Learning Stable Robust Adaptive NARMA Controller for UAV and Its Application to Twin Rotor MIMO Systems

Abstract

This study presents a nonlinear auto-regressive moving average (NARMA) based online learning controller algorithm providing adaptability, robustness and the closed loop system stability. Both the controller and the plant are identified by the proposed NARMA based input–output models of Wiener and Hammerstein types, respectively. In order to design the NARMA controller, not only the plant but also the closed loop system identification data are obtained from the controlled plant during the online supervised learning mode. The overall closed loop model parameters are determined in suitable parameter regions to provide Schur stability. The identification and controller parameters are calculated by minimizing the ε-insensitive error functions. The proposed controller performances are not only tested on two simulated models such as the quadrotor and twin rotor MIMO system (TRMS) models but also applied to the real TRMS with having severe cross-coupling effect between pitch and yaw. The tracking error performances of the proposed controller are observed better compared to the conventional adaptive and proportional–integral–derivative controllers in terms of the mean squared error, integral squared error and integral absolute error. The most noticeable superiority of the developed NARMA controller over its linear counterpart, namely the adaptive auto-regressive moving average (ARMA) controller, is observed on the TRMS such that the NARMA controller shows a good tracking performance not only for the simulated TRMS model but also the real TRMS. On the other hand, it is seen that the adaptive ARMA is incapable of producing feasible control inputs for the real TRMS whereas it works well for the simulated TRMS model.