A multilevel sampled‐data approach for resilient navigation and control of autonomous systems

Autonomous systems are rapidly becoming an integrated part of the modern life. Safe and secure navigation and control of these systems present significant challenges in the presence of uncertainties, physical failures, and cyber attacks. In this paper, we formulate a navigation and control problem for autonomous systems using a multilevel control structure, in which the high‐level reference commands are limited by a saturation function, whereas the low‐level controller tracks the reference by compensating for disturbances and uncertainties. For this purpose, we consider a class of nested, uncertain, multiple‐input–multiple‐output systems subject to reference command saturation, possibly with nonminimum phase zeros. A multirate output‐feedback adaptive controller is developed as the low‐level controller. The sampled‐data (SD) design of this controller facilitates the direct implementation on digital computers, where the input/output signals are available at discrete time instances with different sampling rates. In addition, stealthy zero‐dynamics attacks become detectable by considering a multirate SD formulation. Robust stability and performance of the overall closed‐loop system with command saturation and multirate adaptive control are analyzed. Simulation scenarios for navigation and control of a fixed‐wing drone under failures/attacks are provided to validate the theoretical findings.     

Packet loss detection in networked control systems

One of the main issues related to the reliable operation of network control systems concerns the design of mechanisms able to detect anomalies in the functioning of the communication network through which the control loops are closed. In this article, we address the problem of detecting the occurrence of packet losses and design a mechanism that can detect the occurrence of packet losses directly from process sensor data. Specifically, we propose a moving‐horizon estimation scheme that permits to detect if the packet‐loss rate exceeds prescribed thresholds, which are representative of stability and performance of the control system. We discuss theoretical properties of the proposed solution along with an approximation scheme with reduced computational burden. A numerical example is discussed to substantiate the analysis.     

Zone model predictive control for pressure management of water distribution network

In the water distribution network (WDN), although the water demand of the node is changing constantly, the water quantity and water pressure of the node need to be met at each moment. To realize energy saving and consumption reduction, it is proposed to control the nodal water head of WDN in an appropriate narrow range. The frequent large fluctuation of the water demand, which may lead the water pressure exceed the expected range, increases the difficulty of the zone control. To realize optimized WND control, a novel zone predictive control is proposed, where two switching cases are considered. The switching condition is whether there are feasible solutions to keep the pressure within the expected region over the prediction horizon. When the condition is satisfied, the controller minimizes the variation of inputs with constraints of pressure range for ensuring the tank level staying within the expected zone and obtaining optimal economic cost. When the current pressure is out of the expected region or the condition is not satisfied due to the large variation of water demand, a reference trajectory of outputs is introduced, which is combined with the inputs as an optimization variable, and the constraints of expected zone are moved from the output to the introduced reference trajectory. Through minimizing the distance between reference trajectory and output, the controller will keep the tank level from deviating too far from the expected zone and will drive the tank level rapidly into the expected zone once the tank level exceeds the expected range. An application of the proposed zone MPC to WDN in Shinan District of Shanghai is given to illustrate its effectiveness.     

2种灰色模型在矿区地表沉陷预计中的适用性

目前矿区地表单点沉陷动态预计方法主要基于传统的水准测量数据,监测方法单一,成本高,观测点易破坏,不能保证地表形变信息的实时性,且采用灰色模型进行地表沉陷预计时只针对单一模型的应用,没有结合模型自身特点分析其适用性。以袁店二矿7221工作面为试验区域,采用合成孔径雷达差分干涉测量技术监测矿区地表沉陷量,分别建立了描述沉陷量与时间关系的GM(1,1)与灰色Verhulst模型进行地表沉陷量预计,实现了矿区地表沉陷监测与动态预计一体化。通过比较、分析GM(1,1)与灰色Verhulst模型对地表沉陷量的拟合及预计结果,得出了2种灰色模型在矿区地表沉陷预计中的适用性:在矿区开采沉陷开始至活跃前期,若地表单点沉陷量曲线呈近似单峰型,则宜采用GM(1,1)进行短期预计;当矿区地表沉陷进入衰退阶段,单点沉陷量曲线呈平底饱和状态,则宜采用灰色Verhulst模型进行中长期预计。     

