function res = test_linearSysDT_conform_05_gray
% test_linearSysDT_conform_05_gray - example script for reachset 
%   conformance synthesis using a gray-box model. This method identifies 
%   parameters of a gray-box model using fmincon in an outer loop as in 
%   Sec. III.B of [1] and updates the required uncertain sets according 
%   to [2],[3] in an inner loop. There exists a corresponding unit test.
%
% Syntax:
%    completed = test_linearSysDT_conform_05_gray
%
% Inputs:
%    None
%
% Outputs:
%    res - boolean
%
% References:
%    [1] S. B. Liu and M. Althoff, "Online Verification of 
%        Impact-Force-Limiting Control for Physical Human-Robot 
%        Interaction," 2021 IEEE/RSJ International Conference on 
%        Intelligent Robots and Systems (IROS), 2021, pp. 777-783.
%    [2] M. Althoff. Checking and Establishing Reachset Conformance in
%        CORA 2023. In Proc. of the 10th International Workshop on 
%        Applied Verification of Continuous and Hybrid Systems, 2023.
%    [3] Liu et al., "Guarantees for Real Robotic Systems: Unifying Formal
%        Controller Synthesis and Reachset-Conformant Identification", 202x.
%

% Authors:       Matthias Althoff
% Written:       30-August-2023
% Last update:   ---
% Last revision: ---

% ------------------------------ BEGIN CODE -------------------------------


%% System
% set seed for determinism 
rng(2023);

% define state space system 
dt = 0.1;

% system matrices
A = [0.5, -0.1;
      0.03, 0.4];
B = [ -0.01, -0.03;
      -0.02, 0.03];
C = [ 24, -110;
     150,  50];
D = [ 0, -2;
      2,  0];
  
% instantiate system  
sys = linearSysDT(linearSys(A,B,[],C,D), dt);

% parameters of the system
params.tStart       = 0;
params.tFinal       = 2;
params.R0conf       = zonotope([ones(2,1),0.1*eye(2)]);
params.R0           = params.R0conf;
params.W            = zonotope(zeros(2, 1), eye(2));
params.V            = zonotope(zeros(2, 1), eye(2));
params.getMatricesFromP = @getMatricesFromP_twoDimExample;
maxNrOfTimeSteps = ceil(params.tFinal/dt); % maximum number of timeSteps

% Initial estimations of system matrices, e.g., obtained from a system
% identification
A_0     = A + [0.1, -0.1; 0, -0.2];
B_0     = B + [0.2, -0.2; 0.1, -0.1];
C_0     = C + [0, 0; 0.1, -0.1];
D_0     = D + [-0.1, -0.1; -0.1, -0.1];
% parameter vector is simply the concatenation of entries of all system
% matrices
p_0     = [ reshape(A_0, [], 1); ...
            reshape(B_0, [], 1); ...
            reshape(C_0, [], 1); ...
            reshape(D_0, [], 1)];

% options for the system identification
dim_x = 2;
dim_u = 2;
dim_y = 2;
n_params = dim_x^2 + dim_x*dim_u + dim_y*dim_x + dim_y*dim_u;
options.p_min               = -100*ones(n_params, 1);
options.p_max               = 100*ones(n_params, 1);
options.p_min(1:dim_x^2)    = -1*ones(dim_x^2 , 1); % bounds for system matrices are chosen tighter
options.p_max(1:dim_x^2)    = 1*ones(dim_x^2 , 1); % bounds for system matrices are chosen tighter
options.p_free              = true(n_params, 1);      
options.p_0                 = p_0;
options.confAlg             = 'gray';

% options to weight cost function of different time steps
options.w = ones(maxNrOfTimeSteps+1,1);
options.zonotopeOrder = 200;

%% Create test cases
params.testSuite = aux_createTestCases(sys, params);

%% Conformance synthesis
% Perform conformance synthesis for gray-box model
[params_conform, ~, ~, ~, sys] = conform(sys,params,options);

%% Check conformance of obtained model
options.confAlg = 'dyn';
params_conform.V = enlarge(params_conform.V,1.001); % slightly enlarge V for numerical robustness
res = conform(sys,'check',params_conform,options);

end


% Auxiliary functions -----------------------------------------------------

function synthesizedTests = aux_createTestCases(sys, params)
    % because the system has no imaginary eigenvalues, the extreme cases
    % suffice to reproduce the actual uncertainties
    % combine zonotopes for R0, W, and V in a single zonotope
    
    % obtain dimensions of x, w, v
    dim_x = dim(params.R0);
    dim_u = size(sys.B,2);
    dim_w = dim(params.W);
    dim_v = dim(params.V);
    % Cartesian product of initial set, disturbance set, and measurement
    % uncertainty
    S_tmp = cartProd(params.R0, params.W);
    S = cartProd(S_tmp, params.V);
    % compute vertices of combined zonotope
    Vmat = vertices(S);
    % nr of tests 
    nrOfTests = size(Vmat,2);
    % init result
    synthesizedTests = cell(nrOfTests,1);
    % maximum number of timeSteps
    maxNrOfTimeSteps = ceil(params.tFinal/sys.dt); 
    % loop over each vertex
    for i = 1:nrOfTests
        % overwrite parameters
        params.R0 = zonotope(Vmat(1:dim_x,i)); % initial state
        params.W = zonotope(Vmat(dim_x+1:dim_x+dim_w,i)); % disturbance 
        params.V = zonotope(Vmat(dim_x+dim_w+1:dim_x+dim_w+dim_v,i)); % measurement uncertainty  
        % simulate system
        simOpt.points = 1;
        simRes = simulateRandom(sys, params, simOpt);
        % construct input vector
        uVec = zeros(maxNrOfTimeSteps+1,dim_u);
        % save in test case
        synthesizedTests{i} = testCase(simRes.y{1}, uVec, simRes.x{1}, sys.dt);
    end
end

% ------------------------------ END OF CODE ------------------------------
