Robust Passivity Research Articles

Passivity analysis of spatially and temporally BAM neural networks with the Neumann boundary conditions

In the paper, a delay-differential equation modeling a bidirectional associative memory neural networks (BAM NNs) with reaction-diffusion terms is investigated, for which the input and output variables are varied with the time and space variables. By taking advantage of the inequality techniques, some Lyapunov-Krasovskii functional candidates are introduced to arrive at the novel sufficient conditions that warrant the passivity of spatially and temporally BAM NNs with mixed time delays. Moreover, when the parameter uncertainties appear in spatially and temporally BAM NNs, the criterion for robust passivity is also given. Novel passivity criteria are proposed in terms of inequalities, which can be checked easily. A numerical example is provided to demonstrate the effectiveness of the proposed results.

Robust passivity analysis for stochastic impulsive neural networks with leakage and additive time-varying delay components

The purpose of this paper is to investigate the problem of robust passivity analysis for delayed stochastic impulsive neural networks with leakage and additive time-varying delays. The novel contribution of this paper lies in the consideration of a new integral inequality proved to be well-known Jensen’s inequality and takes fully the relationship between the terms in the Leibniz–Newton formula within the framework of linear matrix inequalities (LMIs). By constructing a suitable Lyapunov–Krasovskii functional with triple and four integral terms using Jensen’s inequality, integral inequality technique and LMI frame work, which guarantees stability for the passivity of addressed neural networks. This LMI can be easily solved via convex optimization techniques. Finally, two interesting numerical examples are given to show the effectiveness of the theoretical results.

Robust passivity, feedback passification and global robust stabilisation for switched non‐linear systems with structural uncertainty

This study examines the issues of robust passivity, feedback passification and global stabilisation for a class of switched non-linear system with structural uncertainty. First, a sufficient condition for such a system to be robust passive is given by the design of a state-dependent switching law. Second, a state-dependent switching law and state feedback controllers are designed to render the resulting closed-loop system robust passive. Finally, a recursive feedback passification design technique is developed to achieve the global stabilisation problem when the subsystems have any uniform vector relative degree. A numerical example is presented to illustrate the effectiveness of the proposed approach.

New delay‐dependent global robust passivity analysis for stochastic neural networks with Markovian jumping parameters and interval time‐varying delays

The problem of passivity analysis for stochastic neural networks with Markovian jumping parameters and interval time‐varying delays is investigated in this article. By constructing a novel Lyapunov–Krasovskii functional based on the complete delay‐decomposing idea and using improved free‐weighting matrix method, some improved delay‐dependent passivity criteria are established in terms of linear matrix inequalities. Numerical examples are also given to show the effectiveness of the proposed methods. © 2015 Wiley Periodicals, Inc. Complexity 21: 167–179, 2016

Robust passivity based resilient control for networked control systems with random gain fluctuations

SummaryThe problem of resilient controller design for a class of networked control system based on passivity theory is presented. By using the Lyapunov–Krasovskii stability theory and linear matrix inequality approach, a set of sufficient conditions for the existence of state feedback controllers is derived. The networked control system under consideration is modeled by taking the network‐induced imperfections like packet dropouts and transmission delays as a single time‐varying delay. The controller is considered with a stochastic fluctuations in its gain matrix by using the Bernoulli distributed white sequence with time‐varying probability measures. The probability‐dependent sufficient conditions are established to guarantee the resulting closed‐loop system to be stochastically stable with a prescribed mixed and passivity performance criterion. The results are expressed in the form of convex optimization problem subject to the set of LMIs, which can be easily solved by using some standard numerical packages. Finally, a numerical example by using the high‐incidence research model is presented to illustrate the effectiveness and applicability of the theoretical results. Copyright © 2015 John Wiley & Sons, Ltd.

Observer-Based Robust Passive Control for a Class of Uncertain Neutral Systems: An Integral Sliding Mode Approach

The problem of integral sliding mode control (ISMC) with passivity is investigated for a class of uncertain neutral systems with time-varying delay (NTSTD) and external disturbance. The system states are unavailable. An ISMC strategy is proposed based on the state estimate. By employing a novel sliding functional, a new sufficient criterion of robust asymptotic stability and passivity for both the error system and the sliding mode (SM) dynamic system is derived via linear matrix inequality (LMI) technique. Then, a SM controller is synthesized to guarantee the reachability of the sliding surface predefined in the state estimate space. Finally, a numerical example shows the feasibility and superiority of the obtained result.

