Juntao Fei

Robust delay-dependent exponential stability for uncertain stochastic neural networks with mixed delays

This paper is concerned with the robust delay-dependent exponential stability of uncertain stochastic neural networks (SNNs) with mixed delays. Based on a novel Lyapunov-Krasovskii functional method, some new delay-dependent stability conditions are presented in terms of linear matrix inequalities, which guarantee the uncertain stochastic neural networks with mixed delays to be robustly exponentially stable. Numerical examples are given to illustrate the effectiveness of our results.

New Results on Robust Exponential Stability of Uncertain Stochastic Neural Networks with Mixed Time-Varying Delays

This letter considers the robust exponential stability of uncertain stochastic neural networks with mixed time-varying delays. By using Lyapunov–Krasovskii functional and Itô’s differential formula, several new sufficient conditions guaranteeing the global robust exponential stability are derived in terms of linear matrix inequalities. Numerical examples are given to illustrate the effectiveness and less conservativeness of our results.

Robust delay-range-dependent non-fragile H∞ filtering for uncertain neutral stochastic systems with Markovian switching and mode-dependent time delays

Abstract This paper deals with the problem of robust non-fragile H ∞ filter design of uncertain neutral stochastic system with Markovian jumping parameters and mode-dependent time delays. The parameter uncertainties are assumed to be norm-bounded. The time delays depend on system modes and vary in an interval. Attention is focused on the design of a non-fragile filter that guarantees the exponential mean-square stability and a prescribed H ∞ performance level of the filtering error systems. A delay-range-dependent stability condition in terms of linear matrix inequality(LMI) is derived for the solvability of this problem. Numerical examples compared with the existing results are provided to demonstrate the less complicated and less conservative of the proposed method.

Adaptive fractional order sliding mode controller with neural estimator

Abstract In this study, an adaptive fractional order sliding mode controller with a neural estimator is proposed for a class of systems with nonlinear disturbances. Compared with traditional sliding mode controller, the new proposed fractional order sliding mode controller contains a fractional order term in the sliding surface. The fractional order sliding surface is used in adaptive laws which are derived in the framework of Lyapunov stability theory. The bound of the disturbances is estimated by a radial basis function neural network to relax the requirement of disturbance bound. To investigate the effectiveness of the proposed adaptive neural fractional order sliding mode controller, the methodology is applied to a Z-axis Micro-Electro-Mechanical System (MEMS) gyroscope to control the vibrating dynamics of the proof mass. Simulation results demonstrate that the proposed control system can improve tracking performance as well as parameter identification performance.

Stability analysis of stochastic Markovian switching static neural networks with asynchronous mode-dependent delays

Abstract This paper is concerned with the asymptotic stability analysis for stochastic static neural networks with mode-dependent time-varying delays, in which the delay modes and the system modes are asynchronous. That is, they depend on different jumping modes. In addition, the derivatives of the mode-dependent time-varying delays are no longer required to be smaller than one. By constructing new Lyapunov–Krasovskii functional and combining with a convex polyhedron method, several delay-dependent stability conditions are formulated based on linear matrix inequalities (LMIs). The usefulness of the proposed approach are finally demonstrated by two numerical examples.

Dynamic global proportional integral derivative sliding mode control using radial basis function neural compensator for three-phase active power filter

An adaptive dynamic special global sliding mode controller that is based on proportional integral derivative (PID) sliding surface using radial basis function (RBF) neural network (NN) for a three- phase active power filter (APF) was presented in this paper. To overcome the problems associated with the schemes of the conventional sliding mode control, a global PID sliding manifold is introduced to realize the whole process of robustness and inhibition of the steady state error, accelerating the system response meanwhile. In addition, the nested dynamic sliding mode controller can reduce the influence of chattering that may lead to malfunction of the insulated gate bipolar transistor (IGBT) caused by sign function in the control law, achieving a better property. Moreover, owing to the parameter uncertainties and the external disturbances, a RBF neural estimator is added to eliminate the chattering phenomenon that further optimizes the performance of the system. Eventually, simulation studies in the MATLAB/SimPower Systems Toolbox verify the outstanding performance of the designed RBFNN dynamic global PID sliding mode controller in three different conditions, and some comparisons are made at the same time to demonstrate the excellent properties of the raised control method.

