Control 5.0: From Newton to Merton in Popper's Cyber-Social-Physical Spaces

The future of control in cyberspace of parallel worlds is discussed. It argues for the coming age of Control 5.0,the control technology for the new IT capable of dealing with artificial worlds with VR, AR, AI and robotics. The discipline of automation needs a new interpretation of its core knowledge and skill set of modeling, analysis, and control for cyber-socialphysical systems, and a paradigm shift from Newtonian Systems with Newton's Laws or Big Laws with Small Data to Mertonian Systems with Merton's Laws or Small Laws with Big Data.

教育5.0时代:内涵、需求和挑战

数字化转型开启了教育5.0时代,为教育带来新的格局、多模态学习的新体验、人机协同的新范式。文章探讨了以学习者为中心、生成式人工智能为技术支撑、智慧教育生态环境建设为依托的教育5.0的内涵、需求及面临的挑战。教育5.0强调学习的主体性、包容性、公平性、可持续性与韧性、主观能动性和团结协作性。文章通过分析教育的演进过程与阶段特征,阐释了教育5.0时代的理念升级、内容更新和方式变革,指出未来教育更应注重人本主义,关注学习者学习体验,满足其个性化和多样化需求,促进其全面发展;将数字技术整合到教育各层面,推动全方位创新与变革,推进教育优质公平与支持终身学习,形成具有开放性、适应性、个性化、自主性等特征的教育新生态。

工业5.0时代中等和低收入国家职业教育转型及启示——对世界银行《创建更优质正规职业教育体系报告》的解读

为加快推进中等和低收入国家职业教育转型发展,适应工业5.0时代对职业人才培养的新需求,世界银行发布了《创建更优质正规职业教育体系报告》,明确了从“身份认同”向“追求卓越”、从“注重输入”向“强调效能”、从“推测决策”到“循证决策”三大转型战略,对我国新质生产力战略下的职业教育转型发展具有重要启示。

2019年四川威远东两次M_(S)>5.0地震序列活动特征及震源区体波速度结构

自2015年以来,四川威远及邻区的地震活动强度显著增强,其中2019年9月8日和12月18日在威远东发生了M_(S)5.4和M_(S)5.2地震,刷新了威远地区有记录以来历史地震震级上限.本文利用2019年8月至2020年3月期间威远及邻区25个流动台和17个固定台记录到的5638次M_(L)≥1.2地震Pg和Sg波到时以及连续波形资料,采用双差层析成像方法获得了威远东这两次地震序列的重定位结果和震源区浅层地壳高分辨率三维体波速度结构.重定位结果显示震源区地震震源深度主要集中在3~4 km.基于两次M_(S)>5.0地震震后10天余震序列空间展布,本文推测两次地震的发震构造均为NNE走向且倾向SE(倾角40°左右)的盲冲断层.三维体波速度结构反演结果揭示:震源区V_(P)异常变化较V_(S)异常变化显著,V_(P)/V_(S)比背景值普遍低4%~10%.在深度小于5 km的浅层内,地震多分布在V_(P)低速异常和V_(S)弱高速异常以及二者的高低速异常过渡带内;在5~7 km深度范围,地震基本上都发生在V_(P)低速异常区.上述速度结构特征揭示震源区附近可能发育着富含气体的孔隙和裂缝且存在流体活动.此外,2019年11月7日以后的地震活动具有明显的流体扩散驱动地震迁移特征,沿两次地震发震断层走向和倾向的流体扩散系数分别为1.5 m~2·s^(-1)和0.1 m~2·s^(-1).两次地震的发生机理很可能是流体扩散引起的孔隙压力上升.

日本实现“社会5.0”的科技创新政策梳理及其对中国未来社会发展的启示

在老龄少子化等社会问题凸显、综合国力不断下行的背景下,日本政府于2016年提出“社会5.0”构想。对日本的“社会5.0”先行经验进行分析,有助于更好地理解中国的科技创新政策对中国未来社会发展的影响,并为中国未来发展提供参考借鉴。通过政策研究和对比,对日本“社会5.0”科技创新政策的起源与发展阶段进行梳理和定义,解析日本科技战略框架,结合相关应用案例分析其亮点和意义,并综合比较新加坡、英国、美国等不同国家数智科技政策的异同。研究发现,日本“社会5.0”政策在智能城市与物联网、医疗与健康护理、无人驾驶汽车与自动驾驶、节能和可持续性等领域取得较好的实践应用,推动信息技术与物理社会融合,以实现更智能、更可持续、更人性化的社会。进一步,通过总结中国数智科技创新的现状与挑战,认为中国目前仍需要应对高端技术创新不足、科技成果转化率低等问题,对此,借鉴日本“社会5.0”,提出中国应通过加强国际协同合作和分享、加强关键技术创新进行智能化产业升级,以及加强建设智慧城市、鼓励终身学习、加强相关立法和提供个性化服务等。

Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications

Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, respectively.Then, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation significantly.Finally, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.

