1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Copyright (c) 2023, Amon Lahr, Simon Muntwiler, Antoine Leeman & Fabian Flürenbrock Institute for Dynamic Systems and Control, ETH Zurich.
%
% All rights reserved.
%
% 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
end
methods
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 ...
];
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
tic;
[optimizer_out,errorcode,~] = obj.yalmip_optimizer{x};
solvetime = toc;
% 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
|
