The snag of common linear filtered Inverse Control (AIC) method is modify to hold with the characteristic of non-linear object with time delay and corresponding filtered ∈-adaptive Algorithm based on Real-Time Recurrent Learning (RTRL) is presented to identify the parameters and d controller. The simulation result on a non model of "The R.O.V. Zeefakkel" and Adaptive PID control keeps the same dynamic response performance, and also mathematical model for ship Course keeping be discussed. A neural network adaptive filter is introduced for the removal of impulse noise in digital images.
This paper specially exhibits about the time delay and mean square stochastic differential equations in impetuous stabilization is analyzed. By projecting a delay differential inequality and using the stochastic analysis technique, a few present stage for mean square exponential stabilization are survived. It is express that an unstable stochastic delay system can be achieved some stability by impetuous. This example is also argued to derived the efficiency of the obtained results.
This paper is connected with the stability analysis of Discrete-Time recurrent neural networks (RNNs) with time delays as random variable drawn from some probability distribution. By introducing the variation probability of the time delay, a common delayed Discrete-Time RNN system is transformed into one with stochastic parameters. Improved condition for the mean square stability of these systems are obtained by employing new Lyapunov function and more techniques are used to achieves delay dependence. The merit of the proposed condition lies in its reduced conservation which is made possible by considering not only the range of the time delays but also the variation probability distribution.
This paper is concerned with a new development of a delay-robust stabilizing feedback control law for linear ordinary differential equation coupled with two linear first order hyperbolic equations in the actuation path. A second change of variables that reduce stabilization problem of the PDE-ODE system to that of a time-delay system for which a forecaster can be constructed. Hence, by choosing the pole placement on the ODE when constructing the forecaster, enabling a trade-off between convergence rate and delay-robustness. The proposed feedback law is finally proved to be robust to small delays in the actuation.
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