Data-Driven Learning Control for Stochastic Nonlinear Systems: Multiple Communication Constraints and Limited Storage

This paper proposes a data-driven learning control method for stochastic nonlinear systems under random communication conditions, including data dropouts, communication delays, and packet transmission disordering. A renewal mechanism is added to the buffer to regulate the arrived packets, and a recognition mechanism is introduced to the controller for the selection of suitable update packets. Both intermittent and successive update schemes are proposed based on the conventional P-type iterative learning control algorithm, and are shown to converge to the desired input with probability one. The convergence and effectiveness of the proposed algorithms are verified by means of illustrative simulations.