Multiple Communication Conditions and Finite Memory
This chapter 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 ILC algorithm, and are shown to converge to the desired input in almost sure sense.
- 4.Chen, H.F.: Stochastic Approximation and its Applications. Kluwer (2002)Google Scholar
- 5.Shen, D.: Data-driven learning control for stochastic nonlinear systems: multiple communication constraints and limited storage. IEEE Trans. Neural Netw. Learn. Syst. (2018). https://doi.org/10.1109/TNNLS.2017.2696040