共查询到20条相似文献，搜索用时 31 毫秒
1.
Parameter-dependent robust stability for uncertain Markovian jump systems with time delay 总被引：1，自引：0，他引：1
In this paper, the problem of parameter-dependent robust stability analysis is addressed for uncertain Markovian jump linear systems (MJLSs) with polytopic parameter uncertainties and time-varying delay. By constructing parameter-dependent Lyapunov functional, some sufficient conditions are developed to enable robust exponential mean square stability for the systems. New parameter-dependent robust stability criteria for MJLSs are established in the form of linear matrix inequalities (LMIs), which can be solved efficiently by the interior-point algorithm. Finally, a numerical example is given to demonstrate the effectiveness of the proposed approach. 相似文献
2.
This paper considers the problem of visual servoing of planar robot manipulators in the presence of uncertainty associated with robot dynamics, camera system and Jacobian matrix. By using radial basis function neural networks, these uncertainties can be compensated effectively. Two kinds of robust visual servoing are proposed, one is for image uncertainties, another is for Jacobian matrix uncertainty. By Lyapunov method and input-to-state stability technique, we prove that these robust controllers with neural compensators are stable. Real-time experiments are presented to show the applicability of the approach presented in this paper. 相似文献
3.
This paper is concerned with the finite-time stabilization for a class of stochastic BAM neural networks with parameter uncertainties. Compared with the previous references, a continuous stabilizator is designed for stabilizing the states of stochastic BAM neural networks in finite time. Based on the finite-time stability theorem of stochastic nonlinear systems, several sufficient conditions are proposed for guaranteeing the finite-time stability of the controlled neural networks in probability. Meanwhile, the gains of the finite-time controller could be designed by solving some linear matrix inequalities. Furthermore, for the stochastic BAM neural networks with uncertain parameters, the problem of robust finite-time stabilization could also be ensured as well. Finally, two numerical examples are given to illustrate the effectiveness of the obtained theoretical results. 相似文献
4.
Xian Zhang Xinxiao Liu Yantao Wang Xin Wang 《Journal of The Franklin Institute》2019,356(7):4043-4060
In this paper, we will investigate the necessary conditions, described by the Lyapunov matrix, for the robust exponential stability for a class of linear uncertain systems with a single constant delay and time-invariant parametric uncertainties, which are some generalizations of the existing results on uncertain linear time-delay systems. As a medium step, several pivotal properties of parameter-dependent Lyapunov matrix are proposed, which set up the relationships between fundamental matrix and Lyapunov matrix for the considered system. In addition, to calculate the parameter-dependent Lyapunov matrix, we introduce the differential equation method and the Lagrange interpolation method, respectively. Furthermore, it is noted that the proposed necessary conditions can be used to estimate the range of time delay, when the linear uncertain time-delay system is robust exponential stability. Finally, the validity of the obtained theoretical results is illustrated via numerical examples. 相似文献
5.
Mathematical models are an approximate of physical systems and design procedures are only complete when modeling errors have been quantified. Uncertainties are incorporated in design procedure to compensate such discrepancies and to add robustness. This paper investigates the design problem of parameter-dependent switched observers for polytopic uncertain switched systems. State-space model is considered subject to time-varying uncertainties, and designated observer gains ensuring stability of overall system are also parameter-dependent. Synthesis procedure is demonstrated by employing ?∞ performance criteria which has become a standard for robust system design against external disturbances. This investigation is carried out in the framework of finite-time stability (FTS) and finite-time boundedness (FTB) which is the focus of researchers recently because of its apparent practical significance, especially after the emergent utilization of linear matrix inequalities. 相似文献
6.
This paper deals with the problem of the global robust asymptotic stability of the class of dynamical neural networks with multiple time delays. We propose a new alternative sufficient condition for the existence, uniqueness and global asymptotic stability of the equilibrium point under parameter uncertainties of the neural system. We first prove the existence and uniqueness of the equilibrium point by using the Homomorphic mapping theorem. Then, by employing a new Lyapunov functional, the Lyapunov stability theorem is used to establish the sufficient condition for the asymptotic stability of the equilibrium point. The obtained condition is independent of time delays and relies on the network parameters of the neural system only. Therefore, the equilibrium and stability properties of the delayed neural network can be easily checked. We also make a detailed comparison between our result and the previous corresponding results derived in the previous literature. This comparison proves that our result is new and improves some of the previously reported robust stability results. Some illustrative numerical examples are given to show the applicability and advantages of our result. 相似文献
7.
