Global Robust Exponential Stability Research Articles

Robust stability of moving horizon estimation for continuous-time systems

Abstract We consider a moving horizon estimation (MHE) scheme involving a discounted least squares objective for general nonlinear continuous-time systems. Provided that the system is detectable (incrementally integral input/output-to-state stable, i-iIOSS), we show that there exists a sufficiently long estimation horizon that guarantees robust global exponential stability of the estimation error in a time-discounted L 2-to-L ∞ sense. In addition, we show that i-iIOSS Lyapunov functions can be efficiently constructed by verifying certain linear matrix inequality conditions. In combination, we propose a flexible Lyapunov-based MHE framework in continuous time, which particularly offers more tuning possibilities than its discrete-time analog, and provide sufficient conditions for stability that can be easily verified in practice. Our results are illustrated by a numerical example.

Global Exponential Robust Stability of Generalized Differential Dynamical System With Deviating Argument and Random Disturbance

Global Exponential Robust Stability of Generalized Differential Dynamical System With Deviating Argument and Random Disturbance

Global robust stability of fuzzy cellular neural networks with parameter uncertainties

<abstract><p>The global robust stability of uncertain delayed fuzzy cellular neural networks (UDFCNNs) was analyzed in this paper. The major results of this paper provided some new criteria for the existence and uniqueness of the equilibrium point of UDFCNN. Furthermore, suitable Lyapunov-Krasovskii functionals was designed for obtaining the adequate conditions for the global asymptotic robust stability and global exponential robust stability of UDFCNN. Finally, several numerical examples was provided to verify the validity of the results.</p></abstract>

Global robust exponential stability of interval BAM neural networks with multiple time-varying delays: A direct method based on system solutions

This paper analyzes global robust exponential stability of interval bidirectional associative memory (BAM) neural networks with multiple time-varying delays, proposes a direct method based on system solutions, and gives sufficient conditions under which interval BAM neural networks have a unique and globally robustly exponentially stable equilibrium point. This method not only avoids the difficult to set up any Lyapunov–Krasovskii functional, but also derives simpler global robust exponential stability criteria. Compared with the data from other literature, the robust exponential stability criteria obtained in this paper have been presented to have more merits theoretically and numerically.

Linkage-constraint criteria for robust exponential stability of nonlinear BAM system with derivative contraction coefficients and piecewise constant arguments

This paper presents linkage-constraint criteria to guarantee the robustness of global exponential stability (RoGES) for a class of generalized nonlinear bidirectional associative memory (BAM) systems in the presence of derivative contraction coefficients and piecewise constant arguments (NBAM-dp system). However, because of the appearance of the tricky derivative compression coefficients and coupling relation with piecewise constant arguments, the previous Gronwall inequality is not applicable here. Thus, by means of generalized Gronwall inequality and norm inequality techniques, an independent parameters and interdependent variables (IPIV) principle is designed to solve the implicit bivariate transcendental equations to provide the respective supremum of the two interference factors, and the major criteria for guaranteeing the RoGES for NBAM-dp systems are obtained accordingly. Meanwhile, according to the IPIV principle, the relationship between the above two interference factors reflected in the linkage-constraint criteria is ensured to be mutually constrained and dynamically interconnected. Finally, comparative simulations and auxiliary data are provided to demonstrate the effectiveness of the theoretical findings.

Novel robust stability criteria for uncertain parameter quaternionic neural networks with mixed delays: Whole quaternionic method

This paper concentrates on the robust stability of uncertain parameter quaternionic neural networks (QNNs) with both time-varying delays and infinite distributed delays. To this end, a derivative formula of quaternionic function’s norm is firstly established. Then, based on this formula, algebraic standards are obtained by employing M-matrix theory as well as analytical techniques to guarantee the global robust exponential stability of the considered QNNs. Particularly, different from most existing decomposition approaches, this whole quaternionic method can be used whether the QNNs are decomposable or not and greatly reduces computation cost. The utility of the easy-to-use results formulated in the form of quaternionic norm’s M-matrix is confirmed by three given instances with numerical simulation.

