Finite-time prescribed performance tracking control for nonlinear time-delay systems with state constraints and actuator hysteresis.

In this paper, the problem of adaptive neural network prescribed performance tracking control for a class of non-strict feedback time-delay systems constrained by full-state is studied. Radial basis function (RBF) neural networks (NNs) are integrated into the backstepping medium to deal with the uncertain functions and the barrier Lyapunov function (BLF) technique ensures that the state of the system does not exceed its limits. Subsequently, integrated with the Lyapunov–Krasovskii functional, the proposed control scheme makes the tracking errors converge to the preset region while the state constraint is not violated. Finally, the effectiveness of the scheme is supported by two simulation experiments. • An adaptive neural control scheme is proposed for nonlinear time-delay systems. • For unknown model, the proposed controller can achieve the control objective. • Coordinate transformation is used to improve system tracking performance. [ABSTRACT FROM AUTHOR]