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Key Words:BARRIER LYAPUNOV FUNCTIONS; STRICT-FEEDBACK SYSTEMS; ADAPTIVE NEURAL-CONTROL; TRACKING CONTROL; CONTROL DESIGN; STABILIZATION; INPUT

Abstract:In this paper, the adaptive multi-dimensional Taylor network (MTN) control problem is investigated for nonlinear stochastic systems with full state time-ying constraints and the finite-time output constraint. By combining the MTN-based approximation method and the adaptive backstepping control method, a novel adaptive MTN control scheme is provided by constructing the time-ying barrier Lyapunov function (TVBLF). To implement the finite-time output constraint, the finite-time performance function (FTPF) is introduced in the control scheme. The proposed scheme can ensure that the tracking error finally converges to a small neighborhood of the origin in the finite-time and all signals in the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB) in probability. Finally, two simulation examples are presented to show the effectiveness of the provided control scheme.

Volume:24

Issue:6

Translation or Not:no

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