150 lines
4.2 KiB
Matlab
150 lines
4.2 KiB
Matlab
classdef gp_mpc_system < matlab.System & matlab.system.mixin.Propagates
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% untitled Add summary here
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%
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% This template includes the minimum set of functions required
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% to define a System object with discrete state.
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properties
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% Control horizon
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N = 0;
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% Time Step
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TimeStep = 0;
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% Max Electrical Power Consumption
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Pel = 6300;
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end
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properties (DiscreteState)
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end
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properties (Access = private)
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% Pre-computed constants.
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casadi_solver
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u_lags
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y_lags
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lbx
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ubx
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end
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methods (Access = protected)
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function num = getNumInputsImpl(~)
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num = 2;
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end
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function num = getNumOutputsImpl(~)
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num = 1;
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end
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function dt1 = getOutputDataTypeImpl(~)
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dt1 = 'double';
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end
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function [dt1, dt2] = getInputDataTypeImpl(~)
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dt1 = 'double';
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dt2 = 'double';
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end
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function sz1 = getOutputSizeImpl(~)
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sz1 = 1;
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end
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function sz1 = getInputSizeImpl(~)
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sz1 = 1;
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end
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function cp1 = isInputComplexImpl(~)
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cp1 = false;
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end
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function cp1 = isOutputComplexImpl(~)
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cp1 = false;
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end
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function fz1 = isInputFixedSizeImpl(~)
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fz1 = true;
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end
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function fz1 = isOutputFixedSizeImpl(~)
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fz1 = true;
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end
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function setupImpl(obj,~,~)
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% Implement tasks that need to be performed only once,
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% such as pre-computed constants.
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addpath('/home/radu/Media/MATLAB/casadi-linux-matlabR2014b-v3.5.5')
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import casadi.*
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% Initialize CasADi callback
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cs_model = gpCallback('model');
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% Set up problem variables
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T_set = 20;
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n_states = 7;
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COP = 5; %cooling
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EER = 5; %heating
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obj.u_lags = [0];
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obj.y_lags = [23 23 23];
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% Formulate the optimization problem
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J = 0; % optimization objective
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g = []; % constraints vector
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% Set up the symbolic variables
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U = MX.sym('U', obj.N, 1);
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W = MX.sym('W', obj.N, 2);
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x0 = MX.sym('x0', 1, n_states - 3);
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% setup the first state
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wk = W(1, :);
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uk = U(1); % scaled input
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xk = [wk, obj.Pel*uk, x0];
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yk = cs_model(xk);
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J = J + (yk - T_set).^2;
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% Setup the rest of the states
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for idx = 2:obj.N
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wk = W(idx, :);
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uk_1 = uk; uk = U(idx);
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xk = [wk, obj.Pel*uk, obj.Pel*uk_1, yk, xk(5:6)];
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yk = cs_model(xk);
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J = J + (yk - T_set).^2;
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end
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p = [vec(W); vec(x0)];
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nlp_prob = struct('f', J, 'x', vec(U), 'g', g, 'p', p);
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opts = struct;
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%opts.ipopt.max_iter = 5000;
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opts.ipopt.max_cpu_time = 15 * 60;
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opts.ipopt.hessian_approximation = 'limited-memory';
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%opts.ipopt.print_level =0;%0,3
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opts.print_time = 0;
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opts.ipopt.acceptable_tol =1e-8;
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opts.ipopt.acceptable_obj_change_tol = 1e-6;
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solver = nlpsol('solver', 'ipopt', nlp_prob,opts);
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obj.casadi_solver = solver;
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obj.lbx = -COP;
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obj.ubx = EER;
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end
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function u = stepImpl(obj,x,w)
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import casadi.*
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%Update the y lags
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obj.y_lags = [x, obj.y_lags(1:end-1)];
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real_p = vertcat(vec(DM(w)), vec(DM([obj.u_lags obj.y_lags])));
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disp("Starting optimization")
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tic
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%res = obj.casadi_solver('p', real_p, 'ubx', obj.ubx, 'lbx', obj.lbx);
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t = toc;
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disp(t)
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u = obj.Pel * full(res.x(1));
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% Update the u lags
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obj.u_lags = [u, obj.u_lags(2:end-1)];
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end
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function resetImpl(obj)
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% Initialize discrete-state properties.
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end
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end
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end
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