Model-free Adaptive Iterative Learning Control for Nonlinear Systems With Time-varying Constraints

• Published in: International journal of control, automation, and systems Vol. 23; no. 3; pp. 935 - 944
• Main Authors: Wei, Yan, Zhang, Yong-Qi, Dong, Zi-Yuan, Ou, Lin-Lin, Yu, Xin-Yi
• Format: Journal Article
• Language: English
• Published: Bucheon / Seoul Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers 01.03.2025; Springer Nature B.V; 제어·로봇·시스템학회
• Subjects: Constraints; Control; Dynamic models; Engineering; Learning; Mechatronics; Neural networks; Nonlinear control; Nonlinear systems; Radial basis function; Regular Papers; Robotics; Uncertainty; 제어계측공학; model-free adaptive iterative learning control (MFAILC); security; time-varying constraints; Discrete control barrier function (DCBF)
• ISSN: 2005-4092; 1598-6446; 2005-4092
• DOI: 10.1007/s12555-024-0349-7

In this paper, an approach is designed to tackle the complex challenges of modeling unknown systems. This approach addresses the difficulty of relying on accurate modeling information when ensuring the security of a dynamics system. A model-free adaptive iterative learning safety control approach is designed based on the iterative control barrier function. Firstly, the dynamic model of the unknown system is discretized to create a discrete-time model. This transformation incorporates nonlinear model uncertainty by employing iterative dynamic linearization. Simultaneously, the uncertainty is estimated and compensated by radial basis function neural networks (RBFNNs). To ensure safety under time-varying constraints, a model-free adaptive iterative learning method with discrete control barrier function is designed based on the designed dynamic iterative model. Ultimately, the validity of the proposed approach is verified on the Franka-Panda robot platform.