Experimental Verification of a Novel Quad-RLS Technique for Improving Real-time System Identification Performance: A Practical Approach to CMG

Abstract

This paper presents a Quad RLS(Q-RLS) technique with four RLSs under a fixed forgetting factor condition to improve the identification performance of a dynamical system in a real-time fashion. Although an adaptive RLS method with a variable forgetting factor and a higher order model may provide the modeling accuracy, their implementations are not easy because of leakage effects and relatively complex modeling. In practice, the fixed forgetting factor is still used for the RLS-based system identification. Therefore, a novel Q-RLS scheme with concurrent four RLSs is proposed as an alternative way for the better estimation performance in an on-line fashion. In the Q-RLS scheme, the first pair of the forward and inverse RLSs is to identify the forward and inverse models independently. The second pair of the forward and inverse RLSs is to improve the identification of the previously identified model. The proposed approach has several advantages: 1) The RLS with a fixed forgetting factor can avoid the leakage problem. 2) Both forward and inverse models are separately identified to improve the accuracy. 3) Q-RLS can have the 4th order filter structure, but provide the better identification performance. Three schemes such as a second-order RLS, a fourth-order RLS, and the Q-RLS are experimentally tested and their performances are compared for the state observation accuracy of the control moment gyroscope(CMG) system.