MTN-Based Adaptive Finite-Time Tracking Control for Switched Non-Linear Systems With Time-Varying State Function Constraints

This paper studies the finite-time control problem of a class of switched non-linear systems under time-varying state constraints and proposes an adaptive finite-time controller based on a multi-dimensional Taylor network (MTN). Firstly, the time-varying tangent barrier Lyapunov functions (TBLFs) are constructed to ensure that all system states are constrained within a certain range. Secondly, MTNs are used to estimate the unknown non-linear functions during the controller design process. The proposed control scheme ensures that the tracking error of the system can converge to a small domain of the origin in a finite-time. At the same time, all the signals of the closed-loop system are semi-global practical finite-time stable (SGPFS) and all states satisfy the defined time-varying state constraints. Finally, the effectiveness of the control strategy is verified through numerical simulation examples and practical simulation examples.