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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 addresses adaptive neural control for a class of non-strict-feedback stochastic nonlinear systems with time delays. An important structural property of radial basis function (RBF) neural networks (NNs) is introduced to overcome the design difficulty from the non-strict-feedback structure. The Lyapunov–Krasovskii functional is used for control design and stability analysis. Further, a backstepping-based adaptive neural control strategy is proposed. The suggested adaptive neural controller guarantees that all the closed-loop signals are semi-globally uniformly ultimately bounded (SGUUB) and the tracking error converges to a small neighborhood of the origin. Simulation results demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

An adaptive decentralized output-feedback control problem is investigated for a class of pure-feedback large-scale nonlinear systems with mismatched uncertainties, unknown dead-zones and unknown virtual control coefficients. At the same time, the nonlinear interconnections involve time-varying delays. Because only output information can be obtained, a state observer is constructed first. By using the infinite approximation ability of the radial basis function neural networks, the difficulty caused by the unknown nonlinearities is successfully overcome. Based on the appropriate Lyapunov–Krasovskii functions, the time-delay terms are compensated. The unknown virtual control coefficients are disposed by the convex combination method. By combining the backstepping technique with decentralized control principle, the adaptive neural decentralized output-feedback controllers are constructed to guarantee all the signals of the resulting closed-loop systems are bounded. And meanwhile, the error signals can converge to a small neighborhood of the origin. The simulation examples are provided to test our results. [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]

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]