Zengyun Wang

Robust Stability Criterion for Delayed Neural Networks with Discontinuous Activation Functions

The problem of global robust stability for a class of uncertain delayed neural networks with discontinuous activation functions has been discussed. The uncertainty is assumed to be of norm-bounded form. Based on Lyapunov–Krasovskii stability theory as well as Filippov theory, the conditions are expressed in terms of linear matrix inequality, which make them computationally efficient and flexible. An illustrative numerical example is also given to show the applicability and effectiveness of the proposed results.

Robust decentralized adaptive control for a class of uncertain neural networks with time-varying delays

This paper considers the robust control problem for a class of uncertain time-varying delayed neural networks, in which the activation function may be a discontinuous function. A robust decentralized adaptive sliding mode controller is proposed to guarantee the asymptotically stability of the system. The proposed controller, which does not dependent on the time delay, ensures the occurrence of the sliding manifold even when the system is undergoing parameter uncertainties and nonlinear input. Two numerical examples are given to show the effectiveness of the proposed controller.

A new delay-dependent H∞ control criterion for a class of discrete-time systems with time-varying state delays and input delays

This paper deals with delay-dependent H∞ control for discrete-time systems with time-varying state delays and input delays. A new finite-sum inequality is first established to derive a delay-dependent condition, under which the resulting closed-loop system via a state feedback is asymptotically stable with a prescribed H∞ noise attenuation level. Moreover, a modified iterative algorithm according to the previously published algorithm of Ghaoui and Oustry (IEEE Trans. Autom. Control, 1997, 42(8), 1171–1176) involving convex optimization is proposed to obtain a suboptimal H∞ controller. Two numerical examples are presented which show the effectiveness of the proposed method.

Studies of the effects of ion irradiation on ferroelectric domains of triglycine sulfate single crystals on a nanometer scale

Abstract The effects of low dose (109/cm2) ion irradiation on the ferroelectric domains and on the ferroelectric↔paraelectric phase transition of triglycine sulfate (TGS) single crystals has been studied by using variable temperature dynamic contact electrostatic force microscopy. The evolution with temperature of ferroelectric domains of virgin and ion irradiated samples was observed in situ. In general, the phase transition of ion unirradiated samples is similar to that of virgin samples, which can be well described by Landaus mean field theory. However, on ion irradiated samples, there is a strong asymmetry of spontaneous polarization P for positive and negative domains. This asymmetry is due to the defects produced by ion implantation.

GLOBAL ROBUST STABILITY OF TIME-DELAY SYSTEMS WITH DISCONTINUOUS ACTIVATION FUNCTIONS UNDER POLYTOPIC PARAMETER UNCERTAINTIES

This paper concerns the problem of global robust stability of a time-delay discontinuous system with a positive-defined connection matrix under polytopic-type uncertainty. In order to give the stability condition, we firstly address the existence of solution and equilibrium point based on the properties of M-matrix, Lyapunov-like approach and the theories of dierential equations with discontinuous right-hand side as introduced by Filippov. Second, we give the delay-independent and delay-dependent stability condition in terms of linear matrix inequalities (LMIs), and based on Lyapunov function and the properties of the convex sets. One numerical example demonstrate the validity of the proposed criteria.

Neural network-based robust H∞ decentralised control strategy with new learning algorithm for robot manipulators

In this paper, a new robust H∞ intelligent decentralised control (RHIDC) strategy is proposed for the trajectory tracking problem of robot manipulators. The proposed control system is comprised of a computed torque controller and neural robust controller with new learning algorithm. Based on Lyapunov stability theorem and inequality technology, it is shown that the proposed controller can guarantee stability of closed-loop error systems and satisfactory tracking performances. The proposed approach shows that computed torque control method is also valid for controlling uncertain robotic manipulators as long as compensative controller is appropriately designed.