Robust passivity analysis of neural networks with discrete and distributed delays

This paper focuses on the problem of passivity of neural networks in the presence of discrete and distributed delay. By constructing an augmented Lyapunov functional and combining a new integral inequality with the reciprocally convex approach to estimate the derivative of the Lyapunov-Krasovskii functional, sufficient conditions are established to ensure the passivity of the considered neural networks, in which some useful information on the neuron activation function ignored in the existing literature is taken into account. Three numerical examples are provided to demonstrate the effectiveness and the merits of the proposed method.

Robust passivity analysis for discrete-time recurrent neural networks with mixed delays

This article considers the robust passivity analysis for a class of discrete-time recurrent neural networks (DRNNs) with mixed time-delays and uncertain parameters. The mixed time-delays that consist of both the discrete time-varying and distributed time-delays in a given range are presented, and the uncertain parameters are norm-bounded. The activation functions are assumed to be globally Lipschitz continuous. Based on new bounding technique and appropriate type of Lyapunov functional, a sufficient condition is investigated to guarantee the existence of the desired robust passivity condition for the DRNNs, which can be derived in terms of a family of linear matrix inequality (LMI). Some free-weighting matrices are introduced to reduce the conservatism of the criterion by using the bounding technique. A numerical example is given to illustrate the effectiveness and applicability.

Passivity analysis for uncertain discrete-time stochastic BAM neural networks with time-varying delays

This paper is concerned with the passivity analysis problem for a class of discrete-time stochastic bidirectional associative memory neural networks with time-varying delays. Furthermore, the results are extended to the robust passivity analysis with mixed time delays that consist of both the discrete and distributed time delays, and the uncertainties are assumed to be time-varying norm bounded parameter uncertainties. By constructing a new Lyapunov---Krasovskii functional and introducing some appropriate free-weighting matrices, a delay-dependent passivity criterion is derived in terms of LMIs whose feasibility can be easily checked by some available software packages. Finally, two numerical examples with simulation results are given to demonstrate the effectiveness and usefulness of the proposed results.

New Passivity Criteria for Fuzzy Bam Neural Networks with Markovian Jumping Parameters and Time-Varying Delays

This paper addresses the problem of passivity analysis issue for a class of fuzzy bidirectional associative memory (BAM) neural networks with Markovian jumping parameters and time varying delays. A set of sufficient conditions for the passiveness of the considered fuzzy BAM neural network model is derived in terms of linear matrix inequalities by using the delay fractioning technique together with the Lyapunov function approach. In addition, the uncertainties are inevitable in neural networks because of the existence of modeling errors and external disturbance. Further, this result is extended to study the robust passivity criteria for uncertain fuzzy BAM neural networks with time varying delays and uncertainties. These criteria are expressed in the form of linear matrix inequalities (LMIs), which can be efficiently solved via standard numerical software. Two numerical examples are provided to demonstrate the effectiveness of the obtained results.

Robust Passivity and Feedback Design for Nonlinear Stochastic Systems with Structural Uncertainty

This paper discusses the robust passivity and global stabilization problems for a class of uncertain nonlinear stochastic systems with structural uncertainties. A robust version of stochastic Kalman-Yakubovitch-Popov (KYP) lemma is established, which sustains the robust passivity of the system. Moreover, a robust strongly minimum phase system is defined, based on which the uncertain nonlinear stochastic system can be feedback equivalent to a robust passive system. Following with the robust passivity theory, a global stabilizing control is designed, which guarantees that the closed-loop system is globally asymptotically stable in probability (GASP). A numerical example is presented to illustrate the effectiveness of our results.

Robust and Passive Constrained Fuzzy Control for Discrete Fuzzy Systems with Multiplicative Noises and Interval Time Delay

The passive fuzzy control for discrete-time uncertain Takagi-Sugeno (T-S) fuzzy models with multiplicative noises and time delay is investigated subject to robust asymptotical stability. Applying Jensen’s inequality and free-weighting matrix technique, less conservative sufficient conditions are derived via choosing Lyapunov function to analyze and synthesize the robust asymptotical stability and passivity of closed-loop system. The derived conditions are not strictly linear matrix inequality (LMI) problems, thus the cone complementarity technique is employed to propose a suboptimal technique to solve the proposed nonstrictly LMI problems. An algorithm is developed in this paper to design the fuzzy controller which can be accomplished by state-feedback scheme or output-feedback scheme. Finally, numerical examples are provided to demonstrate the feasibility and applicability of the proposed fuzzy controller design technique.

