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This paper presents an adaptive control strategy for a unified chaotic system with unknown functions and full state constraints. The unknown functions in the unified chaotic system are approximated by using the radial basis function neural networks. At present, a great many of the results for chaotic system neglect the situation of full state constraints. In the proposed scheme, we successfully utilize the barrier Lyapunov function approach to prevent the full state from violating constraint condition. In addition, when the full state constraints are considered, the computation online burden is very large in the previous works. In contrast with the existing results for unified chaotic system, the number of the adaptation laws has only two. The state feedback controllers and the adaptive laws for estimating the uncertainties were derived based on the Lyapunov stability theory. Finally, it is proved that all the signals in the unified chaotic system are bounded and the constraints are not violated. The performance of the proposed scheme was validated by using a simulation example. [ABSTRACT FROM AUTHOR]

An adaptive control scheme is studied for unified chaotic systems with unknown function and dead-zone input. Because uncertain nonlinear property is included in the considered unified chaotic systems, the neural networks are used to approximate the uncertainties. An adaptive technique is employed to construct the neural controllers and compensate for the dead-zone parameters. By using the scheme, the chaotic phenomena for unified chaotic systems are overcome. It is proven that the proposed algorithm can guarantee that all the signals in the closed-loop system are bounded and the system states can converge to a neighborhood of zero based on the Lyapunov analysis method. The simulation example for a unified chaotic system is provided to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper studies an adaptive output-feedback control for a class of nonlinear single-input and single-output (SISO) systems with the full-state constraints. A state observer is designed to estimate those unmeasured states. At present, all the results in the output-feedback area ignore the effects of the full-state constraints. The presence of these constraints results in a complicated procedure and the major difficulties in the design. The barrier Lyapunov function (BLF) and a novel design procedure are given to overcome these difficulties. The adaptation law and the controllers are obtained based on the backstepping design procedure. In addition, only one adjustable parameter needs to be updated, and thus, the online computation burden is alleviated. The stability of the closed-loop system is proven by using the Lyapunov theorem. A simulation example is given to verify the effectiveness of the approach. [ABSTRACT FROM PUBLISHER]

In this paper, an adaptive controller is investigated for a class of uncertain single-input-single-output nonlinear systems preceded by an unknown backlash-type hysteresis nonlinearity, which is modeled by a differential equation. Backstepping design procedure is employed to construct the control input and adaptation laws. A new algorithm is designed to reduce the computational burden compared with previous work for unknown backlash-type hysteresis. Based on Lyapunov stability theory, all the signals in the closed-loop system are bounded, and the system output can track the reference signal to a bounded compact set. An example for hard disk drives with hysteresis friction nonlinearity is given to verify the feasibility of the proposed approach. [ABSTRACT FROM AUTHOR]

Online travel reservation has become a consumption pattern of tourism product that gains increasing favors from consumers, with obvious growth volume in hotel online reservation. Currently, in this market, there are both models of travel online business agents and hotel official websites and we witness more and more fierce in their competition. Is there any significant difference in terms of their price? Do different types of hotels show the same outcome? Based on this background we make an empirical study of the price competition of hotel online reservation in China by using paired-sampled T-test method and deeply analyzed the price-fixing regulation and the cause of the formation of these two kinds of models on online reservation channels. Corresponding suggestions are thus put forward. [ABSTRACT FROM AUTHOR]