Inverse Reinforcement Learning for Disturbed Networked Nonlinear Systems With Data Dropouts

This article develops inverse reinforcement learning (IRL) control algorithms for nonlinear networked control systems (NCSs) to mimic trajectories of a target system governed by an unknown optimal cost function, despite the presence of random data dropouts and external disturbances. Data dropouts occur during: 1) reception of target trajectory data by the controller; 2) reception of state feedback data by the controller; and 3) reception of control input data by the actuator. By organically integrating $H_{\infty } $ control to account for disturbances and dropout-induced uncertainty, a model-based IRL algorithm is first developed. Building on this, a neural-network-based data-driven IRL algorithm is developed to infer the cost function and optimal control policy using available data while reducing dependence on system models. The proposed methods enable effective trajectory imitation under partial model knowledge, data dropouts, and disturbances, as demonstrated through simulation studies.