近日，2019中国自动化学会自动化及人工智能颁奖盛典在河北省衡水市隆重举办。《自动化学报》多名编委荣获“杨嘉墀科技奖”、“CAA青年科学家奖”、“CAA科学技术奖”等奖项。

随着物联网、大数据、人工智能（Artificial intelligence，AI）等技术的发展，针对促进新一代信息技术与制造业深度融合、实现制造物理世界与信息世界交互与共融的需要，数字孪生和平行系统技术成为智能制造和复杂系统管理与控制领域研究的热点.本文对数字孪生和平行系统技术的基本概念、技术内涵、相关应用等进行了研究与总结，对比了两者之间的异同，并分析了两者的发展趋势，预期能够给复杂系统管理与控制领域的研究人员提供一定的参考和借鉴.

数字孪生概念模型

In this paper, we introduce the overall business model of power system dispatch, the top level design approach of an intelligent dispatch system, and the parallel intelligent technology with its dispatch applications. We expect that a new dispatch paradigm, namely the parallel dispatch, can be established by incorporating various intelligent technologies, especially the parallel intelligent technology, to enable secure operation of complex power grids, extend system operators' capabilities, suggest optimal dispatch strategies, and to provide decision-making recommendations according to power system operational goals.

The general business model of power grids operation

在高技术战争的背景下，陆用运动体控制系统呈现出数字化、智能化、网络化、无人化的发展趋势.面向未来作战需求，陆用运动体控制系统必须更加高效、可靠、自主与智能.本文针对陆用运动体控制系统的环境与态势感知，火力指挥与控制，多平台协同以及维修保障与健康管理对当前主要研究成果和最新进展做了简要综述.在总结国内外的现有研究成果的基础上，指出了目前存在的挑战与未来的研究方向.

本文对国家自然科学基金1986~2017年自动化领域项目申请和资助数据进行了大量数据分析，统计和分析结果表明自动化领域自然科学基金成为研究者重要的研究资金来源，研究人员规模、研究成果数量、基金资助数据都在稳步提升，研究队伍正呈现年轻化趋势.通过数据挖掘30年来不同研究热点及其变化，笔者发现自动化领域基金资助的相关研究领域能紧跟国际国内研究前沿，热点领域中理论研究比重大于应用研究，近年来具有应用研究背景的项目资助比重逐年提高.本文可为广大自动化领域相关的研究者提供选题等方面的借鉴和参考.

项目负责人年龄分布图

为解决机器人动力学模型未知问题并提升系统鲁棒性，本文基于扰动观测器，考虑动力学模型未知的情况，设计了一种自适应神经网络（Neural network，NN）跟踪控制器.首先分析了机器人运动学和动力学模型，针对模型已知的情况，提出了刚体机械臂通用模型跟踪控制策略；在考虑动力学模型未知的情况下，利用径向基函数（Radial basis function，RBF）神经网络设计基于全状态反馈的自适应神经网络跟踪控制器，并通过设计扰动观测器补偿系统中的未知扰动.利用李雅普诺夫理论证明所提出的控制策略可以使闭环系统误差信号半全局一致有界（Semi-globally uniformly bounded，SGUB），并通过选择合适的增益参数可以将跟踪误差收敛到零域.仿真结果证明所提出算法的有效性并且所提出的控制器在Baxter机器人平台上得到了实验验证.

3旋转关节自由度机器人Robotic toolbox中的模型

This paper focuses on the recent achievements and presents a survey of existing works on human-centered robots. Furthermore, we provide a comprehensive survey of the recent development of the human-centered intelligent robot and discuss the issues and challenges in the field.

In this paper, an adaptive neural network (NN) control approach is proposed for nonlinear pure-feedback systems with time-varying full state constraints. The pure-feedback systems of this paper are assumed to possess nonlinear function uncertainties. By using the mean value theorem, pure-feedback systems can be transformed into strict feedback forms. For the newly generated systems, NNs are employed to approximate unknown items. Based on the adaptive control scheme and backstepping algorithm, an intelligent controller is designed. At the same time, time-varying Barrier Lyapunov functions (BLFs) with error variables are adopted to avoid violating full state constraints in every step of the backstepping design. All closedloop signals are uniformly ultimately bounded and the output tracking error converges to the neighborhood of zero, which can be verified by using the Lyapunov stability theorem. Two simulation examples reveal the performance of the adaptive NN control approach.