Robustness Analysis of Exponential Stability of Neutral-Type Nonlinear Systems With Multi-Interference

With a view to the unfavorable impact of the inevitable exogenous interferences for the practical engineering and signal transmission, here we focus on the robustness of global exponential stability for nonlinear dynamical systems subject to piecewise constant arguments, neutral terms and stochastic disturbances (SNPNDS). A new troublesome problem is that the neutral terms appeared in the derivative part affected on the other two interference factors is not a simple accumulation, so the Lipchitz condition is adopted to establish the ternary transcendental equations. However, different from previous transcendental equations with single or double variables, solving the transcendental equations with three variables becomes a bottleneck again. Hence, the special independent parameters & interdependent variables method targeted for SNPNDS is adopted here: firstly, all relative independent parameters are fixed. Next, the upper bounds of these three interdependent variables are orderly derived by their coupling relationship. Therein, the optimal constraint conditions for piecewise constant arguments and neutral terms are deduced. Through the strategies mentioned above, a class of algebraic problems of estimating three upper bounds by solving transcendental equations with three variables is settled. Besides, the main method ensures that the relationship built among these interference factors is mutually restrictive and dynamic. Meanwhile, the optimized constraints make the linkage effect more comprehensive and valid. Furthermore, the established mechanism is practical enough to be generalized to more multivariable systems. Finally, the numerical simulation comparisons are given to illustrate the validity of the derived results.

Robust Exponential Stability of Recurrent Neural Networks with Deviating Argument and Stochastic Disturbance

It is well known that deviating argument and stochastic disturbance may derail the stability of recurrent neural networks (RNNs). This paper discusses the robustness of global exponential stability (GES) of RNNs accompanied with deviating argument and stochastic disturbance. For a given global exponentially stable RNNs, it is interesting to know how much the length of the interval of piecewise function and the interference intensity so that the disturbed system may still be exponentially stable. The available upper boundary of the range of piecewise variables and the interference intensity in the disturbed RNNs to keep GES are the solutions of some transcendental equations. Finally, some examples are provided to demonstrate the efficacy of the inferential results.

Further research on exponential stability for quaternion-valued neural networks with mixed delays

This paper addresses the global exponential stability of a class of quaternion-valued neural networks (QVNNs) with mixed delays including time-varying delays and infinite distributed delays. Because of the noncommutativity of quaternion multiplication, the concerned quaternion-valued models separated into four real-valued parts to form the equivalent real-valued systems. Based on M-matrix properties and homomorphism mapping theories, some sufficient conditions are derived to guarantee the existence and uniqueness of the equilibrium point of the system. Conditions for ensuring the global exponential stability of the equilibrium point of the system are obtained on the basis of the vector Lyapunov function method instead of the linear matrix inequality method. Using a similar method, the mixed-delay QVNNs with parameter uncertainties are also studied, and the conditions for ensuring the global robust exponential stability of the system are established directly. The adopted approach and the obtained results in this paper complement already the existing ones. Finally, three numerical examples are provided to illustrate the feasibility and the less level conservatism of the main results.

Robust exponential point stabilization control of the high-speed underactuated unmanned marine vehicle with model asymmetry

Point stabilization control of a class of asymmetric underactuated high-speed unmanned marine vehicle is discussed, and a robust exponential stabilization control algorithm is proposed based on homogeneous theory, average system theory, and nonlinear backstepping technology. Firstly, point stabilization control problem of a high-speed underactuated unmanned marine vehicle with model asymmetry is formulated, and then global differential homeomorphism transformation is designed, in order to overcome the difficulties caused by unmanned marine vehicle with model asymmetry. Secondly, the control system is transformed into the standard form of homogeneous interference system by output state variable transformation design and input transformation design. A novel interference function is designed, and then difficulties caused by the higher order velocities in damping coefficients are solved, via homogeneous stability design and homogeneity degree analyzing the expansion of the designed new state variables. Thirdly, by introducing the virtual input of backstepping and the average system theory, point stabilization controller for the underactuated high-speed unmanned marine vehicle is proposed based on homogeneous theory, which could achieve global and periodic time-varying robust exponential stability, and then stability of the point stabilization control algorithm is proved by using homogeneous stability theory and average system stability theory. At last, the effectiveness and accuracy of the control algorithm proposed is verified by semi-physical simulation experiment carried out in our laboratory.