Communication Resource-Efficient Vehicle Platooning Control With Various Spacing Policies

Platooning represents one of the key features that connected automated vehicles may possess as it allows multiple automated vehicles to be maneuvered cooperatively with small headways on roads. However, a critical challenge in accomplishing automated vehicle platoons is to deal with the effects of intermittent and sporadic vehicle-to-vehicle data transmissions caused by limited wireless communication resources. This paper addresses the co-design problem of dynamic event-triggered communication scheduling and cooperative adaptive cruise control for a convoy of automated vehicles with diverse spacing policies. The central aim is to achieve automated vehicle platooning under various gap references with desired platoon stability and spacing performance requirements, while simultaneously improving communication efficiency. Toward this aim, a dynamic event-triggered scheduling mechanism is developed such that the intervehicle data transmissions are scheduled dynamically and efficiently over time. Then, a tractable co-design criterion on the existence of both the admissible event-driven cooperative adaptive cruise control law and the desired scheduling mechanism is derived. Finally, comparative simulation results are presented to substantiate the effectiveness and merits of the obtained results.

“营销5.0+”人才培养的理论与实践探索

“营销5.0+”时代的到来对高校市场营销专业人才的培养提出了新要求。“以人为中心、数智技术赋能、营销向善”代表着新的营销观念与实践的前沿,也定义了当今时代高素质营销人才需具备的新特质。由此,高校营销人才培养的改革创新要实现人文回归、数智融合与多元协同。文章以山东某省属重点建设高校市场营销专业为例,探索新文科建设背景下数智时代营销人才培养在目标理念、课程体系、培养模式等方面的创新与改革路径。

Mitochondrial dysfunction and quality control lie at the heart of subarachnoid hemorrhage

The dramatic increase in intracranial pressure after subarachnoid hemorrhage leads to a decrease in cerebral perfusion pressure and a reduction in cerebral blood flow.Mitochondria are directly affected by direct factors such as ischemia,hypoxia,excitotoxicity,and toxicity of free hemoglobin and its degradation products,which trigger mitochondrial dysfunction.Dysfunctional mitochondria release large amounts of reactive oxygen species,inflammatory mediators,and apoptotic proteins that activate apoptotic pathways,further damaging cells.In response to this array of damage,cells have adopted multiple mitochondrial quality control mechanisms through evolution,including mitochondrial protein quality control,mitochondrial dynamics,mitophagy,mitochondrial biogenesis,and intercellular mitochondrial transfer,to maintain mitochondrial homeostasis under pathological conditions.Specific interventions targeting mitochondrial quality control mechanisms have emerged as promising therapeutic strategies for subarachnoid hemorrhage.This review provides an overview of recent research advances in mitochondrial pathophysiological processes after subarachnoid hemorrhage,particularly mitochondrial quality control mechanisms.It also presents potential therapeutic strategies to target mitochondrial quality control in subarachnoid hemorrhage.

A Tutorial on Quantized Feedback Control

In this tutorial paper, we explore the field of quantized feedback control, which has gained significant attention due to the growing prevalence of networked control systems. These systems require the transmission of feedback information, such as measurements and control signals, over digital networks, presenting novel challenges in estimation and control design. Our examination encompasses various topics, including the minimal information needed for effective feedback control, the design of quantizers, strategies for quantized control design and estimation,achieving consensus control with quantized data, and the pursuit of high-precision tracking using quantized measurements.

Finite-time Prescribed Performance Time-Varying Formation Control for Second-Order Multi-Agent Systems With Non-Strict Feedback Based on a Neural Network Observer

This paper studies the problem of time-varying formation control with finite-time prescribed performance for nonstrict feedback second-order multi-agent systems with unmeasured states and unknown nonlinearities.To eliminate nonlinearities,neural networks are applied to approximate the inherent dynamics of the system.In addition,due to the limitations of the actual working conditions,each follower agent can only obtain the locally measurable partial state information of the leader agent.To address this problem,a neural network state observer based on the leader state information is designed.Then,a finite-time prescribed performance adaptive output feedback control strategy is proposed by restricting the sliding mode surface to a prescribed region,which ensures that the closed-loop system has practical finite-time stability and that formation errors of the multi-agent systems converge to the prescribed performance bound in finite time.Finally,a numerical simulation is provided to demonstrate the practicality and effectiveness of the developed algorithm.

