A Data-Driven Predictive Control Scheme for Nonlinear Discrete-Time Systems

Abstract

This article provides a new methodology to design a novel predictive control (PC) scheme for unknown nonlinear discrete-time systems, by deeply exploiting future ideal controllers and the dynamic linearization (DL) technique. The control input increment vector can be linearly parameterized with the time-varying control gain vector. The PC law is obtained by directly optimizing the control gain vector with the least square method. The system outputs are predicted through the parameterized PC law and the DL data model of the controlled system. The proposed PC scheme is data-driven, that is, it does not depend on the system dynamic model and the control gain vector is adaptively optimized by using only the measured input/output data. The monotonic convergency of the proposed PC scheme is theoretically guaranteed, and its effectiveness is validated by two illustrative examples, i.e., a complicated nonlinear system and a linear time-invariant system.