network-based (NN) Model Predictive Control (MPC)

# NN_MPC Neural network-based (NN) Model Predictive Control (MPC) algorithm to control a multi-agent system (MAS) with stochastic communication topology. Paper available: https://www.sciencedirect.com/science/article/pii/S095219762200361X Floriano, B. R., Vargas, A. N., Ishihara, J. Y., & Ferreira, H. C. (2022). Neural-network-based model predictive control for consensus of nonlinear systems. Engineering Applications of Artificial Intelligence, 116, 105327. ## Algorithm folder Folder includes the NN-based MPC algorithm to achieve consensus in MAS for quadrotor fleet system, system with disturbances and nonlinear robot-car system. ### How to run algorithm The main file is "main.m". In order to run it, uncomment line: * Line 17 - "start_WW20_Fig1" to run linear system (based on Wang et al. (2018)); * Line 18 - "start_GL20_Fig3" to run system with disturbances (based on Gao et al. (2020)); * Line 21 - "start_Car" to run nonlinear robot-car system. The output is the evolution of the system's states over time. ## Results folder Includes the data (.mat) and the code to generate the graphs and tables. ### How to verify the results The subfolder "results" contain all the data (.mat) shown in the Results section. It is organized according to the folder's names as follows: * WW20: refers to the results of the linear system * GL20: refers to the results of the system with disturbances * Car: refers to the results of the nonlinear robot-car system * Crossval: refers to the cross-validation results * Horizon: refers to the results for different horizons * Pi_x: refers to the results with the transition matrix \Pi = \Pi_x To generate the graphics of state's progression over time, run "analysis.m". To generate the cross-validation and horizon graphics, run "analysis_crossval.m".