Neural Preassigned Performance Control for State-Constrained Nonlinear Systems Subject to Disturbances

• Published in: IEEE transaction on neural networks and learning systems Vol. 36; no. 3; pp. 5032 - 5043
• Main Authors: Liu, Wei, Zhao, Jianhang, Zhao, Huanyu, Ma, Qian, Xu, Shengyuan, Park, Ju H.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.03.2025
• Subjects: Adaptive control; Artificial neural networks; Backstepping; Barrier Lyapunov function (BLF); Control design; Convergence; Disturbance observers; finite-time control; nonlinear disturbance observer; predefined performance control (PPC); state-constrained system; Vectors
• ISSN: 2162-237X; 2162-2388; 2162-2388
• DOI: 10.1109/TNNLS.2024.3377462

This article addresses the finite-time neural predefined performance control (PPC) issue for state-constrained nonlinear systems (NSs) with exogenous disturbances. By integrating the predefined-time performance function (PTPF) and the conventional barrier Lyapunov function (BLF), a new set of time-varying BLFs is designed to constrain the error variables. This establishes conditions for satisfying full-state constraints while ensuring that the tracking error meets the predefined performance indicators (PPIs) within a predefined time. Additionally, the incorporation of the nonlinear disturbance observer technique (NDOT) in the control design significantly enhances the ability of the system to reject disturbances and improves overall robustness. Leveraging recursive design based on dynamic surface control (DSC), a finite-time neural adaptive PPC strategy is devised to ensure that the closed-loop system is semi-globally practically finite-time stable (SPFS) and achieves the desired PPIs. Finally, the simulation results of two practical examples validate the efficacy and viability of the proposed approach.