海上稠油油田蒸汽吞吐产量确定新方法

针对目前蒸汽吞吐产量预测模型假设条件简单、普适性差等问题,一般采用测试法和类比法综合确定海上稠油油田蒸汽吞吐初期产量。由于目前海上油田通常只开展常规测试,无法直接获得热采开发初期产量。笔者提出海上稠油油田蒸汽吞吐初期产量确定新方法,建立蒸汽吞吐相对于常规开发的初期产量倍数预测模型,通过蒸汽吞吐产量倍数,将常规测试确定的产量转化为蒸汽吞吐产量。研究表明,蒸汽吞吐初期产量倍数主要受储集层渗透率、原油黏度、注入强度、蒸汽干度等因素影响,利用正交试验设计和多元回归等方法,建立海上稠油油田蒸汽吞吐初期产量倍数与油藏地质参数及注入参数之间的非线性预测模型,该模型经实际生产数据验证,预测误差小于5%,可靠性高,能够为海上稠油油田蒸汽吞吐初期产量的确定提供依据。     

煤样多元物性参数在煤与瓦斯突出防治中的应用研究

煤与瓦斯突出是一种复杂的动力现象，突出的类型不同、引起突出的危险因素不同，预测指标对其的敏感性也就不同，选择适应的突出危险性预测敏感指标及突出敏感指标的临界值，研究可靠的突出预测手段和方法来实现对突出危险性的准确预测，是指导突出防治工作的重要技术基础，而为了获得突出敏感指标的临界值就必须对煤的多元物性参数进行实验研究，掌握煤层的各项物化性质，是有效确定防治突出方法体系的关键基础。     

Performance-based modelling of long-term deterioration to support rehabilitation and investment decisions in drinking water distribution systems

Managing the urban drinking water system in the long term in order to maintain system performance can be challenging due to the difficulty of modelling future deterioration of the networks. This paper establishes a methodology for cohort survival models where historical (empirical) data on decommissioning ages of pipes are used to calibrate survival functions of pipe cohorts according to service level targets. The benefit of the approach is that remaining useful life of pipes, future renewal rates and investment needs can be governed by a required level of service in the network. A case study shows how the methodology can be applied to a cohort of drinking water pipes to create a ‘calibration curve’, which is a survival function calibrated with empirical data.     

A hidden Markov model to detect regime changes in cryptoasset markets

The objective of this work is to understand the dynamics of cryptocurrency prices. Specifically, how prices switch between different regimes, going from “bull” to “stable” and “bear” times. For this purpose, we propose a hidden Markov model that aims at explaining the evolution of Bitcoin prices through different, unobserved states. The implementation of the proposed model includes a likelihood ratio test that allows to compare models with different states and with different covariance structures. Our empirical findings show that the time movements of Bitcoin prices across different exchange markets are well-described by the proposed model. In particular, a parsimonious model with a diagonal covariance matrix leads to better predictions, compared with a model with a full covariance matrix.     

A comparative study for consistency-based decision making with interval multiplicative preference relations

ABSTRACT

To ensure the reasonable application and perfect the theory of decision making with interval multiplicative preference relations (IMPRs), this paper continues to discuss decision making with IMPRs. After reviewing previous consistency concepts for IMPRs, we find that Krej?í’s consistency concept is more flexible and natural than others. However, it is insufficient to address IMPRs only using this concept. Considering this fact, this paper researches inconsistent and incomplete IMPRs that are usually encountered. First, programming models for addressing inconsistent and incomplete IMPRs are constructed. Then, this paper studies the consensus of individual IMPRs and defines a consensus index using the defined correlation coefficient. When the consensus requirement does not satisfy requirement, a programming model for improving consensus level is built, which can ensure the consistency. Subsequently, a procedure for group decision making with IMPRs is offered, and associated examples are provided to specifically show the application of main theoretical results.     

大涡模拟Smagorinsky模型用于磨粒流精密加工喷嘴的质量控制研究

为研究固液两相磨粒流加工喷嘴小孔过程中的流场分布、涡旋形成规律及涡旋的存在对磨粒流加工的影响机制，采用Smagorinsky亚格子模型对磨粒流加工喷嘴小孔的流道进行大涡数值模拟，并使用磨粒流对变直径喷嘴工件进行加工试验。数值模拟发现磨粒流流体中磨粒与壁面的碰撞与剪切作用随流体的速度增大而增大，同一截面的速度存在速度差，其中还伴随涡旋的存在；通过试验研究发现：经固液两相磨粒流加工后的喷嘴小孔表面质量得到明显提高，喷嘴经过四次不同入口速度的磨粒流加工后大孔处表面粗糙度Ra由1.24 μm降至0.542 μm，小孔处表面粗糙度Ra由1.21 μm降至0.437 μm。结论显示固液两相磨粒流加工技术可有效提高被加工喷嘴工件的内表面质量，加工时同一截面的速度存在速度差，速度差的存在利于涡旋的形成，涡旋的存在利于提高磨粒流加工过程的剪切作用，有助于获得高质量的喷嘴小孔内通道表面。