Adaptive neural network control of nonlinear systems with unknown dynamics. Issue 3 (1st February 2021)

• Record Type: Journal Article
• Authors: Cheng, Lin; Wang, Zhenbo; Jiang, Fanghua; Li, Junfeng
• Is Part Of: Advances in space research. Volume 67:Issue 3(2021)
• Page Start: 1114
• Page End: 1123
• Publication Date: 2021-02-01
• Subjects: Unknown dynamics -- Input–output linearization -- Extended state observation -- Iterative control learning -- Adaptive network control; Space sciences -- Periodicals; Astronautics -- Periodicals; Geophysics -- Periodicals; 500.505
• DOI: 10.1016/j.asr.2020.10.052
• Languages: English
• ISSNs: 0273-1177

Abstract

Abstract: In this study, an adaptive neural network control approach is proposed to achieve accurate and robust control of nonlinear systems with unknown dynamics, wherein the neural network is innovatively used to learn the inverse problem of system dynamics with guaranteed convergence. This study focuses on the following three contributions. First, the considered system is transformed into a multi-integrator system using an input–output linearization technique, and an extended state observation technique is used to identify the transformed states. Second, an iterative control learning algorithm is proposed to achieve the neural network training, and stability analysis is given to prove that the network's predictions converge to ideal control inputs with guaranteed convergence. Third, an adaptive neural network controller is developed by combining the trained network and a proportional-integral controller, and the long-standing challenge of model-based methods for control determination of unknown dynamics is resolved. Simulation results of a virtual control mission and an aerospace altitude tracking mission are provided to substantiate the effectiveness of the proposed techniques and illustrate the adaptability and robustness of the proposed controller.