Local Feedback Research Articles

Hierarchical Cooperative Control of Connected Vehicles: From Heterogeneous Parameters to Heterogeneous Structures

As one of the typical applications of connected vehicles (CVs), the vehicle platoon control technique has been proven to have the advantages of reducing emissions, improving traffic throughout and driving safety. In this paper, a unified hierarchical framework is designed for cooperative control of CVs with both heterogeneous model parameters and structures. By separating neighboring information interaction from local dynamics control, the proposed framework is designed to contain an upper-level observing layer and a lower-level tracking control layer, which helps address the heterogeneity in vehicle parameters and structures. Within the proposed framework, an observer is designed for following vehicles to observe the leading vehicle's states using neighboring communication, while a tracking controller is designed to track the observed leading vehicle using local feedback control. Closed-loop stability in the absence and presence of communication time delay is analyzed, and the observer is further extended to a finite time convergent one to address string stability under general communication topology. Numerical simulation and field experiment verify the effectiveness of the proposed method.

Consensus Control in Heterogeneous Nonlinear Multiagent Systems With Position Feedback and Switching Topologies

The primary challenge in designing a consensus algorithm under switching topologies is to deal with discontinuous interactive information. At present, most consensus control results are limited to either agents with mismatched uncertainties in a time-invariant interaction environment or integrator dynamics in a dynamically changing interaction environment. This work addresses consensus problems of heterogeneous nonlinear multiagent systems without these limitations. We propose a novel, simple, yet effective higher-order filter to yield differentiable control variables employing neighbor position information and discontinuous topology information. By applying this filter and defining a suitable auxiliary error, we develop distributed consensus controllers for second-order and uncertain higher-order systems. One of the salient attributes of the proposed methodology is to allow each agent to require minimal local feedback, i.e., only the position information of itself and its neighboring agents. Theoretical and simulation verifications of the scheme guaranteeing asymptotic convergence of consensus and the boundedness of each signal in the closed-loop system are carefully examined.

Absolutely Stable Spatiotemporal Order in Noisy Quantum Systems.

We introduce a model of nonunitary quantum dynamics that exhibits infinitely long-lived discrete spatiotemporal order robust against any unitary or dissipative perturbation. Ergodicity is evaded by combining a sequence of projective measurements with a local feedback rule that is inspired by Toom's "north-east-center" classical cellular automaton. The measurements in question only partially collapse the wave function of the system, allowing some quantum coherence to persist. We demonstrate our claims using numerical simulations of a Clifford circuit in two spatial dimensions which allows access to large system sizes, and also present results for more generic dynamics on modest system sizes. We also devise explicit experimental protocols realizing this dynamics using one- and two-qubit gates that are available on present-day quantum computing platforms.

Student engagement with teacher written feedback: Insights from low-proficiency and high-proficiency L2 learners

Although studies on teacher written feedback have proliferated in recent years, much remains to be discovered about L2 learners' engagement with this practice. To bridge this gap, the study examined how low-proficiency and high-proficiency students engaged with teacher written feedback affectively, cognitively, and behaviorally in a Chinese EFL context. The study collected data from multiple sources including students' first drafts of their writing, teacher written feedback, revised drafts, students' verbal reports, and semi-structured interviews. The findings revealed that for this important pedagogical practice, students' engagement with teacher written feedback was mediated by their language proficiency and feedback focus. Specifically, while LP and HP students' engagement with local feedback was quite different, they shared similarities in their engagement with global feedback. The study further found a complex and nonlinear relationship across the three perspectives of engagement (i.e., affective/cognitive/behavioral), which was evidenced in the consistencies and inconsistencies of the students’ engagement. The important pedagogical implications from this study are discussed.

Neurogliaform cells mediate feedback inhibition in the medial entorhinal cortex.