The trajectories of yd , y=x1 , x2 and x3

利用数据驱动控制思想，建立一种设计离散时间非线性系统近似最优调节器的迭代神经动态规划方法.提出针对离散时间一般非线性系统的迭代自适应动态规划算法并且证明其收敛性与最优性.通过构建三种神经网络，给出全局二次启发式动态规划技术及其详细的实现过程，其中执行网络是在神经动态规划的框架下进行训练.这种新颖的结构可以近似代价函数及其导函数，同时在不依赖系统动态的情况下自适应地学习近似最优控制律.值得注意的是，这在降低对于控制矩阵或者其神经网络表示的要求方面，明显地改进了迭代自适应动态规划算法的现有结果，能够促进复杂非线性系统基于数据的优化与控制设计的发展.通过两个仿真实验，验证本文提出的数据驱动最优调节方法的有效性.

评判网络结构

突发事件通常具有难以预测、多成因关联、危害性大及演变复杂等特点，应急情况下如何安全高效疏散人员是应急管控领域的重要研究内容.本文将基于人工系统（Artificial systems，A）、计算实验（Computational experiments，C）、平行执行（Parallel excution，P）方法的平行系统理论引入到应急管控领域，提出平行应急疏散系统（Parallel emergency evacuation systems，PeES）基本概念，构建系统体系框架及集成平台，并介绍人工应急疏散系统、计算实验、平行执行等主要功能模块的基本功能及实现方法.通过PeES能实现虚实应急疏散系统的管理与控制、应急方案的实验与评估以及相关人员的学习与培训.最后，以轨道交通枢纽站火灾场景下的乘客应急疏散为典型应用对平行应急疏散系统进行初步验证.

平行系统基本框架

The purpose of this paper is to provide an overview of recent advances in guided crowd evacuation. Different guided crowd evacuation approaches are classified according to guidance approaches and technologies. A comprehensive analysis and comparison of crowd evacuation with static signage, dynamic signage, trained leader, mobile devices, mobile robot and wireless sensor networks are presented based on a single guidance mode perspective. In addition, the different evacuation guidance systems that use high-tech means such as advanced intelligent monitoring techniques, AI techniques, computer technology and intelligent inducing algorithms are reviewed from a system’s perspective. Future researches in the area of crowd evacuation are also discussed.

Different types of guidance patterns for crowd evacuation

This paper addresses the control design for automatic train operation of high-speed trains with protection constraints. A new resilient nonlinear gain-based feedback control approach is proposed, which is capable of guaranteeing, under some proper non-restrictive initial conditions, the protection constraints control raised by the distance-to-go (moving authority) curve and automatic train protection in practice. A new hyperbolic tangent function-based model is presented to mimic the whole operation process of high-speed trains. The proposed feedback control methods are easily implementable and computationally inexpensive because the presence of only two feedback gains guarantee satisfactory tracking performance and closed-loop stability, no adaptations of unknown parameters, function approximation of unknown nonlinearities, and attenuation of external disturbances in the proposed control strategies. Finally, rigorous proofs and comparative simulation results are given to demonstrate the effectiveness of the proposed approaches.

Trajectory of tanh(h|v(t)|) with v(t):0→1 m/s.

平行系统是一种建立在人工社会和计算实验基础上的科学研究方法，它的特点是既能真实反映现实系统的动态过程，又能实时优化现实系统的控制过程.自动化集装箱码头是一类典型的复杂系统，既存在不计其数的作业方案，同时也有大量的约束条件.如何在最短时间和最低能源消耗的前提下，完成具有间歇和批次特征的集装箱转运任务，是涉及到数学、控制、管理和计算机等多个学科的重大课题.本文采用数据引擎作为人工社会中的基本计算单元，构成一个复杂的平行系统，用于自动化集装箱码头信息控制系统的研究.数据引擎作为一种面向图形化元件组态的计算环境，非常适用于复杂系统的建模与计算.在可视化和动态重构技术的支持下，利用380个数据引擎对一个具有8台岸桥、25辆AGV和16台龙门吊组成的港机系统进行了自动化作业过程的计算实验.

平行控制的基本原理