Improving Model-Free Control Algorithms Based on Data-Driven and Model-Driven Approaches: A Research Study

Ziwei Guo and Huogen Yang ()
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Ziwei Guo: Data Analytic Department, Dickinson College, Carlisle, PA 17013, USA
Huogen Yang: College of Science, Jiangxi University of Science and Technology, Ganzhou 341000, China

Mathematics, 2023, vol. 12, issue 1, 1-15

Abstract: Given the challenges associated with accurately modeling complex nonlinear systems with time delays in industrial processes, this paper introduces an advanced model-free control algorithm that combines data-driven and model-driven approaches. Initially, an enhanced algorithm for multi-innovation model-free control, incorporating error feedback, is presented based on the error feedback principle. Subsequently, a novel control strategy is introduced by delving into PID neural network (NN) recognition and control theory, merging PID NN control with multi-innovation feedback control. Through meticulous mathematical derivation, the proposed strategy is proven to ensure system stability. Compared with traditional NN PID controllers, the convergence rate of the proposed scheme is 50 s faster and the steady-state errors are limited to ±1.

Keywords: complex nonlinear systems; multi-innovation; model-free control; PID; NN (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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