Quantization and Event-Triggered Policy Design for Encrypted Networked Control

This paper proposes a novel event-driven encrypted control framework for linear networked control systems(NCSs),which relies on two modified uniform quantization policies,the Paillier cryptosystem,and an event-triggered strategy.Due to the fact that only integers can work in the Pailler cryptosystem,both the real-valued control gain and system state need to be first quantized before encryption.This is dramatically different from the existing quantized control methods,where only the quantization of a single value,e.g.,the control input or the system state,is considered.To handle this issue,static and dynamic quantization policies are presented,which achieve the desired integer conversions and guarantee asymptotic convergence of the quantized system state to the equilibrium.Then,the quantized system state is encrypted and sent to the controller when the triggering condition,specified by a state-based event-triggered strategy,is satisfied.By doing so,not only the security and confidentiality of data transmitted over the communication network are protected,but also the ciphertext expansion phenomenon can be relieved.Additionally,by tactfully designing the quantization sensitivities and triggering error,the proposed event-driven encrypted control framework ensures the asymptotic stability of the overall closedloop system.Finally,a simulation example of the secure motion control for an inverted pendulum cart system is presented to evaluate the effectiveness of the theoretical results.

Practical Prescribed Time Tracking Control With Bounded Time-Varying Gain Under Non-Vanishing Uncertainties

This paper investigates the prescribed-time control(PTC) problem for a class of strict-feedback systems subject to non-vanishing uncertainties. The coexistence of mismatched uncertainties and non-vanishing disturbances makes PTC synthesis nontrivial. In this work, a control method that does not involve infinite time-varying gain is proposed, leading to a practical and global prescribed time tracking control solution for the strict-feedback systems, in spite of both the mismatched and nonvanishing uncertainties. Different from methods based on control switching to avoid the issue of infinite control gain that involves control discontinuity at the switching point, in our method a softening unit is exclusively included to ensure the continuity of the control action. Furthermore, in contrast to most existing prescribed-time control works where the control scheme is only valid on a finite time interval, in this work, the proposed control scheme is valid on the entire time interval. In addition, the prior information on the upper or lower bound of gi is not in need,enlarging the applicability of the proposed method. Both the theoretical analysis and numerical simulation confirm the effectiveness of the proposed control algorithm.

Noise-Tolerant ZNN-Based Data-Driven Iterative Learning Control for Discrete Nonaffine Nonlinear MIMO Repetitive Systems

Aiming at the tracking problem of a class of discrete nonaffine nonlinear multi-input multi-output(MIMO) repetitive systems subjected to separable and nonseparable disturbances, a novel data-driven iterative learning control(ILC) scheme based on the zeroing neural networks(ZNNs) is proposed. First, the equivalent dynamic linearization data model is obtained by means of dynamic linearization technology, which exists theoretically in the iteration domain. Then, the iterative extended state observer(IESO) is developed to estimate the disturbance and the coupling between systems, and the decoupled dynamic linearization model is obtained for the purpose of controller synthesis. To solve the zero-seeking tracking problem with inherent tolerance of noise,an ILC based on noise-tolerant modified ZNN is proposed. The strict assumptions imposed on the initialization conditions of each iteration in the existing ILC methods can be absolutely removed with our method. In addition, theoretical analysis indicates that the modified ZNN can converge to the exact solution of the zero-seeking tracking problem. Finally, a generalized example and an application-oriented example are presented to verify the effectiveness and superiority of the proposed process.

Quaternion-Based Adaptive Trajectory Tracking Control of a Rotor-Missile with Unknown Parameters Identification

This paper investigates the adaptive trajectory tracking control problem and the unknown parameter identification problem of a class of rotor-missiles with parametric system uncertainties.First,considering the uncertainty of structural and aerodynamic parameters,the six-degree-of-freedom(6Do F) nonlinear equations describing the position and attitude dynamics of the rotor-missile are established,respectively,in the inertial and body-fixed reference frames.Next,a hierarchical adaptive trajectory tracking controller that can guarantee closed-loop stability is proposed according to the cascade characteristics of the 6Do F dynamics.Then,a memory-augmented update rule of unknown parameters is proposed by integrating all historical data of the regression matrix.As long as the finitely excited condition is satisfied,the precise identification of unknown parameters can be achieved.Finally,the validity of the proposed trajectory tracking controller and the parameter identification method is proved through Lyapunov stability theory and numerical simulations.