Layer I of the medial entorhinal cortex (MEC) contains converging axons from several brain areas and dendritic tufts originating from principal cells located in multiple layers. Moreover, specific GABAergic interneurons are also located in the area, but their inputs, outputs, and effect on local network events remain elusive. Neurogliaform cells are the most frequent and critically positioned inhibitory neurons in layer I. They are considered to conduct feed-forward inhibition via GABAA and GABAB receptors on pyramidal cells located in several cortical areas. Using optogenetic experiments, we showed that layer I neurogliaform cells receive excitatory inputs from layer II pyramidal cells, thereby playing a critical role in local feedback inhibition in the MEC. We also found that neurogliaform cells are evenly distributed in layer I and do not correlate with the previously described compartmentalization (“cell islands”) of layer II. We concluded that the activity of neurogliaform cells in layer I is largely set by layer II pyramidal cells through excitatory synapses, potentially inhibiting the apical dendrites of all types of principal cells in the MEC.

Prevalence and scalable control of localized networks

The ability to control network dynamics is essential for ensuring desirable functionality of many technological, biological, and social systems. Such systems often consist of a large number of network elements, and controlling large-scale networks remains challenging because the computation and communication requirements increase prohibitively fast with network size. Here, we introduce a notion of network locality that can be exploited to make the control of networks scalable, even when the dynamics are nonlinear. We show that network locality is captured by an information metric and is almost universally observed across real and model networks. In localized networks, the optimal control actions and system responses are both shown to be necessarily concentrated in small neighborhoods induced by the information metric. This allows us to develop localized algorithms for determining network controllability and optimizing the placement of driver nodes. This also allows us to develop a localized algorithm for designing local feedback controllers that approach the performance of the corresponding best global controllers, while incurring a computational cost orders-of-magnitude lower. We validate the locality, performance, and efficiency of the algorithms in Kuramoto oscillator networks, as well as three large empirical networks: synchronization dynamics in the Eastern US power grid, epidemic spreading mediated by the global air-transportation network, and Alzheimer's disease dynamics in a human brain network. Taken together, our results establish that large networks can be controlled with computation and communication costs comparable to those for small networks.

High current efficiency fast transient gain-boosted single-stage bulk-driven OTA with enhanced input stage suitable for heavy capacitive loads

This article presents a 0.5 V operated, bulk-driven subthreshold OTA with up to 30 nF capacitive load driving capability. The input stage utilizing three unmatched pseudo-differential pairs is biased with two DC level shifters, especially bulk-driven flipped voltage followers in a direct and cross-coupled connection, exhibiting superior performance to existing bulk-driven OTAs. The input transconductance stage with local common-mode feedback (LCMFB) followed by partial positive feedback (PPF) contributes a large dynamic output current using three-successive amplification. As a result, the proposed OTA provides 24.2 times higher effective input core transconductance, 2.62 times higher DC gain, and 11.5 times more slew rate than a conventional class-A BD OTA with PPF at 5% lower static power. The post-layout results of the proposed OTA for a 10 nF capacitive load using UMC 0.18-µm technology in Cadence Virtuoso-XL yield a DC gain of 124.6 dB, a UGF of 183.47 kHz, a PM of 89.98°, and greater figures of merit (FOM) for small- and large-signal performance than the state-of-the-art bulk-driven OTAs.

