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This paper proposes a novel adaptive control approach for a class of stochastic switched nonlinear system with pure feedback structure. An important lemma is developed to overcome the design difficulty from the non-affine pure feedback structure. Combining backstepping technique with neural network approximation, a state feedback adaptive controller is given. And this controller can ensure that all of the signals in the closed-loop system are bounded, and the tracking error converges to a small enough neighborhood of the zero. A simulation example is used to verify the effectiveness of our results. [ABSTRACT FROM AUTHOR]

This paper investigates observer-based adaptive fuzzy control for a class of delayed nonlinear systems in pure-feedback form. We first proposed a linear observer to get the estimation of the system's state. And then utilise fuzzy logic systems to model those nonlinearities. Further, adaptive fuzzy approach and backstepping technique are combined to develop the desired controller via Lyapunov analysis. The suggested adaptive fuzzy control scheme ensures that all the signals of the resulting nonlinear system converge to a small neighbourhood of the origin. Then a real simulation is used to illustrated the effectiveness of our results. [ABSTRACT FROM AUTHOR]

This paper is concerned with a tracking control issue for a class of switched stochastic systems with quantized input. To overcome the difficulty caused by the switching and quantization, a nonlinear decomposition strategy for quantizer is firstly proposed, then by using the common Lyapunov function method, a systematic adaptive control scheme is presented. It is proved that, under the proposed control scheme, the good control performance is obtained although influenced by the effect of switching and quantization. Furthermore, the validity of the proposed control scheme is verified by the simulation 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]