86 lines
3.3 KiB
Matlab
86 lines
3.3 KiB
Matlab
function robot = estimate_dyn(robot,opt)
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% -------------------------------------------------------------------
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% Get datas
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% ------------------------------------------------------------------------
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time = 0:0.01:2;
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f=1;
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q_J = sin(2*pi*f*time);
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qd_J = (2*pi*f)*cos(2*pi*f*time);
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qdd_J = -(2*pi*f)^2*sin(2*pi*f*time);
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q=[q_J;-q_J];
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qd=[qd_J; -qd_J];
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qdd=[qdd_J; -qdd_J];
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g = [0; 0; -9.8];
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tau = zeros([2,101]);
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% pi -> [m;mc;I] 10 element
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robot_pi1=[1;1/2;0;0;1+1/4;0;0;1+1/4;0;1+1/4];
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robot_pi2=[2;1;0;0;1+1/4;0;0;1+1/4;0;1+1/4];
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robot_pi=[robot_pi1;robot_pi2];
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for i = 1:length(q_J)
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regressor = standard_regressor_Two_bar(q(:,i),qd(:,i),qdd(:,i));
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tau(:,i)=regressor*robot_pi;
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end
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idntfcnTrjctry.t = time;
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idntfcnTrjctry.q = q;
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idntfcnTrjctry.qd = qd;
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idntfcnTrjctry.qdd = qdd;
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idntfcnTrjctry.tau = tau;
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% -------------------------------------------------------------------
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% Generate Regressors based on data
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% ------------------------------------------------------------------------
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drvGains = [];
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baseQR = robot.baseQR;
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[Tau, Wb] = buildObservationMatrices(idntfcnTrjctry, baseQR, drvGains);
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% ---------------------------------------------------------------------
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% Estimate parameters
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% ---------------------------------------------------------------------
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sol = struct;
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method = 'OLS';
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if strcmp(method, 'OLS')
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% Usual least squares
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[sol.pi_b, sol.pi_fr] = ordinaryLeastSquareEstimation(Tau, Wb);
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elseif strcmp(method, 'PC-OLS')
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% Physically consistent OLS using SDP optimization
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[sol.pi_b, sol.pi_fr, sol.pi_s] = physicallyConsistentEstimation(Tau, Wb, baseQR);
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else
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error("Chosen method for dynamic parameter estimation does not exist");
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end
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robot.sol = sol;
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% Local unctions
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function [Tau, Wb] = buildObservationMatrices(idntfcnTrjctry, baseQR, drvGains)
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% The function builds observation matrix for UR10E
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E1 = baseQR.permutationMatrix(:,1:baseQR.numberOfBaseParameters);
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Wb = []; Tau = [];
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for i = 1:1:length(idntfcnTrjctry.t)
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% Yi = regressorWithMotorDynamics(idntfcnTrjctry.q(i,:)',...
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% idntfcnTrjctry.qd(i,:)',...
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% idntfcnTrjctry.q2d(i,:)');
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% Yfrctni = frictionRegressor(idntfcnTrjctry.qd_fltrd(i,:)');
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% Ybi = [Yi*E1, Yfrctni];
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base_regressor_func = sprintf('base_regressor_%s',opt.robotName);
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Yb = feval(base_regressor_func, idntfcnTrjctry.q(:,i), ...
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idntfcnTrjctry.qd(:,i),idntfcnTrjctry.qdd(:,i));
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Wb = vertcat(Wb, Yb);
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% Tau = vertcat(Tau, diag(drvGains)*idntfcnTrjctry.i_fltrd(i,:)');
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Tau = vertcat(Tau, idntfcnTrjctry.tau(:,i));
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end
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end
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function [pib_OLS, pifrctn_OLS] = ordinaryLeastSquareEstimation(Tau, Wb)
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% Function perfroms ordinary least squares estimation of parameters
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% pi_OLS = (Wb'*Wb)\(Wb'*Tau);
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% pib_OLS = pi_OLS(1:40); % variables for base paramters
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% pifrctn_OLS = pi_OLS(41:end);
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pib_OLS=pinv(Wb)*Tau;
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pifrctn_OLS = 0;
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end
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function [pib_OLS, pifrctn_OLS] = MultiLeastSquareEstimation(idntfcnTrjctry, Tau, Wb)
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% Function perfroms Multi step ordinary least squares estimation of parameters
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pib_OLS=pinv(Wb)*Tau;
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pifrctn_OLS = 0;
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end
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end |