New delay-dependent criterion for the stability of recurrent neural networks with time-varying delay

This paper is concerned with the global asymptotic stability of a class of recurrent neural networks with interval time-varying delay. By constructing a suitable Lyapunov functional, a new criterion is established to ensure the global asymptotic stability of the concerned neural networks, which can be expressed in the form of linear matrix inequality and independent of the size of derivative of time varying delay. Two numerical examples show the effectiveness of the obtained results. Supported by the National Natural Science Foundation of China (Grant Nos. 60534010, 60728307, 60774048, 60774093), the Program for Cheung Kong Scholars and Innovative Research Groups of China (Grant No. 60521003) and the National High-Tech Research & Development Program of China (Grant No. 2006AA04Z183), China Postdoctoral Sciencer Foundation (Grant No. 20080431150), and the Specialized Research Fund for the Doctoral Program of Higher Education of China (Grant No. 200801451096)     

Lyapunov-based boundary feedback control in multi-reach canals

This paper presents a Lyapunov-based approach to design the boundary feedback control for an openchannel network composed of a cascade of multi-reach canals, each described by a pair of Saint-Venant equations. The weighted sum of entropies of the multi-reaches is adopted to construct the Lyapunov function. The time derivative of the Lyapunov function is expressed by the water depth variations at the gate boundaries, based on which a class of boundary feedback controllers is presented to guarantee the local asymptotic closed-loop stability. The advantage of this approach is that only the water level depths at the gate boundaries are measured as the feedback. Supported by the National Natural Science Foundation of China (Grant Nos. 60504026, 60674041), and the National High-Tech Research & Development Program of China (Grant No. 2006AA04Z173)     

Statistical learning makes the hybridization of particle swarm and differential evolution more efficient—A novel hybrid optimizer

This brief paper reports a hybrid algorithm we developed recently to solve the global optimization problems of multimodal functions, by combining the advantages of two powerful population-based metaheuristics—differential evolution (DE) and particle swarm optimization (PSO). In the hybrid denoted by DEPSO, each individual in one generation chooses its evolution method, DE or PSO, in a statistical learning way. The choice depends on the relative success ratio of the two methods in a previous learning period. The proposed DEPSO is compared with its PSO and DE parents, two advanced DE variants one of which is suggested by the originators of DE, two advanced PSO variants one of which is acknowledged as a recent standard by PSO community, and also a previous DEPSO. Benchmark tests demonstrate that the DEPSO is more competent for the global optimization of multimodal functions due to its high optimization quality. Supported by the National Natural Science Foundation of China (Grant No. 60374069), and the Foundation of the Key Laboratory of Complex Systems and Intelligent Science, Institute of Automation, Chinese Academy of Sciences (Grant No. 20060104)     

基于遗传算法的PID控制系统

遗传算法是1962年由美国Michigan大学的Holland教授提出的模拟自然界遗传机制和生物进化论而形成的一种并行随机搜索最优化方法[1-3],在自动控制领域中得到了越来越广泛的应用。该文引入了"稳定区域算法"求取闭环系统稳定的PID控制器参数区间,并以此算法的计算结果限定进化算法的参数寻优区间,通过仿真试验取得了令人满意的控制效果。     

RAZUMIKHIN-TYPE THEOREMS OF NEUTRAL STOCHASTIC FUNCTIONAL DIFFERENTIAL EQUATIONS

The stability of stochastic functional differential equation with Markovian switching was studied by several authors,but there was almost no work on the stability of the neutral stochastic functional differential equations with Markovian switching.The aim of this article is to close this gap.The authors establish Razumikhin-type theorem of the neutral stochastic functional differential equations with Markovian switching,and those without Markovian switching.     

具有深度自适应估计的视觉伺服优化

在手眼机器人视觉伺服中,如何确定机器人末端摄像机移动的速度和对物体的深度进行有效的估计还没有较好的解决方法.本文采用一般模型法,通过求解最优化控制问题来设计摄像机的速度,同时,利用物体初始及期望位置的深度估计值,提出了一种自适应估计的算法对物体的深度进行估计,给出了深度变化趋势,实现了基于图像的定位控制.该方法能够使机器人在工作空间范围内从任一初始位置出发到达期望位置,实现了系统的全局渐近稳定且不需要物体的几何模型及深度的精确值.最后给出的仿真实例表明了本方法的有效性.     

线性离散时滞重复过程的H∞控制

首先给出了线性离散时滞重复过程的2D(两维)Roesser模型, 采用线性矩阵不等式方法导出了过程稳定并具有H_∞扰动抑制度的一个充分条件. 通过求一个线性矩阵不等式的可行解来构造系统的一个状态反馈H_∞控制器. 进一步, 通过求解一个线性矩阵不等式凸优化问题得到该过程的最优状态反馈H_∞控制器.     

On the model-based networked control for singularly perturbed systems

In this paper, the control of a two-time-scale plant, where the sensor is connected to a linear controller/actuator via a network is addressed. The slow and fast systems of singularly perturbed systems are used to produce an estimate of the plant state behavior between transmission times, by which one can reduce the usage of the network. The approximate solutions of the whole systems are derived and it is shown that the whole systems via the network control are generally asymptotically stable as long as their slow and fast systems are both stable. These results are also extended to the case of network delay.     

高温环境下测试信号传输的稳定性分析

针对测试过程中传输线受高温影响导致信号传输产生一定的不确定性的问题,提出了热电耦合的方法来研究导体的温度演变过程,计算温度变化影响下的电信号稳定性传输,并建立误差公式估算输入与输出电信号之间的关系。采用有限元方法建立了分析模型,讨论了外部环境与传输线的对流换热和通过电流时产生的焦耳热对导体产生的共同作用,以及影响电信号的稳定性传输的主要因素。此分析方法为提高高温环境下的结构测试的准确性提供了依据,对实际工程具有一定的指导意义。     

多分量力传感器的误差补偿与稳定性考核

为解决杯突试验机夹紧力的校准问题,采用单体式多分量结构设计新型测力传感器,同时采用电路补偿和软件对非线性误差修正来提高传感器的准确度,采用计算机测量系统对多分量力传感器的重复性和稳定性进行考核。测量结果表明:设计的多分量力传感器各分量的重复性不大于0.07%,而长期稳定性优于0.26%。