Papers by Keyword: Global Asymptotic Stability

Abstract: This paper considers the problem of globally asymptotic stability of the recurrent neural networks with time-varying delays. A linear matrix inequality (LMI) technology and Lyapunov functional method is employed by combing the means of the nonsmooth analysis. A few new sufficient conditions and criterions were proposed to ensure the delayed recurrent neural networks are uniqueness and globally asymptotic stability of their equilibrium point. A few simulation examples are presented to demonstrate the effectiveness of the results and to improve feasibility.

Abstract: Taking into account the different response speed of each part of the unmanned helicopter(UMH), established a two-timescale nonlinear model which contained the flapping and rotational dynamics. To overcome the problem that a number of terms cant be accounted in the state-dependent Riccati equation (SDRE) design, we employed a nonlinear feedforward compensator that is designed to match the vehicle response to the model used in the SDRE design. We improved SDRE technique and set up a control algorithm can guarantee the global asymptotic stability of the system. Proof was given. Numerical simulation results were listed to illustrate effectiveness of the algorithm.

Abstract: The Unmanned helicopter (UMH) movement was divided into two parts, namely, attitude and trajectory motion. And then a two-timescale nonlinear model was established. The paper improved and expanded state dependent riccati equation (SDRE) control approach, deriving analytical conditions for achieving global asymptotic stability with lyapunov stability theory. Proof was given. By combining improved SDRE control with nonlinear feed-forward compensation technique, the full envelop flight attitude control laws could be designed. On the basis of attitude control, trajectory controller was developed. Actual flight tests were carried out. Test results show that the control strategy is highly effective.

Abstract: In this paper, we address the difference equation xn=px_n-s+x_n-t/q+x_n-t n=0,1,... with positive initial conditions where s, t are distinct nonnegative integers, p, q > 0. Our results not only include some previously known results, but apply to some difference equations that have not been investigated so far.

Abstract: In this paper, the problem of global asymptotic stability in the mean square for stochastic fuzzy cellular neural networks (SFCNN) with time-varying delays is investigated. By constructing a newly proposed Lyapunov-Krasovskii function (LKF) and using Ito’s stochastic stability theory, a novel delay-dependent stability criterion is derived. The obtained stability result is helpful to design the stability of fuzzy cellular neural networks (FCNN) with time-varying delays when stochastic noise is taken into consideration. Since it is presented in terms of a linear matrix inequality (LMI), the sufficient condition is easy to be checked efficiently by utilizing some standard numerical packages such as the LMI Control Toolbox in Matlab. Finally, an illustrate example is given to verify the feasibility and usefulness of the proposed result.