Data-Driven Model-Free Adaptive Predictive Control for a Class of MIMO Nonlinear Discrete-Time Systems With Stability Analysis
Yuan Guo, Zhongsheng Hou, Shida Liu and 1 more
IEEE Access · 2019
doi:10.1109/access.2019.2931198 (opens in a new tab)
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Abstract
In this study, a model-free adaptive predictive control (MFAPC) method is proposed for a class of unknown nonlinear non-affine multiple-input and multiple-output (MIMO) systems based on a novel dynamic linearization technique and a new time-varying Pseudo-Jacobian matrix (PJM) parameter. The advantages of the proposed method are that it does not need the model information in the control system design, and it can avoid a short-sighted control decision and shows better control performance by integrating the idea of predictive control. The applicability and effectiveness of the proposed control scheme have been verified through rigorous mathematical analysis and extensive simulations.
Abstract licence: CC BY-NC-ND
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