Stabilization of One‐Dimensional Schrödinger Equation with Variable Coefficient under Delayed Boundary Output Feedback

This article considers stabilization of a one‐dimensional Schrödinger equation with variable coefficient and boundary observation which suffers from an arbitrary given time delay. We design an observer and predictor to stabilize the system. The state is estimated in the time span where the observation is available, and also predicted in the time interval where the observation is not available. It is shown that the estimated state feedback stabilizes the system exponentially. A numerical simulation is presented to illustrate the effect of the stabilizing controller.     

Bounded Consensus Algorithms for Multi‐Agent Systems in Directed Networks

In this paper, the consensus problem is investigated via bounded controls for the multi‐agent systems with or without communication. Based on the nested saturation method, the saturated control laws are designed to solve the consensus problem. Under the designed saturated control laws, the transient performance of the closed‐loop system can be improved by tuning the saturation level. First of all, asymptotical consensus algorithms with bounded control inputs are proposed for the multi‐agent systems with or without communication delays. Under these consensus algorithms, the states’ consensus can be achieved asymptotically. Then, based on a kind of novel nonlinear saturation functions, bounded finite‐time consensus algorithms are further developed. It is shown that the states’ consensus can be achieved in finite time. Finally, two examples are given to verify the efficiency of the proposed methods.     

Quantized H∞ Control for Networked Systems with Communication Constraints

The problem of quantized H_∞ control for networked control systems (NCSs) subject to time‐varying delay and multiple packet dropouts is investigated in this paper. Both the control input and the measurement output signals are quantized before being transmitted and the quantized errors are described as sector bound uncertainties. The measurement channel and the control channel packet dropouts are considered simultaneously, and the stochastic variables satisfying Bernoulli random binary distribution are utilized to model the random multiple packet dropouts. Sufficient conditions for the existence of an observer‐based controller are established to ensure the exponential mean‐square stablility of the closed‐loop system and achieve the optimal H_∞ disturbance attenuation level. By using a globally convergent algorithm involving convex optimization, the nonconvex feasibility can be solved successfully. Finally, a numerical example is given to illustrate the effectiveness and applicability of the proposed method.     

核电站数字化反应堆保护系统停堆响应时间分析

为了评价出核电站数字化反应堆保护系统停堆响应的最大时间,根据数字化反应堆保护系统结构,分析出保护系统的响应过程须经过AI输入、IO总线通讯、CPU运算、网络通讯、DO输出处理。并基于DCS定周期扫描的运行方式,得出了各处理过程所需最大响应时间的计算方法,DCS停堆响应时间的评价值就是各部分最大响应时间的累加。最后提出了可缩短响应时间的改进方法。     

LED PWM dimming linearity investigation

LED PWM dimming application for large scale LED video displays is analyzed. The need for short light pulse duration is outlined. PWM dimming with short driving pulses is investigated experimentally. The LED response time skew introduces the nonlinearity for PWM dimming. For LED response time skew estimation, a method is suggested that has been successfully applied to measure some of today’s market representative LEDs. PWM dimming nonlinearity can be forecasted using the estimated skew. For a particular driving configuration, it is indicated that LED PWM dimming fails to satisfy the required 14 bit output coding together with the image refresh frequency of 400 Hz. A rough investigation demonstrates that the skew is quite stable. Therefore, the nonlinearity correction for the PWM pulse durations shorter than the skew value should be possible.     

Expected runtimes of evolutionary algorithms for the Eulerian cycle problem

Evolutionary algorithms are randomized search heuristics, which are applied to problems whose structure is not well understood, as well as to problems in combinatorial optimization. They have successfully been applied to different kinds of arc routing problems. To start the analysis of evolutionary algorithms with respect to the expected optimization time on these problems, we consider the Eulerian cycle problem. We show that a variant of the well-known (1+1)$(1+1)$ EA working on the important encoding of permutations is able to find an Eulerian tour of an Eulerian graph in expected polynomial time. Altering the operator used for mutation in the considered algorithm, the expected optimization time changes from polynomial to exponential.     

时间序列季节调整方法在中国的发展:PBC版X-12-ARIMA

中国人民银行(PBc)版X-12-ARIMA软件是基于中国特点而定制的时间序列季节调整软件.通过总结时间序列季节调整方法的特点以及相应软件在国外的发展,针对我国应用的特点,尤其是春节因素的考虑,在解剖X-12-ARIMA方法原理的基础上,在春节因素计算方法、软件应用界面以及用户使用帮助等3个主要方面加以改进,具有数据导入、调整设置文件、运行方式以及结果输出4方面的特色.     

A data collection protocol for real-time sensor applications

The nature of many sensor applications as well as continuously changing sensor data often imposes real-time requirements on wireless sensor network protocols. Due to numerous design constraints, such as limited bandwidth, memory and energy of sensor platforms, and packet collisions that can potentially lead to an unbounded number of retransmissions, timeliness techniques designed for real-time systems and real-time databases cannot be applied directly to wireless sensor networks. Our objective is to design a protocol for sensor applications that require periodic collection of raw data reports from the entire network in a timely manner. We formulate the problem as a graph coloring problem. We then present TIGRA (Timely Sensor Data Collection using Distributed Graph Coloring) — a distributed heuristic for graph coloring that takes into account application semantics and special characteristics of sensor networks. TIGRA ensures that no interference occurs and spatial channel reuse is maximized by assigning a specific time slot for each node. Although the end-to-end delay incurred by sensor data collection largely depends on a specific topology, platform, and application, TIGRA provides a transmission schedule that guarantees a deterministic delay on sensor data collection.     

Adaptive prescribed time control for quadrotor formation with stochastic links failure

The formation control for multiple quadrotors subject to maintaining the formation configuration and collision avoidance in the situation of stochastic links failure is investigated in this paper. First, the distributed formation controller is designed, the position controller is developed to manage the desired formation of position, and the attitude controller is developed to control the translation and rotation movements of the quadrotor. Then, in order to avoid the collisions between multiple quadrotors and the obstacles, a potential energy function method is introduced into the quadrotor formation control combined with the nest adaptive control. Inspired by the design of event trigger controller, a communication compensation controller is designed to ensure the stability of quadrotor formation under the condition of random communication interruption and recovery. Moreover, a prescribed time function is designed, which means the convergence time of the formation system can be set in advance. The prescribed time stability of the formation control system is proved by Lyapunov theory. Finally, the simulation results verify the effectiveness and superiority of this method.     

Volt-VAR control for distribution networks with high penetration of DGs: An overview

Continuously improving the ability to accept distributed renewable energies is the trend of future grid development, and a large number of papers have been published in recent years to study the problem of Volt-VAR control (VVC) for distribution networks with high penetration of distributed generations. This paper summarizes the relevant modeling and solution methods for VVC problems, mainly including VVC based on multiple time scales, hierarchical partitioning, multi-stage and network reconstruction, in conjunction with the operational characteristics of distribution networks containing distributed renewable energies; meanwhile, it analyzes the advantages and disadvantages of traditional optimization methods, heuristic intelligent algorithms and random variable processing methods used to solve VVC problems, and then introduces the application of model-free deep reinforcement learning as a latest decision method in VVC of distribution networks. Most of the models and methods compiled in this article are from the research results of the last three years and have some reference value.