Event‐Triggered Model‐Free Adaptive Predictive Control of Nonlinear Networked Control Systems With Time‐Varying Delays

In this paper, the data‐driven control issue for a class of nonlinear networked control systems (NCSs) with time‐varying delays is investigated. To achieve the tracking control of the nonlinear NCS, an event‐triggered model‐free adaptive predictive control (MFAPC) strategy is developed. First, an equivalent partial‐form dynamic linearization data model is established based on the time‐varying pseudo gradient, which is calculated by adopting the input/output (I/O) data of the nonlinear NCS. Next, the networked predictive control strategy is applied to deal with the negative effects of time‐varying delays on system performance in the sensor‐to‐controller (S–C) and controller‐to‐actuator (C–A) channels. Subsequently, two dynamic event‐triggered control mechanisms are adopted to balance the expected system performance and consumption of network resources. Moreover, the stability criterion of the closed‐loop nonlinear NCS is provided, and zero tracking error can be proved. In the end, the simulation example is performed to demonstrate the validity and superiority of the developed strategy.