Suriguga Mongolian Yonggui Kao Changhong Wang Hongwei Xia 《Journal of The Franklin Institute》2021,358(1):877-894
This article is dedicated to the investigation of the stability problem of delayed stochastic generalized uncertain impulsive reaction-diffusion neural networks (SGUIRDNNs). Applying Lyapunov second method, several new robust mean square stability criteria on the equilibrium point of the delayed SGUIRDNNs are derived in terms of linear matrix inequalities (LMIs). At last, we provide a numerical example to verify the validity of our findings. 相似文献
8.
Yong-Gui Kao Ji-Feng Guo Chang-Hong Wang Xi-Qian Sun 《Journal of The Franklin Institute》2012,349(6):1972-1988
This paper is devoted to investigating the robust stochastic exponential stability for reaction-diffusion Cohen–Grossberg neural networks (RDCGNNs) with Markovian jumping parameters and mixed delays. The parameter uncertainties are assumed to be norm bounded. The delays are assumed to be time-varying and belong to a given interval, which means that the lower and upper bounds of interval time-varying delays are available. Some criteria for delay-dependent robust exponential stability of RDCGNNs with Markovian jumping parameters are established in terms of linear matrix inequalities (LMIs), which can be easily checked by utilizing Matlab LMI toolbox. Numerical examples are provided to demonstrate the efficiency of the proposed results. 相似文献
9.
This paper considers the synchronization problem of coupled chaotic neural networks with time delay in the leakage term and parametric uncertainties using sampled-data control. Motivated by the achievements from both the stability of neural networks with time delay in the leakage term and the synchronization issue of coupled chaotic neural networks with parametric uncertainties, Lyapunov stability theory combining with linear matrix inequalities is employed to derive sufficient criteria ensuring the coupled chaotic neural networks to be completely synchronous. This paper presents an illustrative example and uses simulated results of this example to show the feasibility and effectiveness of the proposed sampled-data controller. 相似文献
10.
Yuhua Xu Defeng Meng Chengrong Xie Guoqiao You Wuneng Zhou 《Journal of The Franklin Institute》2018,355(1):164-176
This paper deals with the synchronization control of a class of delayed neural networks using a fast fixed-time control theory. By employing Lyapunov stability theory, a novel sufficient criterion is derived such that two neural networks can be synchronized within a fixed-time. Compared with some existing results, the proposed controller can render two neural networks faster synchronized. A numerical example is given to demonstrate the effectiveness of the criterion. 相似文献
11.
This paper is concerned with the stability analysis problem for a class of delayed stochastic recurrent neural networks with both discrete and distributed time-varying delays. By constructing a suitable Lyapunov–Krasovskii functional, a linear matrix inequality (LMI) approach is developed to establish sufficient conditions to ensure the global, robust asymptotic stability for the addressed system in the mean square. The conditions obtained here are expressed in terms of LMIs whose feasibility can be checked easily by MATLAB LMI Control toolbox. In addition, two numerical examples with comparative results are given to justify the obtained stability results. 相似文献
12.
13.
Wei Jiang Hongli Wang Jinghui Lu Guangbin Cai Weiwei Qin 《Journal of The Franklin Institute》2017,354(12):4838-4860
This paper focuses on mixed-objective dynamic output feedback robust model predictive control (OFRMPC) for the synchronization of two identical discrete-time chaotic systems with polytopic uncertainties, energy bounded disturbances, and input constraint. Using active control strategy, the chaos synchronization is transformed into standard dynamic OFRMPC scenarios tractable through receding horizon min–max optimization. Utilizing the notion of quadratic boundedness, the augmented closed-loop stability is further characterized. Then, the concepts of mixed performance criteria are firstly incorporated into the dynamic OFRMPC scheme to guarantee both the robust stability and the disturbance attenuation ability while preserving better dynamical behaviors. Necessary and/or sufficient conditions for desired mixed-objective dynamic OFRMPC are formulated involving linear matrix inequalities (LMIs). Finally, two numerical examples are given to demonstrate the theoretical results. 相似文献
14.
In this paper, we investigate the problem of global exponential stability analysis for a class of delayed recurrent neural networks. This class includes Hopfield neural networks and cellular neural networks with interval time-delays. Improved exponential stability condition is derived by employing new Lyapunov-Krasovskii functional and the integral inequality. The developed stability criteria are delay dependent and characterized by linear matrix inequalities (LMIs). The developed results are less conservative than previous published ones in the literature, which are illustrated by representative numerical examples. 相似文献
15.
