Adaptive Neural Network Finite-Time Tracking Control for Uncertain Nonlinear Systems with Full-State-Function Constraints

The finite-time control issue is discussed for nonlinear systems with full-state-function constraints, unknown control gain (UCG) and external disturbances in this paper. In order to prevent state variables from breaking constraints, we construct a barrier Lyapunov function (BLF) and introduce the Nussbaum function to overcome the design difficulties caused by UCG. At the same time, the neural networks (NNS) are introduced to approximate a series of unknown nonlinear functions generated in the controller design. The designed adaptive tracking controller can pledge that all states do not violate the constraint boundary, all signals of the closed-loop system are bounded, and the tracking error converges to a small neighborhood containing the origin in finite time. Finally, the feasibility of the fabricated control scheme is verified by a numerical example.