Data-driven Model-Free Adaptive Control Tuned by Virtual Reference

Abstract

This paper proposes a new tuning approach, by which, all parameters of a datadriven Model-Free Adaptive Control (MFAC) algorithm are automatically determined using a nonlinear Virtual Reference Feedback Tuning (VRFT) algorithm. The approach is referred to as mixed MFAC-VFRT control and it leads to mixed MFAC-VFRT algorithms. An advantage of mixed MFAC-VFRT control, is that it combines systematically, the features of VRFT (it computes the controller parameters using only the input/output data) with those of MFAC. This is especially illustrated by comparison with the classical MFAC algorithms, the initial values of the parameters, which are obtained through a process that involves solving an optimization problem. The application that validates the mixed MFAC-

Intelligent Thermal Imaging-Based Diagnostics of Turbojet Engines

“…The system is designed to be objective, and does not need any input or examination of the infrared images by humans. It can also be integrated into a complex situational control algorithm of a turbojet engine in laboratory conditions, where outputs of the expert system can be used as inputs for the situational classifier and also to tune adaptive controllers of the engine [62,64,65].…”

“…The idea behind the design of the presented expert system is to evaluate its possibilities and its ability to correctly present diagnostic signals to the engine's operator or supervisory control system [64,65]. The designed rule base was therefore constructed to be very simple in order to evaluate the five pre-defined temperature states represented by hypotheses D. Pilot tests were conducted to prove that the system works as intended.…”