Data learning-based model-free adaptive control and application to an NAO robot

In this article, an improved model-free adaptive control method based on the controller dynamic linearization technique combined with the locally weighted regression-based lazy learning method (cMFAC-LL) is presented, and it is applied to solve the path-tracking control problem for an NAO robot. In the proposed cMFAC-LL method, two dynamic linearization techniques are first applied on the controlled plant, and then the cMFAC-LL controller is further designed with the time-varying parameters estimated using a novel locally weighted regression-based lazy learning (LWR-LL) technique. The greatest advantage of the cMFAC-LL method is that it is a pure data-driven control method, and the designed controller makes full use of both the online and offline measurement data of the plant. Moreover, the introduction of the local learning (LL) method gives the cMFAC-LL method strong data learning ability and satisfactory control performance. The stability of cMFAC-LL is proven via rigorous mathematical analysis. Furthermore, cMFAC-LL is applied to the path-tracking control of the NAO robot under different walking environments, by which the applicability of cMFAC-LL is further verified.