Robust Exponential Stability of Switched Complex-Valued Neural Networks with Interval Parameter Uncertainties and Impulses

In this paper, dynamic behavior analysis has been discussed for a class of switched complex-valued neural networks with interval parameter uncertainties and impulse disturbance. Sufficient conditions for guaranteeing the existence, uniqueness, and global robust exponential stability of the equilibrium point have been obtained by using the homomorphism mapping theorem, the scalar Lyapunov function method, the average dwell time method, and M-matrix theory. Since there is no result concerning the stability problem of switched neural networks defined in complex number domain, the stability results we describe in this paper generalize the existing ones. The effectiveness of the proposed results is illustrated by a numerical example.

Dynamics of interval Cohen‐Grossberg neural networks with time‐varying delays based on LMI computation

SummaryThe problem of the global robust exponential stability of delayed interval Cohen‐Grossberg neural networks is considered. By constructing suitable Lyapunov functional and using the linear matrix inequality (LMI) technique, some sufficient conditions are derived to ensure the existence, uniqueness and robust exponential stability of the equilibrium point. Based on LMI computation, numerical examples are employed to show that the new results are less restrictive and less conservative than some existing results in the literature.

Robust stability analysis and stabilisation of uncertain impulsive positive systems with time delay

ABSTRACTThis paper considers the robust stability and stabilisation of uncertain impulsive positive systems with delay in state. The robust global exponential stability criterion under ranged dwell-time is first established by employing an impulse-time-dependent copositive Lyapunov function. Extensions to the exponential stability of the considered system under constant, arbitrary, maximal and minimal dwell-time are then derived. To positively stabilise the considered system under ranged dwell-time, a method of state-feedback controller design is presented based on the derived stability condition. Several numerical examples illustrate the reduced conservatism and effectiveness of the obtained theoretical results.

Novel global robust exponential stability criterion for uncertain inertial-type BAM neural networks with discrete and distributed time-varying delays via Lagrange sense

In this paper, the global robust exponential stability problem for a class of uncertain inertial-type BAM neural networks with both time-varying delays is focused through Lagrange sense. The existence of time-varying delays in discrete and distributed terms is explored with the availability of lower and upper bounds of time-varying delays. Firstly, we transform the proposed inertial BAM neural networks to usual one. Secondly, by the aid of LKF, stability theory, integral inequality, some novel sufficient conditions for the global robust exponential stability of the addressed neural networks are obtained in terms of linear matrix inequalities, which can be easily tested in practice by utilizing LMI control toolbox in MATLAB software. Furthermore, many comparisons of proposed work are listed with some existing literatures to get less conservatism. Finally, two numerical examples are provided to demonstrate the advantages and superiority of our theoretical outcomes.

A New Global Robust Exponential Stability Criterion for H∞ Control of Uncertain Stochastic Neutral-type Neural Networks with Both Timevarying Delays

This paper mainly focuses on a novel H∞ control design to handle the global robust exponential stability problem for uncertain stochastic neutral-type neural networks (USNNNs) with mixed time-varying delays. Here the delays are assumed to be both discrete and distributed, which means that the lower and upper bounds can be derived. Firstly, we draw a control law for stabilized and stability of the neutral-type neural networks (NNNs). Secondly, by employing the Lyapunov-Krasovskii functional(LKF) theory, Jensen’s integral inequality, new required sufficient conditions for the global robust exponential stability of the given neural networks (NNs) are established in terms of delay-dependent linear matrix inequalities (LMIs), which can be easily checked in practice. The conditions obtained are expressed in terms of LMIs whose feasibility can be verified easily by MATLAB LMI control toolbox. Moreover, we have compared our work with previous one in the existing literature and showed that it reduces conservatism. Finally, one numerical example with their simulations is given to validate the effectiveness of our proposed theoretical results.

