lambert_algorithm/main.m at master · Byunk/lambert_algorithm · GitHub

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
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
clear variables; close all; clc;

%% Input

%'Gauss', 'Battin', 'Analytic_Gradient'
% method = 'Gauss';

%% Test
lambda = [-0.9, -0.7, -0.5, -0.3, -0.1, 0.0, ...
    0.1, 0.3, 0.5, 0.7, 0.9];
T = [0.3, 0.5, 0.7, 0.9, 1.0, 3.0, 5.0, 7.0, 9.0, 11.0];

Re = 6.3781*10^3;
muC = 3.986*10^5;
r1 = [Re, Re, Re, 10*Re, Re];
r2 = [Re, Re/2, 2*Re, 10*Re, 10*Re];
phi = [5, 30, 55, 80, 105, 130, 155, 180, 205, 230, 255, 280, 305, 330, 355];
tf = [50, 100, 200, 400, 800, 1600, 3200, 6400, 12800, 25600, 51200];

%% Gauss method
result = zeros(length(lambda), length(T));
for i = 1:length(T)
    column = [];
    for j = 1:length(lambda)
        fprintf("Gauss method for T = %f, lambda = %f\n", ...
            T(i), lambda(j));
        try
            [~, iter] = lambert_gauss(T(i), lambda(j));
            result(j, i) = iter;
        catch
            result(j, i) = "err";
        end
    end
end

result_table = array2table(result, "VariableNames", ...
    string(T), "RowNames", string(lambda));
file_name = strcat("Gauss", ".csv");
writetable(result_table, file_name);
disp(result_table);

%% Battin method (Free Parameter)
result = zeros(length(lambda), length(T));
for i = 1:length(T)
    column = [];
    for j = 1:length(lambda)
        fprintf("Battin method for T = %f, lambda = %f\n", ...
            T(i), lambda(j));
        try
            [~, iter] = lambert_battin(T(i), lambda(j));
            result(j, i) = iter;
        catch
            result(j, i) = "err";
        end
    end
end

result_table = array2table(result, "VariableNames", ...
    string(T), "RowNames", string(lambda));
file_name = strcat("Battin", ".csv");
writetable(result_table, file_name);
disp(result_table);

%% Battin method (Iterations near 360 degrees)
lambda_for_iter = -0.99:0.01:-0.90;
T_for_iter = 1:2:11;

result = zeros(length(lambda_for_iter), length(T_for_iter));
for i = 1:length(T_for_iter)
    for j = 1:length(lambda_for_iter)
        fprintf("Battin method for T = %f, lambda = %f\n", ...
            T_for_iter(i), lambda_for_iter(j));
        try
            [~, iter] = lambert_battin(T_for_iter(i), ...
                lambda_for_iter(j));
            result(j, i) = iter;
        catch
            result(j, i) = "err";
        end
    end
end

result_table = array2table(result, "VariableNames", ...
    string(T_for_iter), "RowNames", string(lambda_for_iter));
disp(result_table);

%% Analytic Gradient method
test_titles = [
    "ReRe", ...
    "ReRe_2", ...
    "Re2Re", ...
    "10ReRe", ...
    "Re10Re"
    ];

for i = 1:length(r1)
    result_row = zeros(length(phi), length(tf));
    for j = 1:length(phi)
        for k = 1:length(tf)
%             fprintf("Analytic Gradient method for phi = %d, " + ...
%                 "tf = %d\n", phi(j), tf(k));
            try
                [~, iter] = lambert_analytic_gradient( ...
                    r1(i), r2(i), phi(j), tf(k), muC);
                result_row(j, k) = iter;
            catch
                result_row(j, k) = "err";
            end
        end
    end
    result_table = array2table(result_row, "VariableNames", ...
        string(tf), "RowNames", string(phi));
    disp(result_table);
    file_name = strcat("AG", test_titles(i), ".csv");
    writetable(result_table, file_name);
end

%% Comparison (Above 2 proposed methods)
phi = [5, 30, 55, 80, 105, 130, 155, 180, 205, 230, ...
    255, 280, 305, 330, 355];
tf = [50, 100, 200, 400, 800, 1600, 3200, 6400, 12800, 25600, 51200];

% Re, Re
result_row1 = zeros(length(phi), length(tf));
result_row2 = zeros(length(phi), length(tf));
for i=1:length(phi)
    lambda = cos(0.5*deg2rad(phi(i)));
    s = sqrt(2*Re^2*(1-cos(phi(i))));

    for j = 1:length(tf)
        T = sqrt(8*muC/s^3)*tf(j);
        try
            [~, iter1] = lambert_analytic_gradient( ...
                Re, Re, phi(i), tf(j), muC);
        catch
            iter1 = Inf;
        end

        try
            [~, iter2] = lambert_battin(T, ...
                lambda);
        catch
            iter2 = Inf;
        end
        fprintf("iter1: %d, iter2: %d\n", iter1, iter2);
        result_row1(i, j) = iter2;
        result_row2(i, j) = iter1;
    end
end

result_table1 = array2table(result_row1, "VariableNames", ...
        string(tf), "RowNames", string(phi));
result_table2 = array2table(result_row2, "VariableNames", ...
        string(tf), "RowNames", string(phi));
disp(result_table1);
disp(result_table2);