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

TLDR

State constraints frequently arise in real‑life plants, making their avoidance a critical challenge. The paper investigates an adaptive neural‑network control method to stabilize uncertain nonlinear strict‑feedback systems under full‑state constraints. A novel adaptive backstepping design employs a barrier Lyapunov function at each step to guarantee constraint satisfaction while using minimal learning parameters. Lyapunov analysis shows the closed‑loop signals are semiglobal uniformly ultimately bounded and the output tracks the desired trajectory, as confirmed by simulation.

Abstract

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.