diff --git a/CheckAndersLMI.m b/CheckAndersLMI.m
deleted file mode 100644
index 325b68e0705e1b8b228429b4a51f97b8710af8ed..0000000000000000000000000000000000000000
--- a/CheckAndersLMI.m
+++ /dev/null
@@ -1,195 +0,0 @@
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-%
-% This code simulates distributed minimax adaptive control algorithm for
-% uncertain networked systems.
-%
-% Copyrights Authors: 1) Venkatraman Renganathan - Lund University, Sweden.
-% 2) Anders Rantzer - Lund University, Sweden.
-% 3) Olle Kjellqvist - Lund University, Sweden.
-%
-% Email: venkatraman.renganathan@control.lth.se
-% anders.rantzer@control.lth.se
-% olle.kjellqvist@control.lth.se
-%
-% Date last updated: 20 October, 2023.
-%
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-
-% Make a fresh start
-clear all; close all; clc;
-
-% set properties for plotting
-set(groot,'defaultAxesTickLabelInterpreter','latex');
-set(groot,'defaulttextinterpreter','latex');
-set(groot,'defaultLegendInterpreter','latex');
-
-% Flag deciding whether to generate new data or load existing data
-% dataPrepFlag = 1: Generates new network data
-% dataPrepFlag = 0: Loads existing network data
-dataPrepFlag = 1;
-
-% When dataPrepFlag = 1: Generate new data for network
-if(dataPrepFlag)
-
- disp('Generating new data for network as dataPrepFlag = 1.');
-
- %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
- % Define the network data using graph structure
- %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-
- % Specify Number of nodes
- N = 6;
- % Specify Number of edges
- M = N - 1;
- % Create a random graph with N nodes and M edges
- [adjMatrix, I, graphVariable] = GenerateRandomTreeGraph(N);
- % Get number of edges on graph
- numEdges = size(graphVariable.Edges.EndNodes, 1);
- fprintf('Generated random graph is acyclic and has only one component \n');
-
- % Plot the graph - large graph can be time consuming & may not be informative
- if N <= 300
- plot(graphVariable);
- else
- disp('The graph is too large to display.');
- end
-
- % Infer state and input dimensions
- [n,m] = size(I);
-
- % Get the degree vector
- degreeVec = sum(adjMatrix, 1)';
-
- % Get one of the node indeces with maximum degree for plotting
- [~, maxDegreeNodeIndex] = max(degreeVec);
-
- % Form input matrix B with parameter b and matrix I such that B/b = I
- b = 0.1;
- B = b*I;
-
- %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
- % Prepare the dynamics matrix A for each Model
- %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-
- % Specify the number of possible plant network parameters
- numModels = 2;
-
- % Populate all the possible plant models
- for dIter = 1:numModels
- % Placeholders for a matrix for each component
- ai = zeros(n, 1);
- % Flag for stopping while loop
- dynamicsPreparationFlag = 1;
- % Counter for populating ai vector
- iter = 1;
- % Loop through until ai^2 + bb^{T} < ai is satisfied
- while dynamicsPreparationFlag
- % Generate a random ai
- ai(iter, 1) = rand;
- % Check condition
- if(ai(iter, 1)^2 - ai(iter, 1) + 2*b^2*degreeVec(iter, 1) < 0)
- iter = iter + 1;
- end
- % if counter exceeds limit, form the A matrix & stop while loop
- if(iter > n)
- A = diag(ai);
- dynamicsPreparationFlag = 0;
- end
- end
- % Prepare A and B matrices
- AMatrices{dIter} = A;
- BMatrices{dIter} = B;
-
- end
-
- AVector = zeros(n, numModels);
- for dIter = 1:numModels
- AVector(:, dIter) = diag(AMatrices{dIter});
- end
- Arowvectors = {};
- for i = 1:N
- Arowvectors{i} = AVector(i, :);
- end
- Acombs = combvec(Arowvectors{:}).';
- % Find min & max a values for each node from all possible numModels values
- minAVector = min(AVector, [], 2);
- maxAVector = max(AVector, [], 2);
- % Form the Max A matrix using maxAVector
- maxAMatrix = diag(maxAVector);
- % Find the network level global minimum and maximum a value
- netMinA = min(minAVector);
- netMaxA = max(maxAVector);
-
- % Infer the number of possible plants
- numPlants = numModels^(N);
-
- % Compute Hinfty control for each possible plants
- for i = 1:numPlants
- APossibles{i} = diag(Acombs(i, :));
- BPossibles{i} = B;
- % Compute H_infinity control as u = Kx, with K = B'*(A-I)^{-1}
- KinfPossibles{i} = BPossibles{i}'*inv(APossibles{i} - eye(n));
- end
- disp('Finished Computing H_infinity Control Gains');
-
- % Check Anders LMI for each possible plant
- gamma = 7;
- P_non = (2/(1 - netMaxA))*eye(n);
- P_non_inv = ((1 - netMaxA)/2)*eye(n);
- P_wt = inv(P_non_inv - gamma^(-2)*eye(n));
- Q = eye(n);
- R = eye(M);
- happy = 1;
-
- for k = 1:numPlants
- Ak = APossibles{k};
- if(happy == 0)
- break;
- end
- for l = 1:numPlants
- Al = APossibles{l};
- if(happy == 0)
- break;
- end
- for p = 1:numPlants
- if(happy == 0)
- break;
- end
- if(k ~= l && l == p)
- continue;
- end
- Kp = KinfPossibles{p};
- A_cl_kp = Ak + B*Kp;
- A_cl_lp = Al + B*Kp;
- AndersRHS = Q + Kp'*R*Kp + 0.25*(A_cl_kp+A_cl_lp)'*P_wt*(A_cl_kp+A_cl_lp) - 0.25*gamma^(2)*(A_cl_kp-A_cl_lp)'*(A_cl_kp-A_cl_lp);
- AndersLMI = P_non - AndersRHS;
- if(all(eig(AndersLMI)) >= 0)
- happy = 1;
- else
- happy = 0;
- end
- end
- end
- end
-
-
- % Finished Checking Anders LMI
- if(happy == 1)
- disp('Choice of Pkl matrices satisfy Anders LMI.');
- else
- disp('Choice of Pkl matrices do not satisfy Anders LMI.');
- end
-
- % Save the generated network data into mat file.
- disp('Saving the generated small network data.');
- %save('smallNetworkDynamicsData.mat');
-else
- disp('Loading existing data for small network as dataPrepFlag = 0.');
- %load('smallNetworkDynamicsData.mat');
-end
-
-
-
-