Human-in-the-Loop Consensus Control for Nonlinear Multi-Agent Systems With Actuator Faults

This paper considers the human-in-the-loop leader-following consensus control problem of multi-agent systems (MASs) with unknown matched nonlinear functions and actuator faults. It is assumed that a human operator controls the MASs via sending the command signal to a non-autonomous leader which generates the desired trajectory. Moreover, the leader’s input is nonzero and not available to all followers. By using neural networks and fault estimators to approximate unknown nonlinear dynamics and identify the actuator faults, respectively, the neighborhood observer-based neural fault-tolerant controller with dynamic coupling gains is designed. It is proved that the state of each follower can synchronize with the leader’s state under a directed graph and all signals in the closed-loop system are guaranteed to be cooperatively uniformly ultimately bounded. Finally, simulation results are presented for verifying the effectiveness of the proposed control method.      

支持人在环路混合智能的交互设计研究

目的 指出“支持人在环路混合智能的交互设计”这一类设计问题,研究人在环路混合智能系统中交互设计的问题,为相关设计、技术与应用研究提供索引和参考。方法 从人在环路混合智能的概念和架构出发,引出人在环路混合智能的交互设计;基于对相关文献的整理,总结常见界面构成和交互方式;总结整理人在环路混合智能的生命周期。结论 指明了人在环路混合智能是需要用户交互的智能模型,介绍了由用户、人工智能算法、用户接口构成的系统架构;总结了针对不同数据类型的现有工作可能的交互方式;分析了人在环路混合智能完整生命周期中的设计挑战,根据现有文献提取关键界面构成,提出了人在环路混合智能系统的设计建议;提出了从智能系统、用户、设计师三方面建立设计方法论,完善设计工具,更有效地支持和推动人在环路混合智能系统的应用的建议。     

Stability limit of human-in-the-loop model reference adaptive control architectures

Model reference adaptive control (MRAC) offers mathematical and design tools to effectively cope with many challenges of real-world control problems such as exogenous disturbances, system uncertainties and degraded modes of operations. On the other hand, when faced with human-in-the-loop settings, these controllers can lead to unstable system trajectories in certain applications. To establish an understanding of stability limitations of MRAC architectures in the presence of humans, here a mathematical framework is developed whereby an MRAC is designed in conjunction with a class of linear human models including human reaction delays. This framework is then used to reveal, through stability analysis tools, the stability limit of the MRAC–human closed-loop system and the range of model parameters respecting this limit. An illustrative numerical example of an adaptive flight control application with a Neal–Smith pilot model is presented to demonstrate the effectiveness of developed approaches.     

Human-in-the-loop: probabilistic predictive modelling,its role,attributes, challenges and applications

Traditional human-factor-oriented approaches are based on experimentations followed by statistical analyses. Our novel probabilistic predictive modelling (PPM) concept is based on physically meaningful and flexible predictive modelling followed by experimentations geared to the appropriate models. The concept enables one to quantify, on the probabilistic basis, the outcome of a particular effort, situation or a mission. This cost-effective and insightful approach is applicable to numerous human-in-the-loop (HITL) situations, when a human acting as a part of the complex man–instrumentation–equipment–vehicle–environment system encounters an uncertain environment or a hazardous off-normal situation, and when there is an incentive to improve his/her role in a particular mission or a situation. The application of the PPM concept could improve dramatically the state-of-the-art in the HITL field in various vehicular technologies and beyond. The examples are taken mostly from the field of avionic safety.     

A Novel Design Framework for Smart Operating Robot in Power System

This paper proposes the concept and framework of smart operating system based on the artificial intelligence (AI) techniques. The demands and the potential applications of AI technologies in power system control centers is discussed in the beginning of the paper. The discussion is based on the results of a field study in the Tianjin Power System Control Center in China. According to the study, one problem in power systems is that the power system analysis system in the control center is not fast and powerful enough to help the operators in time to deal with the incidents in the power system. Another issue in current power system control center is that the operation tickets are compiled manually by the operators, so that it is less efficient and human errors cannot be avoided. Based on these problems, a framework of the smart operating robot is proposed in this paper, which includes an intelligent power system analysis system and a smart operation ticket compiling system to solve the two problems in power system control centers. The proposed framework is mainly based on the AI techniques, especially the neural network with deep learning, since it is faster and more capable of dealing with the highly nonlinear and complex power system.      

人在回路的数据融合系统

一个端到端的数据融合系统在如下几个阶段需要用户的反馈,包括为实体匹配收集训练数据的阶段,对计算出来的实体聚类进行纠错的阶段以及对每个实体聚类中的数据形式进行转换的阶段.经过这些步骤之后,数据融合系统可以将每个实体分组中的实体合并成一条权威的实体表示(被称作"黄金记录").传统的数据融合系统通常会按照特定的顺序,在用户时...     

两足步行椅机器人的稳定性控制

对两足步行椅机器人的人在环中的控制系统的稳定性控制进行了初步研究,对这种系统的特殊性进行了分析。运用零力矩点（ZMP）的稳定判据方法研究了步行椅机器人行走过程中乘客移动对系统稳定性的影响,对影响系统稳定性的乘客摆动角、角速度及角加速度参数进行了仿真实验和分析,在此基础上研究了机器人对乘客的不稳定因素的补偿性控制并进行了仿真实验。实验结果表明,在实时控制中增加对乘客的位姿控制,可以在尽量减小步态调整幅度的同时很好地保证系统的稳定性。     

EvIcon: Designing High-Usability Icon with Human-in-the-loop Exploration and IconCLIP

Interface icons are prevalent in various digital applications. Due to limited time and budgets, many designers rely on informal evaluation, which often results in poor usability icons. In this paper, we propose a unique human-in-the-loop framework that allows our target users, that is novice and professional user interface (UI) designers, to improve the usability of interface icons efficiently. We formulate several usability criteria into a perceptual usability function and enable users to iteratively revise an icon set with an interactive design tool, EvIcon. We take a large-scale pre-trained joint image-text embedding (CLIP) and fine-tune it to embed icon visuals with icon tags in the same embedding space (IconCLIP). During the revision process, our design tool provides two types of instant perceptual usability feedback. First, we provide perceptual usability feedback modelled by deep learning models trained on IconCLIP embeddings and crowdsourced perceptual ratings. Second, we use the embedding space of IconCLIP to assist users in improving icons' visual distinguishability among icons within the user-prepared icon set. To provide the perceptual prediction, we compiled IconCEPT10K, the first large-scale dataset of perceptual usability ratings over 10,000 interface icons, by conducting a crowdsourcing study. We demonstrated that our framework could benefit UI designers' interface icon revision process with a wide range of professional experience. Moreover, the interface icons designed using our framework achieved better semantic distance and familiarity, verified by an additional online user study.