From ce14ae31fcb13362ff341e54ace5dccc4f94c095 Mon Sep 17 00:00:00 2001
From: Yuan Xu <yuanxu@student.ethz.ch>
Date: Sun, 7 May 2023 21:10:45 +0200
Subject: update 15,16 and modify generate_constraints.m

---
 templates/MPC.m | 89 +++++++++++++++++++++++++++++++++++++++++++++++++++------
 1 file changed, 80 insertions(+), 9 deletions(-)

(limited to 'templates/MPC.m')

diff --git a/templates/MPC.m b/templates/MPC.m
index 3e9d2f1..b2d411d 100644
--- a/templates/MPC.m
+++ b/templates/MPC.m
@@ -6,6 +6,44 @@
 % Please see the LICENSE file that has been included as part of this package.
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
+% classdef MPC
+%     properties
+%         yalmip_optimizer
+%     end
+% 
+%     methods
+%         function obj = MPC(Q,R,N,params)
+%             nu = params.model.nu;
+%             nx = params.model.nx;
+% 
+%             % define optimization variables
+%             U = sdpvar(repmat(nu,1,N),ones(1,N),'full');
+%             X0 = sdpvar(nx,1,'full');
+% 
+%             % YOUR CODE HERE
+%             
+%             opts = sdpsettings('verbose',1,'solver','quadprog');
+%             obj.yalmip_optimizer = optimizer(constraints,objective,opts,X0,{U{1} objective});
+%         end
+% 
+%         function [u, ctrl_info] = eval(obj,x)
+%             %% evaluate control action by solving MPC problem, e.g.
+%             tic;
+%             [optimizer_out,errorcode] = obj.yalmip_optimizer(x);
+%             solvetime = toc;
+%             
+%             [u, objective] = optimizer_out{:};
+% 
+%             feasible = true;
+%             if (errorcode ~= 0)
+%                 feasible = false;
+%             end
+% 
+%             ctrl_info = struct('ctrl_feas',feasible,'objective',objective,'solvetime',solvetime);
+%         end
+%     end
+% end
+
 classdef MPC
     properties
         yalmip_optimizer
@@ -15,31 +53,64 @@ classdef MPC
         function obj = MPC(Q,R,N,params)
             nu = params.model.nu;
             nx = params.model.nx;
-
+            % YOUR CODE HERE
             % define optimization variables
+            A=params.model.A;
+            B=params.model.B;
             U = sdpvar(repmat(nu,1,N),ones(1,N),'full');
+            X = sdpvar(repmat(nx,1,N+1),ones(1,N+1),'full');
+
+            [K,P,~] = dlqr(A,B,Q,R);
+
+            % define constraints
+%             s_max=params.constraints.MaxAbsPositionXZ;
+%             y_max=params.constraints.MaxAbsPositionY;
+%             u_max = params.constraints.MaxAbsThrust;
+            H_x = params.constraints.StateMatrix;
+            h_x = params.constraints.StateRHS;
+            H_u = params.constraints.InputMatrix;
+            h_u = params.constraints.InputRHS;
             X0 = sdpvar(nx,1,'full');
+            objective = 0;
+            constraints = X{1} == X0;
+            for k = 1:N
+                constraints = [ ...
+                    constraints, ...
+                    X{k+1} == A*X{k} + B*U{k} , ...
+                    H_x * X{k} <= h_x, ...
+                    H_u * U{k} <= h_u ...
+                ];
 
-            % YOUR CODE HERE
-            
+                objective = objective + X{k}'*Q*X{k} + U{k}'*R*U{k};
+            end
+            objective=objective+X{N+1}'*P*X{N+1};
+            % terminal constraint
+%             constraints = [ ...
+%                 constraints, ...
+%                 X{N+1} == zeros(nx,1)
+%             ];
             opts = sdpsettings('verbose',1,'solver','quadprog');
             obj.yalmip_optimizer = optimizer(constraints,objective,opts,X0,{U{1} objective});
         end
 
         function [u, ctrl_info] = eval(obj,x)
-            %% evaluate control action by solving MPC problem, e.g.
+            % evaluate control action by solving MPC problem
             tic;
-            [optimizer_out,errorcode] = obj.yalmip_optimizer(x);
+            [optimizer_out,errorcode,~] = obj.yalmip_optimizer{x};
             solvetime = toc;
-            
-            [u, objective] = optimizer_out{:};
 
-            feasible = true;
+            % extract optimal control action and objective function value
+            u = optimizer_out{1};
+            objective = optimizer_out{2};
+
+            % check feasibility of optimization problem
+            feasible = ~isnan(objective) && ~isinf(objective);
             if (errorcode ~= 0)
                 feasible = false;
             end
 
+            % create control info struct
             ctrl_info = struct('ctrl_feas',feasible,'objective',objective,'solvetime',solvetime);
         end
     end
-end
\ No newline at end of file
+end
-- 
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