Robust adaptive nonsingular terminal sliding mode control of MEMS gyroscope using fuzzy-neural-network compensator

To attenuate the effect of time-varying parameters, quadrature errors, and external disturbances and realize finite-time control, a robust adaptive nonsingular terminal sliding mode (NTSM) tracking control scheme using fuzzy-neural-network (FNN) compensator is presented for micro-electro-mechanical systems (MEMS) vibratory gyroscopes in this paper. By introducing a nonsingular terminal sliding mode manifold, a novel terminal sliding mode controller is designed for MEMS gyroscopes, while ensuring the control system could reach the sliding surface and converge to equilibrium point in a finite period of time from any initial state. In the presence of unknown model uncertainties and external disturbances, an adaptive fuzzy-neural-network controller is employed to compensate such system nonlinearities and improve the tracking performance. Online fuzzy-neural-network weight tuning algorithms are derived in the sense of Lyapunov stability theorem to guarantee the network convergence as well as stable control performance. Numerical simulations for a MEMS gyroscope are provided to justify the claims of the proposed adaptive fuzzy-neural-network control scheme and demonstrate the satisfactory tracking performance and robustness.

Delay-dependent L2L filtering for fuzzy neutral stochastic time-delay systems

L2L filter design are firstly investigated for fuzzy neutral stochastic systems.Based on the LyapunovKrasovskii functional and the Jensen equality in the neutral stochastic setting, the L2L filtering problem for fuzzy neutral stochastic systems is considered.The sufficient condition for the existence of an exponential L2L filter are less conservative. The delay-dependent L2L filter design for a class of TakagiSugeno fuzzy neutral stochastic time-delay systems is considered in this paper. Based on the LyapunovKrasovskii functional, this paper presents a novel approach to design a fuzzy filter such that the filtering error systems are exponentially stable in the mean-square with a prescribed L2L performance index. Delay-dependent sufficient conditions for the existence of L2L filters are obtained in terms of linear matrix inequalities (LMIs). Three examples are finally given to demonstrate the effectiveness and advantages of the proposed approach.

State estimation for uncertain discrete-time stochastic neural networks with Markovian jump parameters and time-varying delays

The state estimation problem is considered for a class of discrete-time stochastic neural networks with Markovian jumping parameters in this paper. Norm-bounded parameter uncertainties in the state and measurement equation and time-varying delays are investigated. The neuron activation function satisfies sector-bounded conditions, and the nonlinear perturbation of the measurement equation satisfies standard Lipschitz condition and sector-bounded conditions. By constructing proper Lyapunov–Krasovskii functional, delay-dependent conditions are developed in terms of linear matrix inequalities (LMIs) to estimate the neuron state such that the dynamic of the estimation error system is asymptotically stable. Finally, numerical examples are shown to demonstrate the effectiveness and applicability of the proposed design method.

Adaptive neural dynamic global PID sliding mode control for MEMS gyroscope

In this paper, a dynamic global proportional integral derivative (PID) sliding mode controller based on an adaptive radial basis function (RBF) neural estimator is developed to guarantee the stability and robustness in the presence of a lumped uncertainty for a micro electromechanical systems (MEMS) gyroscope. This approach gives a new dynamic global PID sliding mode manifold, which not only enables system trajectory to run on the global sliding mode surface at the start point more quickly and eliminates the reaching phase of the conventional sliding mode control, but also restrains the steady-state error and reduces the chattering via a dynamic PID sliding surface. A RBF neural network (NN) system is employed to estimate the lumped uncertainty and eliminate the chattering phenomenon at the same time. Additionally, adaptive laws and dynamic global PID sliding control gains that ensure the asymptotic stability of the close-loop system are proposed, together with the techniques for deciding which kind of basis function should be selected. Finally, simulation results demonstrate the effectiveness of RBFNN dynamic global PID sliding mode control method, meanwhile some comparisons are made to verify the good properties of the suggested control approach.