一类时滞混沌忆阻器神经网络的延迟反同步控制

研究了一类时滞混沌忆阻器神经网络的延迟反同步控制问题．通过构造李亚普诺夫函数及采用微分包含理论和Halanay不等式的研究方法,设计了一个线性反馈控制器,并恰当选择控制器增益实现了一类混沌忆阻器神经网络驱动系统与响应系统之间的延迟反同步,所设计的控制器简单并易于实现．最后,仿真例子验证了所设计的控制器的有效性．     

一类非线性系统的神经网络L2鲁棒自适应控制

对于一类具有三角结构的单输入单输出不确定非线性系统的跟踪控制问题,用反步法和动态面控制方法设计了一种神经网络L2鲁棒自适应控制器.控制器设计中没有直接解HJI(Hamilton-Jacobi-Isaac)不等式,而是合理地选择了L2增益性能指标,将被控系统各个状态变量的跟踪误差和神经网络各权值的跟踪误差看作整个控制系统的各个状态变量,并用李亚普诺夫定理和HJI不等式证明了使用提出的摔制器后,这些状态变量具有小于等于事先规定的正实数y的L2增益,并且当所考虑的干扰向量为零向量时,提出的控制器在原点大范围渐近稳定.仿真研究结果表明所提出的控制器具有很好的跟踪性能和很强的鲁棒性.     

Quantized Control for Synchronization of Delayed Fractional-Order Memristive Neural Networks

Neural Processing Letters - This research addresses the synchronization of delayed fractional-order memristive neural networks (DFMNNs) via quantized control. The motivations are twofold: (1) the...     

Synchronization of Non-Identical Unknown Chaotic Delayed Neural Networks Based on Adaptive Sliding Mode Control

The synchronization problem is studied in this paper for non-identical chaotic neural networks with time delays and fully unknown parameters, where the mismatched parameters, activation functions and neural network architectures are taken into account. To overcome the difficulty that complete synchronization of non-identical chaotic neural networks cannot be achieved only by utilizing output feedback control, we design an adaptive sliding mode controller to realize the synchronization. Our synchronization criteria are easily verified and do not need to solve any linear matrix inequality. These results generalize a few previous known results and remove some restrictions on the parameters, activation functions and neural network architectures. This paper also presents an illustrative example and uses simulated results of this example to show the feasibility and effectiveness of the proposed scheme.     

基于神经网络的鲁棒自适应控制

考虑摩擦及外界干扰的情况下，针对具有不确定性参数的机器人系统，提出一种基于神经网络动态补偿的鲁棒自适应控制策略，采用神经网络在线补偿控制器以克服系统的外部扰动，未建模动力学部分等非参数不定性带来的影响，从而提高了系统的动态性能和稳态精度，并对闭环系统稳定性进行了证明，仿真结果表明，所提方法具有良好的跟踪性能和较强的鲁棒性。     

Robust Synchronization of an Array of Coupled Stochastic Discrete-Time Delayed Neural Networks

This paper is concerned with the robust synchronization problem for an array of coupled stochastic discrete-time neural networks with time-varying delay. The individual neural network is subject to parameter uncertainty, stochastic disturbance, and time-varying delay, where the norm-bounded parameter uncertainties exist in both the state and weight matrices, the stochastic disturbance is in the form of a scalar Wiener process, and the time delay enters into the activation function. For the array of coupled neural networks, the constant coupling and delayed coupling are simultaneously considered. We aim to establish easy-to-verify conditions under which the addressed neural networks are synchronized. By using the Kronecker product as an effective tool, a linear matrix inequality (LMI) approach is developed to derive several sufficient criteria ensuring the coupled delayed neural networks to be globally, robustly, exponentially synchronized in the mean square. The LMI-based conditions obtained are dependent not only on the lower bound but also on the upper bound of the time-varying delay, and can be solved efficiently via the Matlab LMI Toolbox. Two numerical examples are given to demonstrate the usefulness of the proposed synchronization scheme.      

一个基于神经网络模型的鲁棒自适应控制算法

对于具有不确定因素的离散非线性动态系统,通过校正神经网络预报器的输出,运用加权预报控制性能指标和网络辨识器模型局部线性化的思想,提出了一个间接鲁棒自适应神经网络控制算法,仿真研究证实了该控制策略的鲁棒性和有效性.     

Robust Adaptive Sliding Mode Neural Networks Control for Industrial Robot Manipulators

International Journal of Control, Automation and Systems - This paper proposes an original robust adaptive controller by using Radial Basis Function Neural networks (RBFNNs) for industrial robot...     

Robust State Estimation for Delayed Complex-Valued Neural Networks

This paper is concerned with the state estimation problem for the uncertain complex-valued neural networks with time delays. The parameter uncertainties are assumed to be norm-bounded. Through available output measurements containing nonlinear Lipschitz-like terms, we aim to design a state estimator to estimate the complex-valued network such that, for all admissible parameter uncertainties and time delay, the dynamics of the error-state system is guaranteed to be globally asymptotically stable. In addition, the case that there are no parameter uncertainties is also considered. By utilizing the Lyapunov functional method and matrix inequality techniques, some sufficient delay-dependent criteria are derived to assure the existence of the desired estimator gains. Finally, two numerical examples with simulations are presented to demonstrate the effectiveness of the proposed estimation schemes.     

