Neural Network Control-Based Adaptive Learning Design for Nonlinear Systems With Full-State Constraints

• Published in: IEEE transaction on neural networks and learning systems 2016-07, Vol.27 (7), p.1562-1571
• Main Authors: Yan-Jun Liu, Jing Li, Shaocheng Tong, Chen, C. L. Philip
• Format: Article
• Language: English
• Subjects: Adaptive control; Adaptive control systems; Adaptive neural control; Approximation algorithms; Approximation error; Artificial neural networks; Backstepping; barrier Lyapunov function (BLF); Computer simulation; Design analysis; Design engineering; full-state constraints; Learning; Lyapunov functions; Neural networks; neural networks (NNs); Nonlinear systems; Tasks
• ISSN: 2162-237X; 2162-2388
• DOI: 10.1109/TNNLS.2015.2508926

In order to stabilize a class of uncertain nonlinear strict-feedback systems with full-state constraints, an adaptive neural network control method is investigated in this paper. The state constraints are frequently emerged in the real-life plants and how to avoid the violation of state constraints is an important task. By introducing a barrier Lyapunov function (BLF) to every step in a backstepping procedure, a novel adaptive backstepping design is well developed to ensure that the full-state constraints are not violated. At the same time, one remarkable feature is that the minimal learning parameters are employed in BLF backstepping design. By making use of Lyapunov analysis, we can prove that all the signals in the closed-loop system are semiglobal uniformly ultimately bounded and the output is well driven to follow the desired output. Finally, a simulation is given to verify the effectiveness of the method.