Lotka Volterra fishing problem (ACADO) - Revision history

FelixMueller at 17:51, 31 January 2016

2016-01-31T17:51:15Z

← Older revision		Revision as of 17:51, 31 January 2016
Line 1:		Line 1:
	This pages states the code to solve the (ralaxed) Lotka Volterra fishing problem with the code generation fo the ACADO Toolkit from Matlab (via Matlab interface). Hence one needs Matlab and ACADO.		This pages states the code to solve the (ralaxed) [[Lotka Volterra fishing problem]] with the code generation fo the ACADO Toolkit from Matlab (via Matlab interface). Hence one needs Matlab and ACADO.
	The m-file should be located in the matlab interface of ACADO and the path <ACADOROOT>/interfaces/matlab/examples/codegeneratio/nmpc/. While we use the NMPC more precisely the RTI capability of		The m-file should be located in the matlab interface of ACADO and the path <ACADOROOT>/interfaces/matlab/examples/codegeneratio/nmpc/. While we use the NMPC more precisely the RTI capability of
	the ACADO codegeneration, this features are misused to implement a 20 step SQP solver without stopping criteria.		the ACADO codegeneration, this features are misused to implement a 20 step SQP solver without stopping criteria.

TobiasWeber at 10:21, 28 January 2016

2016-01-28T10:21:08Z

← Older revision		Revision as of 10:21, 28 January 2016
Line 131:		Line 131:

	</source>		</source>
			[[Category:ACADO]]

TobiasWeber at 09:22, 20 January 2016

2016-01-20T09:22:41Z

← Older revision		Revision as of 09:22, 20 January 2016
Line 3:		Line 3:
	the ACADO codegeneration, this features are misused to implement a 20 step SQP solver without stopping criteria.		the ACADO codegeneration, this features are misused to implement a 20 step SQP solver without stopping criteria.

	== ACADO ==		== ACADO (Matlab) ==

	The source code for a fixed grid discretisation with fixed stepsize integrator.		The source code for a fixed grid discretisation with fixed stepsize integrator. This is an m-file that should be called from Matlab AFTER
			calling make from matlab in the matlab interface of ACADO.

	<source lang="Matlab">		<source lang="Matlab">

TobiasWeber at 09:20, 20 January 2016

2016-01-20T09:20:55Z

← Older revision		Revision as of 09:20, 20 January 2016
Line 1:		Line 1:
	This pages states the code to solve the (ralaxed) Lotka Volterra fishing problem with the code generation fo the ACADO Toolkit from Matlab (via Matlab interface). Hence one needs Matlab and ACADO.		This pages states the code to solve the (ralaxed) Lotka Volterra fishing problem with the code generation fo the ACADO Toolkit from Matlab (via Matlab interface). Hence one needs Matlab and ACADO.
	The m-file should be located in the matlab interface of ACADO and the path <ACADOROOT>/interfaces/matlab/examples/codegeneratio/nmpc/. While we use the NMPC more precisely the RTI capability of		The m-file should be located in the matlab interface of ACADO and the path <ACADOROOT>/interfaces/matlab/examples/codegeneratio/nmpc/. While we use the NMPC more precisely the RTI capability of
	the ACADO ~~Code generation~~, this features are misused to implement a 20 step SQP solver without stopping criteria.		the ACADO codegeneration, this features are misused to implement a 20 step SQP solver without stopping criteria.

	== ACADO ==		== ACADO ==

TobiasWeber: Created page with "This pages states the code to solve the (ralaxed) Lotka Volterra fishing problem with the code generation fo the ACADO Toolkit from Matlab (via Matlab interface). Hence one ne..."

2016-01-20T09:20:34Z

Created page with "This pages states the code to solve the (ralaxed) Lotka Volterra fishing problem with the code generation fo the ACADO Toolkit from Matlab (via Matlab interface). Hence one ne..."

New page

This pages states the code to solve the (ralaxed) Lotka Volterra fishing problem with the code generation fo the ACADO Toolkit from Matlab (via Matlab interface). Hence one needs Matlab and ACADO.
The m-file should be located in the matlab interface of ACADO and the path <ACADOROOT>/interfaces/matlab/examples/codegeneratio/nmpc/. While we use the NMPC more precisely the RTI capability of
the ACADO Code generation, this features are misused to implement a 20 step SQP solver without stopping criteria.

== ACADO ==

The source code for a fixed grid discretisation with fixed stepsize integrator.

<source lang="Matlab">

% implements the lotka volterra fishing problem on mintoc.de

clc;
clear all;
close all;

Ts = 0.1;
EXPORT = 1;

%% Variables
DifferentialState x0 x1;
Control w;

n_XD = length(diffStates);
n_U = length(controls);

% Constants
c0 = 0.4;
c1 = 0.2;

%% Differential Equation

f = dot([x0; x1]) == [ x0-x0*x1-c0*x0*w; ...
-x1 + x0*x1-c1*x1*w];

h = [diffStates];
hN = [diffStates];

%% MPCexport
acadoSet('problemname', 'mpc');

N = 200;
ocp = acado.OCP( 0.0, 12, N );

W_mat = eye(n_XD,n_XD);
WN_mat = eye(n_XD,n_XD);
W = acado.BMatrix(W_mat);
WN = acado.BMatrix(WN_mat);

ocp.minimizeLSQ( W, h );
ocp.minimizeLSQEndTerm( WN, hN );

ocp.subjectTo( 0 <= w <= 1 );
ocp.setModel(f);

mpc = acado.OCPexport( ocp );
mpc.set( 'HESSIAN_APPROXIMATION', 'GAUSS_NEWTON' );
mpc.set( 'DISCRETIZATION_TYPE', 'MULTIPLE_SHOOTING' );
mpc.set( 'SPARSE_QP_SOLUTION', 'FULL_CONDENSING_N2');
mpc.set( 'INTEGRATOR_TYPE', 'INT_IRK_GL4' );
mpc.set( 'NUM_INTEGRATOR_STEPS', N );
mpc.set( 'QP_SOLVER', 'QP_QPOASES' );
mpc.set( 'HOTSTART_QP', 'NO' );
mpc.set( 'LEVENBERG_MARQUARDT', 1e-10 );

if EXPORT
mpc.exportCode( 'export_MPC' );
copyfile('../../../../../../external_packages/qpoases', 'export_MPC/qpoases')

cd export_MPC
make_acado_solver('../acado_MPCstep')
cd ..
end

%% PARAMETERS OPTIMIZATION
X0 = [0.5 0.7];
input.x0=X0';
Xref = [1 1];
input.x = repmat(Xref,N+1,1);
Xref = repmat(Xref,N,1);
input.od = [];

Uref = zeros(N,n_U);
input.u = Uref;

input.y = [Xref(1:N,:)];
input.yN = Xref(N,:).';

input.W = diag([1 1]);
input.WN = diag([1 1]);

%% SOLVER LOOP
display('------------------------------------------------------------------')
display(' SOLVER Loop' )
display('------------------------------------------------------------------')

for i=1:20
tic
% Solve NMPC OCP
output = acado_MPCstep(input);

input.x=output.x;
input.u=output.u;

disp([' (RTI step: ' num2str(output.info.cpuTime*1e6) ' µs)'])

end

%% PLOT RESULTS

t_end = 12;

% States
figure(1)
plot([0:t_end/N:t_end],output.x)
ylabel('States')
xlabel('Time')
legend('x1','x2')

% Control
figure(2)
plot([0:t_end/N:t_end-t_end/N],output.u)
ylabel('Control (u)')
xlabel('Time')

</source>