基于神经网络的不确定非线性系统的鲁棒控制

利用神经网络和Ｈ＾∞控制理论对一类不确定非线性系统提出一种新的鲁棒控制器。该方法通过在线调整网络权来改善系统的暂态性能,不要求网络的离线学习过程和先验逼近误差界的知识,并可保证闭环系统的稳定性。仿真结果表明所提出方法的有效性。     

Fixed-Time Lag Synchronization Analysis for Delayed Memristor-Based Neural Networks

Neural Processing Letters - In this paper, the fixed-time lag synchronization for a general class of memristor-based neural networks (MNNs) with time delays is considered. Under the extended...     

Impulsive Control and Synchronization for Delayed Neural Networks With Reaction–Diffusion Terms

This paper discuss the global exponential stability and synchronization of the delayed reaction–diffusion neural networks with Dirichlet boundary conditions under the impulsive control in terms of $p$-norm and point out the fact that there is no constant equilibrium point other than the origin for the reaction–diffusion neural networks with Dirichlet boundary conditions. Some new and useful conditions dependent on the diffusion coefficients are obtained to guarantee the global exponential stability and synchronization of the addressed neural networks under the impulsive controllers we assumed. Finally, some numerical examples are given to demonstrate the effectiveness of the proposed control methods.      

Synchronization of a Class of Improved Neural Networks Based on Periodic Intermittent Control

In this paper, the synchronization of a class of improved neural networks with variable time delay is concerned via intermittent control. Firstly, a class of modified delayed neural networks is introduced, in which communication delays between different neurons are considered and the intra-neuron delays are negligible. Secondly, using Lyapunov functional theory, inequality techniques, and multi-parameter method, the synchronization criteria are established based on p-norm via intermittent control, and a feasible synchronization control region is given. Besides, by means of the component analysis method, the reduction to absurdity, mathematical induction, the exponential synchronization of the addressed networks is discussed in terms of the infinite norm, some criteria and the feasible control region of synchronization are also derived. Finally, some numerical examples are provided to show the validity and effectiveness of the theoretical results.     

Robust Mittag-Leffler Synchronization for Uncertain Fractional-Order Discontinuous Neural Networks via Non-fragile Control Strategy

This paper deals with the global robust non-fragile Mittag-Leffler synchronization issue for uncertain fractional-order neural networks with discontinuous activations. Firstly, a new inequality, which is concerned with the fractional derivative of the variable upper limit integral for the non-smooth integrable functional, is developed, and to be applied in the main results analysis. Then, the appropriate non-fragile controller with two types of gain perturbations is designed, and the global asymptotical stability is discussed for the synchronization error dynamical system by applying Lyapunov functional approach, non-smooth analysis theory and inequality analysis technique. In addition, the robust non-fragile Mittag-Leffler synchronization conditions are addressed in terms of linear matrix inequalities. Finally, two numerical examples are given to demonstrate the feasibility of the proposed non-fragile controller and the validity of the theoretical results.     

一类不确定奇异摄动系统的全局鲁棒控制

研究一类具有脉冲模态及非线性不确定扰动项的奇异摄动系统的全局鲁棒控制问题.通过引入一个输出反馈,对系统进行预处理消去系统中的脉冲模记,然后利用李亚普诺夫稳定性理论、矩阵分析理论及不等式技巧,通过将系统分解为慢时标上的退化系统和快时标上的边界层系统的2个子系统,再分别对这2个子系统设计稳定控制器,在此基础上获得了可行性和...     

Exponential Synchronization of Delayed Neural Networks with Actuator Failure Using Stochastic Sampled-data Control

International Journal of Control, Automation and Systems - This paper investigates the exponential synchronization issue for delayed neural networks with stochastic sampling. The variable sampling...     

Decentralized Robust Adaptive Control for the Multiagent System Consensus Problem Using Neural Networks

A robust adaptive control approach is proposed to solve the consensus problem of multiagent systems. Compared with the previous work, the agent's dynamics includes the uncertainties and external disturbances, which is more practical in real-world applications. Due to the approximation capability of neural networks, the uncertain dynamics is compensated by the adaptive neural network scheme. The effects of the approximation error and external disturbances are counteracted by employing the robustness signal. The proposed algorithm is decentralized because the controller for each agent only utilizes the information of its neighbor agents. By the theoretical analysis, it is proved that the consensus error can be reduced as small as desired. The proposed method is then extended to two cases: agents form a prescribed formation, and agents have the higher order dynamics. Finally, simulation examples are given to demonstrate the satisfactory performance of the proposed method.     

具有切换拓扑的时滞耦合网络的同步控制

针对一类具有耦合时滞且耦合拓扑集合给定的动态复杂网络模型存在同步的要求,传统的网络同步不能通过单一拓扑结构实现时,如何通过拓扑之间的切换来实现网络的同步.为解决上述问题,提出构建合适的Lyapunov函数给出了网络同步所需要满足的条件和相应的切换规则.进行仿真的结果与已有的研究结果相比,动态复杂网络的耦合矩阵可为一般的形式,并不需要满足同时上三角化和同时对角化等条件.仿真结果验证了结论的有效性和正确性.     

不匹配不确定线性时滞系统的鲁棒自适应控制

对一类同时具有匹配不确定性及结构确定不匹配不确定性的不确定时滞系统进行鲁棒自适应控制。首先,采用李雅谱诺夫函数方法,结合基于线性矩阵不等式的鲁棒控制器设计方法和变结构控制方法,设计鲁棒控制器,保证闭环系统的二次渐近稳定。利用自适应参数估计方法,设计具有匹配不确定性范数界估计能力的鲁棒自适应控制器,保证闭环系统的一致终结有界。结合算例,进行控制器的设计和仿真研究,验证所提出的设计方法的有效性。