Event-Triggered Model-Free Adaptive Control

This paper investigates an event-triggered model-free adaptive control for nonaffined nonlinear systems under a data-driven design framework. By introducing a compact form dynamic linearization (CFDL) scheme, a linear data model of the nonlinear nonaffine system is derived. Then, a parameter estimation algorithm is developed to offline identify the linear data model. On the basis of the identified linear data model, a CFDL-based event-triggered model-free adaptive control (CFDL-ET-MFAC) is developed by designing an event-triggering condition to guarantee the Lyapunov stability. The control action is active only when the event-triggering condition is satisfied. Otherwise, the input signal remains the same as that at the previous triggering instant. In addition, the parameter estimation algorithm is developed for the proposed CFDL-ET-MFAC to identify the CFDL model in real time for improving the robustness to the uncertainties. Meanwhile, both a partial form dynamic linearization-based event-triggered MFAC and a full form dynamic linearization-based event-triggered MFAC are proposed to further improve the control performance by using additional parameters to capture the more complicated behavior of complex nonlinear systems. The proposed ET-MFAC methods only rely on the linear data models directly obtained from data without using any other mechanistic model information. The validity of the three ET-MFAC methods is confirmed through both theoretical analysis and simulation studies.