Experimental Comparison of Two Data-Driven Algorithms for Pitch Control of an Aerospace System

Abstract

Data-driven control (DDC) algorithms have been developed in the last decades, whose design is based only on the data collected from the controlled plant, without using a process model. These techniques that do not use an explicit model of the system have become very attractive for the control of complex processes with high nonlinearities. This paper presents two DDC algorithms, one model-free adaptive control (MFAC), and the other model-free intelligent P(iP), whose performances are experimentally evaluated using the AERO 2 platform, a highly nonlinear aerospace system made by Quanser. The similarities and differences between the two DDC are succinctly presented and based on the results obtained through real-time experiments, the performances are compared.