Neural Network Controller Design for a Class of Nonlinear Delayed Systems With Time-Varying Full-State Constraints

• Published in: IEEE transaction on neural networks and learning systems 2019-09, Vol.30 (9), p.2625-2636
• Main Authors: Li, Dapeng, Chen, C. L. Philip, Liu, Yan-Jun, Tong, Shaocheng
• Format: Article
• Language: English
• Subjects: Adaptive control; Artificial neural networks; barrier Lyapunov functions (BLFs); Computer simulation; Constraints; Control systems design; Delay effects; Delays; Economic models; Liapunov functions; Neural networks; neural networks (NNs); Nonlinear systems; nonlinear time-delayed systems; Stability; Stability analysis; Task complexity; Time lag; Time-varying systems
• ISSN: 2162-237X; 2162-2388
• DOI: 10.1109/TNNLS.2018.2886023

This paper proposes an adaptive neural control method for a class of nonlinear time-varying delayed systems with time-varying full-state constraints. To address the problems of the time-varying full-state constraints and time-varying delays in a unified framework, an adaptive neural control method is investigated for the first time. The problems of time delay and constraint are the main factors of limiting the system performance severely and even cause system instability. The effect of unknown time-varying delays is eliminated by using appropriate Lyapunov-Krasovskii functionals. In addition, the constant constraint is the only special case of time-varying constraint which leads to more complex and difficult tasks. To guarantee the full state always within the time-varying constrained interval, the time-varying asymmetric barrier Lyapunov function is employed. Finally, two simulation examples are given to confirm the effectiveness of the presented control scheme.