Finite-time adaptive neural control for nonstrict-feedback stochastic nonlinear systems with input delay and output constraints

Highlights：

• A new nonlinear mapping is developed under the stochastic version, which is different from the deterministic one. Based on this approach, the symmetric and asymmetric output constraints problems are researched in the same control scheme. Moreover, some disadvantages existing in the barrier Lyapunov function control approach can be avoided.

• The input delay problem is handled by the Pade approximation technique, which transforms the original delay input systems into delay-free ones. Finally, by the presented finite time control strategy, the tracking error signals can converge into a small neighborhood of origin point in probability during the finite time.

摘要

•This paper proposes a novel adaptive finite-time control strategy for a class of nonstrict-feedback stochastic systems, in which the input delay and output constraints problems exist simultaneously. Compared with Refs. [28], [38], [51], this paper dealt with these problems in a unified framework.•A new nonlinear mapping is developed under the stochastic version, which is different from the deterministic one. Based on this approach, the symmetric and asymmetric output constraints problems are researched in the same control scheme. Moreover, some disadvantages existing in the barrier Lyapunov function control approach can be avoided.•The input delay problem is handled by the Pade approximation technique, which transforms the original delay input systems into delay-free ones. Finally, by the presented finite time control strategy, the tracking error signals can converge into a small neighborhood of origin point in probability during the finite time.