In this paper, the problem of stabilization for a class of switched delay systems with polytopic type uncertainties under asynchronous switching is investigated. When the switching of the controllers has a lag to the switching of subsystems, i.e. the switching signal of the switched controller involves delay, parameter-dependent Lyapunov functionals are constructed, which are allowed to increase during the running time of active subsystems with the mismatched controller. Based on the average dwell time method, sufficient conditions for exponential stability are developed for a class of switching signals. Finally, a river pollution control problem is given to demonstrate the feasibility and effectiveness of the proposed design techniques. 相似文献
16.
This paper deals with the problem of a new delay-dependent robust stability criteria for a class of mixed neutral and Lur’e systems. The system has time-varying uncertainties, interval time-varying delays and sector-bounded nonlinearity. The proposed method is based on Lyapunov method, a delay-dependent criterion for asymptotic stability is established in terms of linear matrix inequality (LMI). Numerical examples show the effectiveness of the proposed method. 相似文献
17.
《Journal of The Franklin Institute》2022,359(16):9129-9167
In this paper, we investigate the use of Model Predictive Control (MPC) applications for quasi-Linear Parameter Varying (qLPV) systems subject to faults along the input channels. We propose a Fault Tolerant Control (FTC) mechanism based on a robust state-feedback MPC synthesis, considering polytopic inclusions. In order to alleviate the numerical burden of the robust min-max procedure, we use small prediction horizons, in such a way that the solution becomes viable for real-time systems. The FTC system is able to tolerate time-varying saturation of the actuator, which may happen due to malfunctions. Recursive feasibility and poly-quadratic stability guarantees are ensured through the synthesis of adequate terminal ingredients. Accordingly, we present a catalogue of three different LMI remedies, considering: (a) parameter-independent ingredients, (b) a parameter-dependent terms and (c) a parameter-dependent maps that take into account bounded rates of parameter variation. An autonomous driving car example is used to illustrate the performances of the proposed technique, which is compared to other MPCs from the literature. The proposed FTC method is able to ensure good performances, obtained with reduced computational demand. 相似文献
18.
This paper deals with the problem of delay-dependent stability analysis for neural networks with time-varying delays. First, by constructing an augmented Lyapunov–Krasovskii functional and utilizing a generalized free-weighting matrix integral inequality, an improved stability criterion for the concerned network is derived in terms of linear matrix inequalities. Second, by considering a marginal augmented vector and modifying a Lyapunov–Krasovsii functional, a further enhanced stability criterion is presented. Third, a less conservative stability condition in which a relaxed inequality related to activation functions is added is introduced. Finally, three numerical examples are included to illustrate the advantage and validity of the proposed criteria. 相似文献
19.
Sabri Arik 《Journal of The Franklin Institute》2019,356(1):276-291
This paper investigates the problem for stability of neutral-type dynamical neural networks involving delay parameters. Different form the previously reported results, the states of the neurons involve multiple delays and time derivative of states of neurons include discrete time delays. The stability of such neural systems has not been given much attention in the past literature due to the difficulty of finding Lyapunov functionals which are suitable for stability analysis of this type of neural networks. This paper constructs a generalized Lyapunov functional by introducing new terms into the well-known Lyapunov functional that enables us to conduct a theoretical investigation into stability analysis of delayed neutral-type neural systems. Based on this modified novel Lyapunov functional, sufficient criteria are derived, which guarantee the existence, uniqueness and global asymptotic stability of the equilibrium point of the neutral-type neural networks with multiple delays in the states and discrete delays in the time derivative of the states. The applicability of the proposed stability conditions rely on testing two basic matrix properties. The constraints impose on the system matrices are determined by using nonsingular M-matrix condition, and the constraints imposed on the coefficients of the time derivative of the delayed state variables are derived by exploiting the vector-matrix norms. We also note that the obtained stability conditions have no involvement with the delay parameters and expressed in terms of nonlinear Lipschitz activation functions. We present a constructive numerical example for this class of neural networks to give a systematic procedure for determining the imposed conditions on the whole system parameters of the delayed neutral-type neural systems. 相似文献
20.
《Journal of The Franklin Institute》2022,359(1):12-26
Logical network is a special kind of positive systems with finite state space and logical operations. This paper analyzes the robust stability of switched delayed logical networks (SDLNs) with all unstable modes. The main mathematical tool is the semi-tensor product of matrices, which is used to convert the dynamics of SDLNs with all unstable modes into an equivalent algebraic state space representation. Following the algebraic state space representation framework, the concept of robustly stable parts is proposed, based on which, a new criterion is established for the robust stability of SDLNs with all unstable modes. Finally, two examples are given for the verification of main results. 相似文献