Design and Testing of a Constrained Data-Driven Iterative Reference Input Tuning Algorithm

This paper presents aspects concerning the design and testing of a new data-driven Iterative Reference Input Tuning (IRIT) algorithm that solves a reference trajectory tracking problem expressed as an optimization problem with control signal saturation constraints and control signal rate constraints. The design of the IRIT algorithm uses an experiment-based stochastic search algorithm formulated in the framework of Iterative Learning Control (ILC) in order to combine the advantages of data-driven control and of ILC. The iterative tuning is model-free in the sense it does not use control system models. A set of simulation results tests and validates the IRIT algorithm in a case study related to a representative mechatronics application that deals with the position control of a nonlinear aero-dynamical system. The IRIT algorithm offers the performance improvement by few iterations and experiments conducted on the process.