Versatile and controlled quantum teleportation network

A quantum teleportation network involving multiple users is essential for future quantum internet.So far,controlled quantum teleportation has been demonstrated in a three-user network.However,versatile and controlled quantum teleportation network involving more users is in demand,which satisfies different combinations of users for practical requirements.Here we propose a highly versatile and controlled teleportation network that can switch among various combinations of different users.We use a single continuous-variable six-partite Greenberger-Horne-Zeilinger(GHZ)state to realize such a task by choosing the different measurement and feedback operations.The controlled teleportation network,which includes one sub-network,two sub-networks and three sub-networks,can be realized for different application of user combinations.Furthermore,the coherent feedback control(CFC)can manipulate and improve the teleportation performance.Our approach is flexible and scalable,and would provide a versatile platform for demonstrations of complex quantum communication and quantum computing protocols.

Adaptive admittance tracking control for interactive robot with prescribed performance

An adaptive control approach is presented in this paper for tracking desired trajectories in interactive manipulators. The controller design incorporates prescribed performance functions (PPFs) to improve dynamic performance. Notably, the performance of the output error is confined in an envelope characterized by exponential convergence, leading to convergence to zero. This feature ensures a prompt response from admittance control and establishes a reliable safety framework for interactions. Simulation results provide practical insights,demonstrating the viability of the control scheme proposed in this paper.

Nonlinear robust adaptive control for bidirectional stabilization system of all-electric tank with unknown actuator backlash compensation and disturbance estimation

Since backlash nonlinearity is inevitably existing in actuators for bidirectional stabilization system of allelectric tank,it behaves more drastically in high maneuvering environments.In this work,the accurate tracking control for bidirectional stabilization system of moving all-electric tank with actuator backlash and unmodeled disturbance is solved.By utilizing the smooth adaptive backlash inverse model,a nonlinear robust adaptive feedback control scheme is presented.The unknown parameters and unmodelled disturbance are addressed separately through the derived parametric adaptive function and the continuous nonlinear robust term.Because the unknown backlash parameters are updated via adaptive function and the backlash effect can be suppressed successfully by inverse operation,which ensures the system stability.Meanwhile,the system disturbance in the high maneuverable environment can be estimated with the constructed adaptive law online improving the engineering practicality.Finally,Lyapunov-based analysis proves that the developed controller can ensure the tracking error asymptotically converges to zero even with unmodeled disturbance and unknown actuator backlash.Contrast co-simulations and experiments illustrate the advantages of the proposed approach.

A Fractional-Order Ultra-Local Model-Based Adaptive Neural Network Sliding Mode Control of n-DOF Upper-Limb Exoskeleton With Input Deadzone

This paper proposes an adaptive neural network sliding mode control based on fractional-order ultra-local model for n-DOF upper-limb exoskeleton in presence of uncertainties,external disturbances and input deadzone.Considering the model complexity and input deadzone,a fractional-order ultra-local model is proposed to formulate the original dynamic system for simple controller design.Firstly,the control gain of ultra-local model is considered as a constant.The fractional-order sliding mode technique is designed to stabilize the closed-loop system,while fractional-order time-delay estimation is combined with neural network to estimate the lumped disturbance.Correspondingly,a fractional-order ultra-local model-based neural network sliding mode controller(FO-NNSMC) is proposed.Secondly,to avoid disadvantageous effect of improper gain selection on the control performance,the control gain of ultra-local model is considered as an unknown parameter.Then,the Nussbaum technique is introduced into the FO-NNSMC to deal with the stability problem with unknown gain.Correspondingly,a fractional-order ultra-local model-based adaptive neural network sliding mode controller(FO-ANNSMC) is proposed.Moreover,the stability analysis of the closed-loop system with the proposed method is presented by using the Lyapunov theory.Finally,with the co-simulations on virtual prototype of 7-DOF iReHave upper-limb exoskeleton and experiments on 2-DOF upper-limb exoskeleton,the obtained compared results illustrate the effectiveness and superiority of the proposed method.

Seismic control of multi-degrees-of-freedom structures by vertical mass isolation method using MR dampers

Vertical mass isolation(VMI)is one of the novel methods for the seismic control of structures.In this method,the entire structure is assumed to consist of two mass and stiffness subsystems,and an isolated layer is located among them.In this study,the magnetorheological damper in three modes:passive-off,passive-on,and semi-active mode with variable voltage between zero and 9 volts was used as an isolated layer between two subsystems.Multi-degrees-of-freedom structures with 5,10,and 15 floors in two dimensions were examined under 11 pairs of near field earthquakes.On each level,the displacement of MR dampers was taken into account.The responses of maximum displacement,maximum inter-story drift,and maximum base shear in controlled and uncontrolled buildings were compared to assess the suggested approach for seismic control of the structures.According to the results,the semi-active control method can reduce the response by more than 12%compared to the uncontrolled mode in terms of maximum displacement of the mass subsystem of the structures.This method can reduce more than 16%and 20%of the responses compared to the uncontrolled mode in terms of maximum inter-story drift and base shear of the structure,respectively.