An improved full-form-dynamic-linearization based MFAC for a class of nonlinear systems with exogenous disturbance

Abstract

In this paper, an improved full-form-dynamic-linearization (iFFDL) based model free adaptive control (MFAC) scheme (iFFDL-MFAC) is proposed for a class of discrete-time nonlinear systems with exogenous disturbance. The novel iFFDL data model is built along the dynamic operation points of the controlled plant using the concept called pseudo gradient (PG), where the exogenous disturbance is regarded as an element of pseudo gradient and it can be estimated merely using measured input and output data of the controlled plant. Then, the MFAC scheme is designed based on the proposed iFFDL data model according to a cost function of control input. By virtue of the proposed iFFDL method, the possible complicated behavior of the PG for the original nonlinear system caused by the exogenous disturbance may be better captured and dispersed. As a result, the proposed iFFDL-MFAC can deal with the exogenous disturbance more effectively and gives better control performance comparing with the prototype MFAC. Simulation results illustrate the correctness and effectiveness of the iFFDL-MFAC scheme.

Mengxue Hou

Assistant Professor, Electrical Engineering

My research interests include robotic autonomy, mobile sensor networks, and human robot interaction. I aim to devise practical, computationally-efficient, and provably-correct algorithms that prepare robotic systems to be cognizant, taskable, and adaptive, and can collaborate with human operators to co-exist in a complex, ever-changing and unknown environment.