184 lines
9.6 KiB
Matlab
184 lines
9.6 KiB
Matlab
function debugPlot(robot, benchmarkSettings, experimentDataStruct, Beta_0, estimatedBeta, options, expNb, algName, Betas, it, varargin)
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% Authors: Quentin Leboutet, Julien Roux, Alexandre Janot and Gordon Cheng
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%
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% Generate a set of debug plots allowing to visualize the relevance of a
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% given set of parameters along a given trajectory.
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if nargin < 9
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Betas = estimatedBeta;
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it = 0;
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end
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if options.debug == true
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[estimatedBeta benchmarkSettings.Beta_obj]
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options.filter = 'butterworth';
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[~, Tau, Qpp, Qp, Q, ~, ~, ~, ~, ~, ~] = getFilteredData(robot, benchmarkSettings, experimentDataStruct, options, expNb, algName);
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jointNb = 3;
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tau = Tau(jointNb,:)';
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q = Q(jointNb,:)';
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qp = Qp(jointNb,:)';
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qpp = Qpp(jointNb,:)';
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% Simulation of the robot using the computed parameters:
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t_control = linspace(benchmarkSettings.t_i, benchmarkSettings.t_f, benchmarkSettings.nbCtrlSamples); % Control epochs
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augmentedDesiredState = benchmarkSettings.trajectoryData.getTrajectoryData(t_control, benchmarkSettings.interpolationAlgorithm); % augmentedState = [Qpp; Qp; Q];
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% Simulation of the robot:
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if strcmp(benchmarkSettings.codeImplementation,'optim')
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% Simulation of the robot using the current parameters:
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if Beta_0 ~= estimatedBeta % In case the algorithm requires an initial estimate (e.g. CLIE, CLOE, IV, DIDIM...)
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[~, State_0, ~] = integrateClosedLoopDynamics_mex(augmentedDesiredState, Beta_0, robot.name, robot.numericalParameters.Geometry, ...
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robot.numericalParameters.Gravity, benchmarkSettings.t_i, benchmarkSettings.t_f, benchmarkSettings.nbCtrlSamples, benchmarkSettings.nbSamples, ...
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robot.controlParameters.Kp, robot.controlParameters.Ki, robot.controlParameters.Kd, robot.controlParameters.Ktau, robot.controlParameters.antiWindup, ...
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robot.physicalConstraints.limQ_L, robot.physicalConstraints.limQ_U, robot.physicalConstraints.limQp_L, robot.physicalConstraints.limQp_U, ...
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robot.physicalConstraints.limQpp_L, robot.physicalConstraints.limQpp_U, robot.physicalConstraints.limTau_L, robot.physicalConstraints.limTau_U, benchmarkSettings.integrationAlgorithm);
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end
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[~, State_it, ~] = integrateClosedLoopDynamics_mex(augmentedDesiredState, estimatedBeta, robot.name, robot.numericalParameters.Geometry, ...
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robot.numericalParameters.Gravity, benchmarkSettings.t_i, benchmarkSettings.t_f, benchmarkSettings.nbCtrlSamples, benchmarkSettings.nbSamples, ...
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robot.controlParameters.Kp, robot.controlParameters.Ki, robot.controlParameters.Kd, robot.controlParameters.Ktau, robot.controlParameters.antiWindup, ...
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robot.physicalConstraints.limQ_L, robot.physicalConstraints.limQ_U, robot.physicalConstraints.limQp_L, robot.physicalConstraints.limQp_U, ...
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robot.physicalConstraints.limQpp_L, robot.physicalConstraints.limQpp_U, robot.physicalConstraints.limTau_L, robot.physicalConstraints.limTau_U, benchmarkSettings.integrationAlgorithm);
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elseif strcmp(benchmarkSettings.codeImplementation,'classic')
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% Simulation of the robot using the current parameters:
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if Beta_0 ~= estimatedBeta % In case the algorithm requires an initial estimate (e.g. CLIE, CLOE, IV, DIDIM...)
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[~, State_0, ~] = integrateClosedLoopDynamics(augmentedDesiredState, Beta_0, robot.name, robot.numericalParameters.Geometry, ...
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robot.numericalParameters.Gravity, benchmarkSettings.t_i, benchmarkSettings.t_f, benchmarkSettings.nbCtrlSamples, benchmarkSettings.nbSamples, ...
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robot.controlParameters.Kp, robot.controlParameters.Ki, robot.controlParameters.Kd, robot.controlParameters.Ktau, robot.controlParameters.antiWindup, ...
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robot.physicalConstraints.limQ_L, robot.physicalConstraints.limQ_U, robot.physicalConstraints.limQp_L, robot.physicalConstraints.limQp_U, ...
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robot.physicalConstraints.limQpp_L, robot.physicalConstraints.limQpp_U, robot.physicalConstraints.limTau_L, robot.physicalConstraints.limTau_U, benchmarkSettings.integrationAlgorithm);
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end
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[~, State_it, ~] = integrateClosedLoopDynamics(augmentedDesiredState, estimatedBeta, robot.name, robot.numericalParameters.Geometry, ...
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robot.numericalParameters.Gravity, benchmarkSettings.t_i, benchmarkSettings.t_f, benchmarkSettings.nbCtrlSamples, benchmarkSettings.nbSamples, ...
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robot.controlParameters.Kp, robot.controlParameters.Ki, robot.controlParameters.Kd, robot.controlParameters.Ktau, robot.controlParameters.antiWindup, ...
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robot.physicalConstraints.limQ_L, robot.physicalConstraints.limQ_U, robot.physicalConstraints.limQp_L, robot.physicalConstraints.limQp_U, ...
