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.
Highlights
With the modern computer science developing rapidly, there have been great changes in large-scale industrial processes
The study of the data-driven control (DDC) theory are of significance in industrial fields [1]
The system’s pseudo partial derivative (PPD) is online estimated by using system I/O data, and the controller is designed using the equivalent dynamic linearization data model according to some weighted one-step-ahead cost functions
Summary
With the modern computer science developing rapidly, there have been great changes in large-scale industrial processes. There exist other datadriven control methods, including proportion integral differential control (PID) [7], [8], iterative learning control (ILC) [9], [10], frequency domain robust control [11], iterative feedback tuning (IFT) [12], [13], model-free sliding mode control [14], lazy learning (LL) [15], model-free adaptive control (MFAC) [16] and so on These control methods bypass the steps of modeling and design controllers directly through offline or online input and output data. The system’s PPD is online estimated by using system I/O data, and the controller is designed using the equivalent dynamic linearization data model according to some weighted one-step-ahead cost functions This method does not need precise model and identification process, so it has the advantages of simple controller structure, simple controller parameter on-line tuning algorithm, small calculation burden, convenient implementation and strong robustness.
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