Sampled-Data Control for Time-Scale-Type Networked Control Systems From a Data-Driven Perspective

This paper addresses the data-driven stabilization problem for a class of time-scale-type (TST) sampled-data networked control systems (NCSs) with unknown model parameters. A novel data-driven sampled-data (DDSD) control protocol is developed that relies solely on offline-collected datasets, thus eliminating the need for explicit model knowledge. Furthermore, to enlarge the maximum allowable sampling interval (ASI) while ensuring admissibility of all sampling instants, a TST matrix exponential gain is embedded into the DDSD control protocol, and a backward-jump sampling operator is introduced to address the discontinuities inherent in time scales. By integrating the direct data-driven methods, the theory of TST dynamic equations, and a generalized Halanay-like inequality, we establish a set of sufficient conditions that ensure the exponential stability of TST sampled-data NCSs under the proposed DDSD control protocol. We further employ a convex optimization framework to maximize the ASIs while satisfying stability constraints. Finally, the effectiveness of the proposed approach is demonstrated through numerical simulations and a case study involving an operational amplifier circuit with unknown model parameters.