%0 Generic
%A Benenati, Emilio
%A Grammatico, Sergio
%D 2024
%T Data and code underlying the arXiv submission: Linear-Quadratic Dynamic Games as Receding-Horizon Variational Inequalities
%U 
%R 10.4121/ea21437d-6fb7-4b37-b640-e7cb53a56a45.v1
%K dynamic games
%K game theory
%K Generalized Nash Equilibrium
%K GNE
%K receding horizon
%K simulation
%K vehicle platooning
%K power control
%X <p>This data contains simulation results for the automatic power generation control of a 4-zone system, and for a vehicle platooning application., controlled using a receding-horizon approach based on the open-loop Nash equilibrium (ol-NE) and the closed-loop Nash equilibrium (cl-NE) computation</p><p><br></p><p><strong><u>Automatic power generation test</u></strong></p><p>The N=4 agents perform a receding-horizon control action based on the computation of a cl-NE for the underlying dynamic game. The test is performed over N_tests=100 randomized initial conditions, and the proposed methodology (with a terminal cost) is compared to a "baseline method", namely, non-cooperative MPC method (without terminal cost). The simulation time T_sim is 100 time-steps. The relative 4_zones_power_system.mat file contains the following data:</p><ul><li>x_cl: array of size (n_x, 1, T_sim+1, N_tests). It contains the state at each time-step computed using the cl-NE method</li><li>u_cl: array of size (n_u, 1, N, T_sim+1, N_tests), where N is the number of agents and n_u is the numbers of input variables for each agent. It contains the input at each time-step computed using the cl-NE method</li><li>x_bl: array of size (n_x, 1, T_sim+1, N_tests). It contains the state at each time-step computed using the baseline method</li><li>u_bl: array of size (n_u, 1, N, T_sim+1, N_tests), where N is the number of agents and n_u is the numbers of input variables for each agent. It contains the input at each time-step computed using the baseline method</li><li>X_f_cl: EllipsoidSet class (see MPT3 toolbox), which cointains the estimated terminal set of the proposed method</li><li>norms_x_0_to_test: vector of dimension 5: for each test, the norm of the initial state is one of the elements, times the radius of X_f_cl</li></ul><p><strong><u>Vehicle platooning test</u></strong></p><p>The N=5 agents perform a receding-horizon control action based on the computation of an ol-NE for the underlying dynamic game. The test is performed over N_tests=1 randomized initial conditions. The simulation time T_sim is 200 time-steps. The relative vehicle_platooning.mat file contains the following data:</p><ul><li>x_ol: array of size (n_x, 1, T_sim+1, N_tests). It contains the state at each time-step computed using the ol-NE method</li><li>u_ol: array of size (n_u, 1, N, T_sim+1, N_tests), where N is the number of agents and n_u is the numbers of input variables for each agent. It contains the input at each time-step computed using the ol-NE method</li></ul><p><br></p><p><br></p><p><br></p>
%I 4TU.ResearchData