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.