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Abstract This manuscript studies a finite-time tracking problem of switched stochastic nonlinear uncertain systems. By utilizing a comparison theorem and a mean value theorem of integrals, a significant finite-time stability criterion for stochastic nonlinear systems is first set up. Then, combining a common Lyapunov function method and an adaptive fuzzy control approach, a novel common control scheme is formed. Under the proposed adaptive mechanism, the system performance is achieved in finite-time. By using the universal approximation ability, the nonlinear functions and their bounding functions may be completely unknown to the designer, which is more in line with reality than the existing finite-time researches for stochastic nonlinear systems. [ABSTRACT FROM AUTHOR]

An adaptive output-feedback control method is developed for a class of multi-input and multi-output (MIMO) delayed nonlinear systems which are subject to input saturated nonlinearities, modeling uncertainties and time delays. Because state variables are unobtainable, state observers are constructed first. And radial basis function (RBF) neural networks (NNs) are used as approximators to identify the unknown nonlinearities. Adaptive technique is applied to estimate the optimal weight vectors of the approximators. Backstepping method is used to construct the desired controllers. Based on Lyapunov stability theory, the proposed tracking control strategy ensure that all signals in the closed-loop systems are bounded and the target trajectories can be tracked within a enough small error as well. At last, numerical simulations demonstrate the effectiveness of the presented control method. [ABSTRACT FROM AUTHOR]

The problem of adaptive practical finite‐time control is considered for a class of single‐input and single‐output non‐linear systems, in which the system non‐linear functions are assumed to be unknown. By combining adaptive fuzzy control approach with the backstepping technology, a backstepping‐based adaptive fuzzy finite‐time control scheme is proposed. In the control design procedure, fuzzy logic systems are employed to identify the non‐linear uncertainties. The stability analysis of the adaptive closed‐loop systems is proposed based on the finite‐time Lyapunov stability theory. The proposed adaptive fuzzy controller guarantees that all the closed signals are semi‐global practical finite‐time stability while the tracking error converges to a small neighbourhood of the origin. Finally, simulation results are presented to validate the effectiveness of our results. [ABSTRACT FROM AUTHOR]

This paper focuses on the issue of adaptive neural output-feedback control for a class of nonlinear pure-feedback systems. A state observer is first constructed to estimate the unmeasurable state variables. Radial basis function (RBF) neural networks (NNs) are used to approximate the unknown nonlinear functions and backstepping adaptive technique is utilized to construct controller. The proposed control scheme guarantees semi-globally uniformly ultimate boundedness (SGUUB) of all the signals in the closed-loop system. At last, a simulation example is used to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]