Data(1,:);
      b3.Data(1,:);
      b4.Data(1,:);
      b5.Data(1,:);
      b6.Data(1,:)]';

As = (Ab - Aa)./vecnorm(Ab - Aa);

l = vecnorm(Ab - Aa)';

J = [As' , cross(Ab, As)'];

save('./jacobian.mat', 'Aa', 'Ab', 'As', 'l', 'J');

3.2 Simscape Model

open('stewart_platform/drone_platform.slx');

Definition of spring parameters 

kx = 50; % [N/m]
ky = 50;
kz = 50;

cx = 0.025; % [Nm/rad]
cy = 0.025;
cz = 0.025;

We load the Jacobian. 

load('./jacobian.mat', 'Aa', 'Ab', 'As', 'l', 'J');

3.3 Identification of the plant

The dynamics is identified from forces applied by each legs to the measured acceleration of the top platform. 

%% Name of the Simulink File
mdl = 'drone_platform';

%% Input/Output definition
clear io; io_i = 1;
io(io_i) = linio([mdl, '/Dw'],              1, 'openinput');  io_i = io_i + 1;
io(io_i) = linio([mdl, '/u'],               1, 'openinput');  io_i = io_i + 1;
io(io_i) = linio([mdl, '/Inertial Sensor'], 1, 'openoutput'); io_i = io_i + 1;

G = linearize(mdl, io);
G.InputName  = {'Dwx', 'Dwy', 'Dwz', 'Rwx', 'Rwy', 'Rwz', ...
                'F1', 'F2', 'F3', 'F4', 'F5', 'F6'};
G.OutputName = {'Ax', 'Ay', 'Az', 'Arx', 'Ary', 'Arz'};

There are 24 states (6dof for the bottom platform + 6dof for the top platform). 

size(G)

State-space model with 6 outputs, 12 inputs, and 24 states.

% G = G*blkdiag(inv(J), eye(6));
% G.InputName  = {'Dw1', 'Dw2', 'Dw3', 'Dw4', 'Dw5', 'Dw6', ...
%                 'F1', 'F2', 'F3', 'F4', 'F5', 'F6'};

Thanks to the Jacobian, we compute the transfer functions in the frame of the legs and in an inertial frame. 

Gx = G*blkdiag(eye(6), inv(J'));
Gx.InputName  = {'Dwx', 'Dwy', 'Dwz', 'Rwx', 'Rwy', 'Rwz', ...
                 'Fx', 'Fy', 'Fz', 'Mx', 'My', 'Mz'};

Gl = J*G;
Gl.OutputName  = {'A1', 'A2', 'A3', 'A4', 'A5', 'A6'};

3.4 Obtained Dynamics

stewart_platform_translations.png

Figure 5: Stewart Platform Plant from forces applied by the legs to the acceleration of the platform

stewart_platform_rotations.png

Figure 6: Stewart Platform Plant from torques applied by the legs to the angular acceleration of the platform

stewart_platform_legs.png

Figure 7: Stewart Platform Plant from forces applied by the legs to displacement of the legs

stewart_platform_transmissibility.png

Figure 8: Transmissibility

3.5 Real Approximation of \(G\) at the decoupling frequency

Let’s compute a real approximation of the complex matrix \(H_1\) which corresponds to the the transfer function \(G_c(j\omega_c)\) from forces applied by the actuators to the measured acceleration of the top platform evaluated at the frequency \(\omega_c\). 

wc = 2*pi*20; % Decoupling frequency [rad/s]

Gc = G({'Ax', 'Ay', 'Az', 'Arx', 'Ary', 'Arz'}, ...
       {'F1', 'F2', 'F3', 'F4', 'F5', 'F6'}); % Transfer function to find a real approximation

H1 = evalfr(Gc, j*wc);

The real approximation is computed as follows: 

D = pinv(real(H1'*H1));
H1 = inv(D*real(H1'*diag(exp(j*angle(diag(H1*D*H1.'))/2))));

3.6 Verification of the decoupling using the “Gershgorin Radii”

First, the Singular Value Decomposition of \(H_1\) is performed: \[ H_1 = U \Sigma V^H \] 

[U,S,V] = svd(H1);

Then, the “Gershgorin Radii” is computed for the plant \(G_c(s)\) and the “SVD Decoupled Plant” \(G_d(s)\): \[ G_d(s) = U^T G_c(s) V \]

This is computed over the following frequencies. 

freqs = logspace(-2, 2, 1000); % [Hz]

Gershgorin Radii for the coupled plant: 

Gr_coupled = zeros(length(freqs), size(Gc,2));

H = abs(squeeze(freqresp(Gc, freqs, 'Hz')));
for out_i = 1:size(Gc,2)
    Gr_coupled(:, out_i) = squeeze((sum(H(out_i,:,:)) - H(out_i,out_i,:))./H(out_i, out_i, :));
end

Gershgorin Radii for the decoupled plant using SVD: 

Gd = U'*Gc*V;
Gr_decoupled = zeros(length(freqs), size(Gd,2));

H = abs(squeeze(freqresp(Gd, freqs, 'Hz')));
for out_i = 1:size(Gd,2)
    Gr_decoupled(:, out_i) = squeeze((sum(H(out_i,:,:)) - H(out_i,out_i,:))./H(out_i, out_i, :));
end

Gershgorin Radii for the decoupled plant using the Jacobian: 

Gj = Gc*inv(J');
Gr_jacobian = zeros(length(freqs), size(Gj,2));

H = abs(squeeze(freqresp(Gj, freqs, 'Hz')));

for out_i = 1:size(Gj,2)
    Gr_jacobian(:, out_i) = squeeze((sum(H(out_i,:,:)) - H(out_i,out_i,:))./H(out_i, out_i, :));
end

simscape_model_gershgorin_radii.png

Figure 9: Gershgorin Radii of the Coupled and Decoupled plants

3.7 Decoupled Plant

Let’s see the bode plot of the decoupled plant \(G_d(s)\). \[ G_d(s) = U^T G_c(s) V \]

simscape_model_decoupled_plant_svd.png

Figure 10: Decoupled Plant using SVD

simscape_model_decoupled_plant_jacobian.png

Figure 11: Decoupled Plant using the Jacobian

3.8 Diagonal Controller

The controller \(K\) is a diagonal controller consisting a low pass filters with a crossover frequency \(\omega_c\) and a DC gain \(C_g\). 

wc = 2*pi*0.1; % Crossover Frequency [rad/s]
C_g = 50; % DC Gain

K = eye(6)*C_g/(s+wc);

3.9 Centralized Control

The control diagram for the centralized control is shown below.

The controller \(K_c\) is “working” in an cartesian frame. The Jacobian is used to convert forces in the cartesian frame to forces applied by the actuators.

centralized_control.png 

G_cen = feedback(G, inv(J')*K, [7:12], [1:6]);

3.10 SVD Control

The SVD control architecture is shown below. The matrices \(U\) and \(V\) are used to decoupled the plant \(G\).

svd_control.png

SVD Control 

G_svd = feedback(G, pinv(V')*K*pinv(U), [7:12], [1:6]);

3.11 Results

Let’s first verify the stability of the closed-loop systems: 

isstable(G_cen)

ans =
  logical
   1

isstable(G_svd)

ans =
  logical
   1

The obtained transmissibility in Open-loop, for the centralized control as well as for the SVD control are shown in Figure 14.

stewart_platform_simscape_cl_transmissibility.png

Figure 14: Obtained Transmissibility

Author: Dehaeze Thomas

Created: 2020-10-05 lun. 18:06