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robot.physicalConstraints.limQpp_L, robot.physicalConstraints.limQpp_U, robot.physicalConstraints.limTau_L, robot.physicalConstraints.limTau_U, benchmarkSettings.integrationAlgorithm);
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else
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error('%s: unknown implementation option', algName);
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end
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% Compute the observation matrix:
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W = observationMatrix(robot.name, robot.paramVectorSize, robot.numericalParameters.Geometry, robot.numericalParameters.Gravity, Q, Qp, Qpp);
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referenceTau = W*benchmarkSettings.Beta_obj;
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recomputedTau = W*estimatedBeta;
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recomputedTau0 = W*Beta_0;
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figure('Name',sprintf('Torque %s',options.debugPlot));
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plot1 = plot(tau,'b','LineWidth',0.5);
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hold on
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plot(referenceTau(jointNb:robot.nbDOF:end),'g', 'LineWidth',2);
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hold on
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plot(recomputedTau0(jointNb:robot.nbDOF:end),'r--','LineWidth',1);
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hold on
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plot(recomputedTau(jointNb:robot.nbDOF:end),'m','LineWidth',1);
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legend({'real noisy signal', 'reference parameters', 'initial parameters',sprintf('%s parameters',algName)})
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plot1(1,1).Color(4)=0.25;
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xlabel('Samples');
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ylabel('Tau');
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title('Recomputed torque')
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grid on
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grid minor
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if Beta_0 ~= estimatedBeta % In case the algorithm requires an initial estimate (e.g. CLIE, CLOE, IV, DIDIM...), plot both the initial estimate and the final estimate trajectories.
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Err=(q-State_it(robot.nbDOF+jointNb,benchmarkSettings.samplingBorder+1:end-benchmarkSettings.samplingBorder)');
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Err0=(q-State_0(robot.nbDOF+jointNb,benchmarkSettings.samplingBorder+1:end-benchmarkSettings.samplingBorder)');
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figure('Name',sprintf('%s',options.debugPlot));
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subplot(4,1,1)
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plot(State_0(robot.nbDOF+jointNb,benchmarkSettings.samplingBorder+1:end-benchmarkSettings.samplingBorder)','--','LineWidth',2)
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hold on
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plot(q,'LineWidth',1)
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legend(sprintf('q%s simulated',jointNb),sprintf('q%s robot',jointNb))
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xlabel('Samples');
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ylabel('Q');
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title('Trajectory initial parameter set')
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grid on
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grid minor
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subplot(4,1,2)
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plot(100*Err0)
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legend('100 x Error')
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xlabel('Samples');
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ylabel('Joint Error');
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title('Trajectory Error initial parameter set')
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grid on
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grid minor
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subplot(4,1,3)
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plot(State_it(robot.nbDOF+jointNb,benchmarkSettings.samplingBorder+1:end-benchmarkSettings.samplingBorder)','--','LineWidth',2)
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hold on
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plot(q,'LineWidth',1)
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legend(sprintf('q%s simulated',jointNb),sprintf('q%s robot',jointNb))
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xlabel('Samples');
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ylabel('Q');
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title(sprintf('Trajectory %s',algName))
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grid on
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grid minor
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subplot(4,1,4)
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plot(100*Err)
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legend('100 x Error')
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xlabel('Samples');
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ylabel('Joint Error');
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title(sprintf('Trajectory Error %s',algName))
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grid on
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grid minor
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else % Otherwise just plot both the final estimate trajectory...
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Err=(q-State_it(robot.nbDOF+jointNb,benchmarkSettings.samplingBorder+1:end-benchmarkSettings.samplingBorder)');
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figure('Name',sprintf('%s',algName));
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subplot(2,1,1)
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plot(State_it(robot.nbDOF+jointNb,benchmarkSettings.samplingBorder+1:end-benchmarkSettings.samplingBorder)','--','LineWidth',2)
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hold on
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plot(q,'LineWidth',1)
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legend(sprintf('q%s simulated',jointNb),sprintf('q%s robot',jointNb))
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xlabel('Samples');
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ylabel('Q');
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title(sprintf('Trajectory %s', algName))
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grid on
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grid minor
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subplot(2,1,2)
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plot(100*Err)
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legend('100 x Error')
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xlabel('Samples');
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ylabel('Joint Error');
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title(sprintf('Trajectory Error %s',algName))
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grid on
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grid minor
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end
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if strcmp(algName,'ANN') || strcmp(algName,'HTRNN')
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if options.debug == true
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cc=parula(robot.paramVectorSize);
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figure('Name',sprintf('%s',options.debugPlot));
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for i=1:robot.paramVectorSize
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plot(Betas(i,:)','LineWidth',2 ,'color',cc(i,:))
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hold on
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plot(repmat(benchmarkSettings.Beta_obj(i),1,it-1)','--','LineWidth',2,'color',cc(i,:))
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end
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hold off
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legend('Identified Parameters','True Parameters')
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xlabel('Iterations');
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ylabel('Beta');
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title(sprintf('Parameter Convergence of %s Parameter Estimation',algName))
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ylim([-5,5])
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grid on
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grid minor
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err = Betas-repmat(benchmarkSettings.Beta_obj,1,it-1);
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err2 = sum(err.^2,1);
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figure('Name','Convergence');
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plot(err2,'LineWidth',2 )
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legend('Squared Error')
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xlabel('Iterations');
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ylabel('Error');
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title(sprintf('Convergence of %s Parameter Estimation',algName))
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grid on
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grid minor
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end
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end
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pause
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end
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end
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