Extended robust global exponential stability for uncertain switched memristor-based neural networks with time-varying delays

This paper is concerned with the problem of global exponential stability for uncertain memristive-based neural networks (UMNNs) with time-varying delays and switching parameters subject to unstable subsystems. Different from most of the existing papers, the considered uncertain switched MNNs with discrete-delays are modeled as switched neural networks (SNNs) with uncertain time-varying parameters. Based on multiple Lyapunov–Krasovskii functional (MLF) approach, average dwell time (ADT) technique and mode-dependent average dwell time (MDADT) method, some LMIs-based stability criteria are derived to design the switching signal and guarantee the exponential stability of the considered uncertain switched neural networks. By exploring the mode-dependent property of each subsystem, all the subsystems are categorized into stable and unstable ones. The concerned SNNs with both stable and unstable subsystems are more general and applicable than the existing models of SNNs only view all subsystems being stable, thus getting less conservatism criteria. The proposed sufficient conditions can be simplified into the forms of LMIs for conveniently using Matlab LMI toolbox. Finally, two numerical examples are exploited to demonstrate the effectiveness and applicability of the proposed theoretical results.

Robust Global Adaptive Exponential Stabilization of Discrete-Time Systems With Application to Freeway Traffic Control

This paper is devoted to the development of adaptive control schemes for uncertain discrete-time systems, which guarantee robust global exponential convergence to the desired equilibrium point of the system. The proposed control scheme consists of a nominal feedback law, which achieves robust global exponential stability properties when the vector of the parameters is known, in conjunction with a nonlinear dead-beat observer. The proposed adaptive control scheme depends on certain parameter observability assumptions. The obtained results are applicable to highly nonlinear uncertain discrete-time systems with unknown constant parameters. The successful applicability of the obtained results to real control problems is demonstrated by the rigorous application of the proposed adaptive control scheme to uncertain freeway models. A provided example demonstrates the efficiency of the approach.

Dynamic Analysis of Hybrid Impulsive Delayed Neural Networks With Uncertainties.

Neural networks (NNs) have emerged as a powerful illustrative diagram for the brain. Unveiling the mechanism of neural-dynamic evolution is one of the crucial steps toward understanding how the brain works and evolves. Inspired by the universal existence of impulses in many real systems, this paper formulates a type of hybrid NNs (HNNs) with impulses, time delays, and interval uncertainties, and studies its global dynamic evolution by a robust interval analysis. The HNNs incorporate both continuous-time implementation and impulsive jump in mutual activations, where time delays and interval uncertainties are represented simultaneously. By constructing a Banach contraction mapping, the existence and uniqueness of the equilibrium of the HNN model are proved and analyzed in detail. Based on nonsmooth Lyapunov functions and delayed impulsive differential equations, new criteria are derived for ensuring the global robust exponential stability of the HNNs. Convergence analysis together with illustrative examples show the effectiveness of the theoretical results.

New Criteria on Exponential Stability for Stochastic Delay Differential Systems Based on Vector Lyapunov Function

This paper established the vector Razumikhin-type theorem on exponential stability for stochastic functional differential systems by the stochastic analysis techniques and the property of ${M}$ -Matrix. Several novel stability criteria with vector $ {\mathscr {L}}$ -operator differential inequalities for stochastic delay differential systems, especially some of which includes the cross-item, were obtained by means of the vector Razumikhin theorem. By applying these new results, the robustness of global exponential stability of delay recurrent neural networks to random disturbed has been analyzed.

Global exponential stabilisation of acyclic traffic networks

ABSTRACTThis work is devoted to the construction of explicit feedback control laws for the robust global exponential stabilisation of general uncertain discrete-time acyclic networks. We consider discrete-time uncertain network models, which satisfy very weak assumptions. The construction of the controllers and the rigorous proof of the robust global exponential stability for the closed-loop system are based on recently proposed vector-Lyapunov function criteria, as well as the fact that the network is acyclic. It is shown, in this study, that the latter requirement is necessary for the existence of a robust global exponential stabiliser of the desired uncongested equilibrium point of the network. Our main focus is on traffic networks and all assumptions are related to features appearing in traffic models. An illustrative example demonstrates the applicability of the obtained results to realistic traffic flow networks.