Prescribed-Time Adaptive Neural Tracking Control of Full-State Constrained Nonlinear Systems

The purpose of this paper is to propose a novel design algorithm based symmetric barrier Lyapunov function (BLF) and radial basis neural networks (RBF NNs) for nonlinear systems with full-state time-varying asymmetric constraints. First, we construct a new form of barrier Lyapunov function (BLF) such that the tracking error tends to a sufficiently small neighborhood of the origin with prescribed finite time and pre-given accuracy. Second, we propose a saturated filter to make virtual controllers naturally meet “feasibility conditions”. In addition, our approach can loosen the limitation of existing constraint boundaries, dealing with more general cases of time-varying state constraint. Finally, the efficacy of the proposed approach is further confirmed by simulation verification.