Model-Free Adaptive State Feedback Control for a Class of Nonlinear Systems

• Published in: IEEE transactions on automation science and engineering Vol. 21; no. 2; pp. 1824 - 1836
• Main Authors: Gao, Shouli, Zhao, Dongya, Yan, Xinggang, Spurgeon, Sarah K.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.04.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation models; Adaptive control; Complexity; Computational modeling; Continuously stirred tank reactors; Control systems design; Data models; Dynamic stability; Effectiveness; Experimentation; Feedback control; Mathematical models; model-free adaptive control; Nonlinear systems; Observers; Process control; Stability analysis; State feedback; State feedback control; state observer
• ISSN: 1545-5955; 1558-3783
• DOI: 10.1109/TASE.2023.3237811

This paper investigates state feedback control for a class of discrete-time multiple input and multiple output nonlinear systems from the perspective of model-free adaptive control and state observation. The design of a dynamic state feedback control can be efficiently carried out using dynamic linearization and state observation. The stability of the proposed method is guaranteed by theoretical analysis. Numerical simulation tests and experimentation on a continuous stirred tank reactor are carried out to validate the effectiveness of the proposed approach. Note to Practitioners-The growth in the scale of factories and the complexity of associated production processes increases the complexity and time involved in associated mathematical modelling. Data driven approaches to control remove the need to model processes. To the best of the authors' knowledge, existing approaches to model-free adaptive control (MFAC) of general systems are all based on an input-output control paradigm. These methods thus cannot guarantee the stability of the system state. The purpose of this study is to develop a novel Model-Free Adaptive Control (MFAC) approach to achieve control of the system state. In this paper, the assumptions required to achieve model-free adaptive control by state feedback are presented mathematically. A controller design and the associated stability proof are then presented. Numerical simulation and experimentation is conducted to validate the effectiveness of the proposed approach. In future research, state feedback data control in the presence of random disturbances will be investigated.