Robust Passivity and Passification for a Class of Singularly Perturbed Nonlinear Systems with Time-Varying Delays and Polytopic Uncertainties via Neural Networks

This paper investigates the problem of robust passivity and passification for a class of singularly perturbed nonlinear systems (SPNS) with time-varying delays and polytopic uncertainties via neural networks. By constructing a proper functional and the linear matrix inequalities (LMIs) technique, some novel sufficient conditions are derived to make SPNS passive. The allowable perturbation bound ξ∗ can be determined via certain algebra inequalities, and the proposed controller based on neural network will make SPNS with polytopic uncertainties passive for all ξ∈(0,ξ∗). Finally, a numerical example is given to illustrate the theoretical results.

Delay-dependent passivity criteria for uncertain switched neural networks of neutral type with interval time-varying delay

This paper is concerned with the robust passivity analysis of uncertain switched neural networks of neutral type with interval time-varying delay. We first discuss the passivity conditions for the addressed model with norm bounded uncertainties and then extend this result to the case of interval uncertainties. For the neural networks under study, a generalized activation function is considered, where the traditional assumptions on the boundedness, monotonicity and differentiability of the activation functions are removed. Constructing a new Lyapunov–Krasovskii functional with triple integral terms and using a minimal number of free-weighting matrices, some passivity criteria are proposed in terms of linear matrix inequalities, which are dependent on the size of the time delay. Finally, some numerical examples are given to illustrate the effectiveness and merits of the developed techniques.

Maneuver Control and Vibration Suppression of a Smart Flexible Satellite Using Robust Passivity Based Control

In this paper a satellite with two flexible appendages and the piezoelectric layers which are attached to them and a central hub is considered. The piezoelectric layers are used as actuators. The governing equations of motion are derived based on Lagrange method. Using Rayleigh-Ritz technique ordinary differential equations of motion are obtained. A robust passivity based control is applied to the system to not only control the three axes maneuver of the satellite but also suppress the vibrations of the flexible appendages. Finally, the system is simulated and simulation results show the good performance of this controller.

Robust stability and robust passivity of parabolic complex networks with parametric uncertainties and time-varying delays

In this paper, the robust global exponential stability and robust passivity are investigated for a class of parabolic complex networks with multiple time-varying delays. By constructing appropriate Lyapunov functionals and utilizing inequality techniques, several criteria for robust global exponential stability and robust passivity are established. Finally, a numerical example is also given to illustrate the effectiveness of the obtained results.

이산시간 불확실 특이시스템의 저차 강인 피동성 필터링

In this paper, we consider the problem of a robust passive filtering with low-order for discrete-time singular systems with polytopic uncertainties. A BRL(bounded real lemma) for robust passivity with a dissipativity of discrete-time uncertain singular systems is derived. A low-order robust passive filter design method is proposed by new reduced-order method and LMI(linear matrix inequality) technique on the basis of the obtained BRL. Finally, illustrative examples are presented to show the applicability of the proposed method.

Robust passivity analysis of a class of discrete-time stochastic neural networks

This paper addresses the passivity problem of a class of discrete-time stochastic neural networks with time-varying delays and norm-bounded parameter uncertainties. New delay-dependent passivity conditions are obtained by using a novel Lyapunov functional together with the linear matrix inequality approach. Numerical examples show the effectiveness of the proposed method.

New robust passivity criteria for discrete-time genetic regulatory networks with Markovian jumping parameters

In this paper, we present some novel results for robust passivity of a class of uncertain discrete-time Markovian jumping genetic regulatory networks (GRNs) with time-varying delays. By constructing a new set of Lyapunov–Krasovskii functionals, together with the matrix inequality technique, we provide a new set of results for the passiveness, and also ensure the mean-square asymptotical stability (internally) of the considered GRNs about their equilibrium points for all time delays satisfying both the lower bound and upper bound of the interval time-varying delay. Further, the obtained results are extended to deal with the robust passiveness of the considered GRNs for all admissible uncertainties. The obtained conditions are derived in terms of linear matrix inequalities (LMIs), which can be easily verified via the LMI toolbox. Finally, numerical examples with simulation results are provided for the GRN model to illustrate the applicability and usefulness of the theory.

Passivity analysis and passification of uncertain Markovian jump systems with partially known transition rates and mode-dependent interval time-varying delays

In this paper, the problem of delay-dependent robust passivity analysis and robust passification of uncertain Markovian jump linear systems (MJLSs) with partially known transition rates and mode-dependent time-varying delays are investigated. In the deterministic model, the time-varying delay is in a given range and the uncertainties are assumed to be norm bounded. By constructing an appropriate Lyapunov–Krapunov functional (LKF) combining with Jensen’s inequality and the free-weighting matrix method, delay-dependent passification conditions are obtained in term of linear matrix inequalities(LMIs). For the robust passification problem, desired passification controllers are designed, which guarantee that the closed-loop MJLS is passive. Finally, a numerical example is given to illustrate the effectiveness of the proposed approach.