基于状态观测器的电镀液温度状态反馈控制系统

针对电镀液温度控制系统,引入状态反馈控制。相对于传统的反馈控制,本方法可以获得更优异的性能。另外,考虑到镀液温度控制具有纯迟延的特点,提出了一种消去纯迟延的方法。仿真验证结果显示,控制效果良好。     

Effects of timing of formative feedback in computer-assisted learning environments

This study examines the effects of timing of corrective formative feedback on processing text information on question-answering. Undergraduate students read an expository text and answered questions in two attempts. Students were randomly assigned to a no feedback, immediate feedback and delayed feedback conditions. Students in the feedback conditions received feedback on the correctness of their answer after the first attempt and were informed about the right answer after the second attempt. Students were prompted to restudy the text after failing in their first attempt. However, students in the no feedback condition were just prompted to search the text. All students were tested on question-answering, corrective probability and a post-test cued-recall test. Results showed that: (a) feedback reduced the initial time reading the text; (b) feedback increased performance on question answering and cued-recall; (c) delayed feedback produced no advantages over immediate feedback. Theoretical and practical implications of these results are discussed.     

Decentralized output feedback stabilization for switched stochastic high-order nonlinear systems with time-varying state/input delays

This paper investigates decentralized output feedback stabilization problem for a class of switched stochastic high-order systems with time-varying state/input delays. With the help of coordinate transformations, a scaling gain is incorporated into the observers and controllers for the nominal system. Based on the homogeneous domination approach and stochastic Lyapunov–Krasovskii stability theorem, it is shown that global asymptotic stability in probability of the closed-loop system can be implemented by tuning the scaling gain. Two examples are given to demonstrate the feasibility of the proposed control method.     

High-Speed Trains Automatic Operation with Protection Constraints: A Resilient Nonlinear Gain-based Feedback Control Approach

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.      

Adaptive neural network command filtered backstepping control of pure‐feedback systems in presence of full state constraints

An adaptive neural network (NN) command filtered backstepping control is proposed for the pure‐feedback system subjected to time‐varying output/stated constraints. By introducing a one‐to‐one nonlinear mapping, the obstacle caused by full stated constraints is conquered. The adaptive control law is constructed by command filtered backstepping technology and radial basis function NNs, where only one learning parameter needs to be updated online. The stability analysis via nonlinear small‐gain theorem shows that all the signals in closed‐loop system are semiglobal uniformly ultimately bounded. The simulation examples demonstrate the effectiveness of the proposed control scheme.     

Fuzzy approximation-based adaptive control of nonstrict feedback stochastic nonlinear systems with time-varying state constraints

This article develops an approximation-based fuzzy control scheme for nonstrict feedback stochastic nonlinear systems (NFSNS) with time-varying state constraints. The difficulty in constructing controller is how to conquer the algebraic loop problem caused by nonstrict feedback structure, as well as prevent the state constraints from violating. To dispose the time-varying state constraints, time-varying barrier Lyapunov function is incorporated into the backstepping design framework. The lumped uncertainties of NFSNS are approximated by the fuzzy logic systems. By virtue of fuzzy basis function, the algebraic loop problem is effectively handled. Theoretical analysis shows that the predefined state constraints are not violated and all signals of the closed-loop systems are bounded. Finally, simulation results substantiate the validity of the devised method.     

Exponential Set-Point Stabilization of Underactuated Vehicles Moving in Three-Dimensional Space

This paper investigates the stabilization of underactuated vehicles moving in a three-dimensional vector space. The vehicle’s model is established on the matrix Lie group SE(3), which describes the configuration of rigid bodies globally and uniquely. We focus on the kinematic model of the underactuated vehicle, which features an underactuation form that has no sway and heave velocity. To compensate for the lack of these two velocities, we construct additional rotation matrices to generate a motion of rotation coupled with translation. Then, the state feedback is designed with the help of the logarithmic map, and we prove that the proposed control law can exponentially stabilize the underactuated vehicle to the identity group element with an almost global domain of attraction. Later, the presented control strategy is extended to set-point stabilization in the sense that the underactuated vehicle can be stabilized to an arbitrary desired configuration specified in advance. Finally, simulation examples are provided to verify the effectiveness of the stabilization controller.      

A Fluorimetric Readout Reporting the Kinetics of Nucleotide‐Induced Human Ribonucleotide Reductase Oligomerization

Human ribonucleotide reductase (hRNR) is a target of nucleotide chemotherapeutics in clinical use. The nucleotide‐induced oligomeric regulation of hRNR subunit α is increasingly being recognized as an innate and drug‐relevant mechanism for enzyme activity modulation. In the presence of negative feedback inhibitor dATP and leukemia drug clofarabine nucleotides, hRNR‐α assembles into catalytically inert hexameric complexes, whereas nucleotide effectors that govern substrate specificity typically trigger α‐dimerization. Currently, both knowledge of and tools to interrogate the oligomeric assembly pathway of RNR in any species in real time are lacking. We therefore developed a fluorimetric assay that reliably reports on oligomeric state changes of α with high sensitivity. The oligomerization‐directed fluorescence quenching of hRNR‐α, covalently labeled with two fluorophores, allows for direct readout of hRNR dimeric and hexameric states. We applied the newly developed platform to reveal the timescales of α self‐assembly, driven by the feedback regulator dATP. This information is currently unavailable, despite the pharmaceutical relevance of hRNR oligomeric regulation.     

Learning to live in a smart home

ABSTRACT

Smart homes promise to significantly enhance domestic comfort, convenience, security and leisure whilst simultaneously reducing energy use through optimized home energy management. Their ability to achieve these multiple aims rests fundamentally on how they are used by householders, yet very little is currently known about this topic. The few studies that have explored the use of smart homes have tended to focus on special-interest groups and be quite short-term. This paper reports on new in-depth qualitative data that explore the domestication of a range of smart home technologies in 10 households participating in a nine-month field trial. Four core themes emerge: (1) smart home technologies are both technically and socially disruptive; (2) smart homes require forms of adaptation and familiarization from householders that can limit their use; (3) learning to use smart home technologies is a demanding and time-consuming task for which there is currently very little support available; and (4) there is little evidence that smart home technologies will generate substantial energy savings and, indeed, there is a risk that they may generate forms of energy intensification. The paper concludes by discussing the implications of these findings for policy, design and further research.