Cleanup of Simulink with MATLAB GP

Simulink/polydome_matlab_gp/gpCallback.m

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+classdef gpCallback < casadi.Callback
+  properties
+    model
+  end
+  methods
+    function self = gpCallback(name)
+      self@casadi.Callback();
+      construct(self, name, struct('enable_fd', true));
+    end
+
+    % Number of inputs and outputs
+    function v=get_n_in(self)
+      v=1;
+    end
+    function v=get_n_out(self)
+      v=1;
+    end
+    % Function sparsity
+    function v=get_sparsity_in(self, i)
+      v=casadi.Sparsity.dense(7, 1);
+    end
+
+    % Initialize the object
+    function init(self)
+      disp('initializing gpCallback')
+      gpr = load('gpr_model.mat', 'model');
+      self.model = gpr.model;
+    end
+
+    % Evaluate numerically
+    function arg = eval(self, arg)
+      x = full(arg{1});
+      % Transpose x since gp predictor takes row by row, and casadi gives
+      % colum by column
+      [mean, ~] = predict(self.model, x');
+      arg = {mean};
+    end
+  end
+end

Simulink/polydome_matlab_gp/gp_mpc_system.m

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+classdef gp_mpc_system < matlab.System & matlab.system.mixin.Propagates
+    % untitled Add summary here
+    %
+    % This template includes the minimum set of functions required
+    % to define a System object with discrete state.
+
+    properties
+        % Control horizon
+        N = 0; 
+        % Time Step
+        TimeStep = 0;
+        % Max Electrical Power Consumption
+        Pel = 6300;
+
+    end
+
+    properties (DiscreteState)
+    end
+
+    properties (Access = private)
+        % Pre-computed constants.
+        casadi_solver
+        u_lags
+        y_lags
+        lbx
+        ubx
+    end
+
+    methods (Access = protected)
+        function num = getNumInputsImpl(~)
+            num = 2;
+        end
+        function num = getNumOutputsImpl(~)
+            num = 1;
+        end
+        function dt1 = getOutputDataTypeImpl(~)
+        	dt1 = 'double';
+        end
+        function [dt1, dt2] = getInputDataTypeImpl(~)
+        	dt1 = 'double';
+            dt2 = 'double';
+        end
+        function sz1 = getOutputSizeImpl(~)
+        	sz1 = 1;
+        end
+        function sz1 = getInputSizeImpl(~)
+        	sz1 = 1;
+        end
+        function cp1 = isInputComplexImpl(~)
+        	cp1 = false;
+        end
+        function cp1 = isOutputComplexImpl(~)
+        	cp1 = false;
+        end
+        function fz1 = isInputFixedSizeImpl(~)
+        	fz1 = true;
+        end
+        function fz1 = isOutputFixedSizeImpl(~)
+        	fz1 = true;
+        end
+        function setupImpl(obj,~,~)
+            % Implement tasks that need to be performed only once, 
+            % such as pre-computed constants.
+            addpath('/home/radu/Media/MATLAB/casadi-linux-matlabR2014b-v3.5.5')
+            import casadi.*
+            
+            % Initialize CasADi callback
+            cs_model = gpCallback('model');
+            
+            % Set up problem variables
+            T_set = 20;
+            n_states = 7;
+            
+            COP = 5; %cooling
+            EER = 5; %heating
+            
+            
+            obj.u_lags = [0];
+            obj.y_lags = [23 23 23];
+            
+            % Formulate the optimization problem
+            J = 0; % optimization objective
+            g = []; % constraints vector
+
+            % Set up the symbolic variables
+            U = MX.sym('U', obj.N, 1);
+            W = MX.sym('W', obj.N, 2);
+            x0 = MX.sym('x0', 1, n_states - 3);
+
+            % setup the first state
+            wk = W(1, :);
+            uk = U(1); % scaled input
+            xk = [wk, obj.Pel*uk, x0];
+            yk = cs_model(xk);
+            J = J + (yk - T_set).^2;
+            
+            % Setup the rest of the states
+            for idx = 2:obj.N
+                wk = W(idx, :); 
+                uk_1 = uk; uk = U(idx);
+                xk = [wk, obj.Pel*uk, obj.Pel*uk_1, yk, xk(5:6)];
+                yk = cs_model(xk);
+                J = J + (yk - T_set).^2;
+            end
+            
+            p = [vec(W); vec(x0)];
+            nlp_prob = struct('f', J, 'x', vec(U), 'g', g, 'p', p);
+            
+            
+            opts = struct;
+            %opts.ipopt.max_iter = 5000;
+            opts.ipopt.max_cpu_time = 15 * 60;
+            opts.ipopt.hessian_approximation = 'limited-memory';
+            %opts.ipopt.print_level =0;%0,3
+            opts.print_time = 0;
+            opts.ipopt.acceptable_tol =1e-8;
+            opts.ipopt.acceptable_obj_change_tol = 1e-6;
+            
+            solver = nlpsol('solver', 'ipopt', nlp_prob,opts);
+            
+            obj.casadi_solver = solver;
+            obj.lbx = -COP;
+            obj.ubx = EER;
+        end
+
+        function u = stepImpl(obj,x,w)        
+            import casadi.*
+            
+            %Update the y lags
+            obj.y_lags = [x, obj.y_lags(1:end-1)];
+            
+            
+            real_p = vertcat(vec(DM(w)), vec(DM([obj.u_lags obj.y_lags])));
+            disp("Starting optimization")
+            tic
+            res = obj.casadi_solver('p', real_p, 'ubx', obj.ubx, 'lbx', obj.lbx);
+            t = toc;
+            disp(t)
+            u = obj.Pel * full(res.x(1));
+            u = 15000 * (20 - x);
+            
+            % Update the u lags
+            obj.u_lags = [u, obj.u_lags(2:end-1)];
+            
+           
+            
+        end
+
+        function resetImpl(obj)
+            % Initialize discrete-state properties.
+        end
+    end
+end

Simulink/polydome_matlab_gp/polydome_params.mat

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+polydome_params.mat

Simulink/polydome_matlab_gp/weather_predictor.m

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+weather_predictor.m