Decentralized Control of Multiagent Systems Using Local Density Feedback

In this article, we stabilize a discrete-time Markov process evolving on a compact subset of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathbb {R}^d$</tex-math></inline-formula> to an arbitrary target distribution that has an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$L^\infty (\cdot)$</tex-math></inline-formula> density and does not necessarily have a connected support on the state space. We address this problem by stabilizing the corresponding Kolmogorov forward equation, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> mean-field model <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> of the system, using a density-dependent transition Kernel as the control parameter. Our main application of interest is controlling the distribution of a multiagent system in which each agent evolves according to this discrete-time Markov process. To prevent agent state transitions at the equilibrium distribution, which would potentially waste energy, we show that the Markov process can be constructed in such a way that the operator that pushes forward measures is the identity at the target distribution. In order to achieve this, the transition kernel is defined as a function of the current agent distribution, resulting in a nonlinear Markov process. Moreover, we design the transition kernel to be <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">decentralized</i> in the sense that it depends only on the local density measured by each agent. We prove the existence of such a decentralized control law that globally stabilizes the target distribution. Furthermore, to implement our control approach on a finite <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$N$</tex-math></inline-formula> -agent system, we smoothen the mean-field dynamics via the process of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mollification</i> . We validate our control law with numerical simulations of multiagent systems with different population sizes. We observe that as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$N$</tex-math></inline-formula> increases, the agent distribution in the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$N$</tex-math></inline-formula> -agent simulations converges to the solution of the mean-field model, and the number of agent state transitions at equilibrium decreases to zero.

Links between Climate Sensitivity and the Large-Scale Atmospheric Circulation in a Simple General Circulation Model

Abstract Thermodynamical and dynamical aspects of the climate system response to anthropogenic forcing are often considered in two distinct frameworks: the former in the context of the forcing–feedback framework, and the latter in the context of eddy–mean flow feedbacks and large-scale thermodynamic constraints. Here we use experiments with the dynamical core of a general circulation model (GCM) to provide insights into the relationships between these two frameworks. We first demonstrate that the climate feedbacks and climate sensitivity in a dynamical core model are determined by its prescribed thermal relaxation time scales. We then perform two experiments: one that explores the relationships between the thermal relaxation time scale and the climatological circulation, and a second that explores the relationships between the thermal relaxation time scale and the circulation response to a global warming–like forcing perturbation. The results indicate that shorter relaxation time scales (i.e., lower climate sensitivities in the context of a dynamical core model) are associated with 1) a more vigorous large-scale circulation, including increased thermal diffusivity and stronger and more poleward storm tracks and eddy-driven jets, and 2) a weaker poleward displacement of the storm tracks and eddy-driven jets in response to the global warming–like forcing perturbation. Interestingly, the circulation response to the forcing perturbation effectively disappears when the thermal relaxation time scales are spatially uniform, suggesting that the circulation response to homogeneous forcing requires spatial inhomogeneities in the local feedback parameter. Implications for anticipating the circulation response to global warming and thermodynamic constraints on the circulation are discussed.

Green Sahara tipping points in transient climate model simulations of the Holocene

The ‘Greening’ and subsequent desertification of the Sahara during the early to mid-Holocene is a dramatic example of natural climate change. We analyse a suite of simulations with a newly palaeo-conditioned configuration of the HadCM3 coupled model that is able to capture an abrupt desertification of North Africa during this time. We find that this model crosses a threshold of moisture availability for vegetation at around 6000 years before present. The resultant rapid reduction in vegetation cover acts to reduce precipitation through moisture recycling and surface albedo feedbacks. Precursor drying events which are not directly forced also indicate that the model is close to a critical moisture level. Similar precursor-like events appear in a Holocene record from the East of the continent, hinting that the natural system may resemble some of the properties of this model simulation. The overall response is not fundamentally altered by the inclusion of solar irradiance variations or volcanic eruptions. The simulated timing of the abrupt transition is mostly controlled by orbital forcing and local positive feedbacks, but it is also modulated to some extent by the state of the atmosphere and ocean. Comparisons with proxy records across North Africa show good agreement with the model simulations, although the simulations remain overly dry in the East. Overall, a threshold response may present a useful model of the real transition, but more high-resolution palaeoclimate records would help to discriminate among the predictions of climate models.

A low-power low-noise neural recording amplifier with an improved recycling telescopic-cascode OTA

In this paper, a fully-differential low-power low-noise neural recording amplifier with a novel recycling telescopic-cascode (RTC) operational transconductance amplifier (OTA) is presented. In the proposed RTC OTA, the current recycling and cross-coupled transistors with local positive feedback are utilized to significantly improve the OTA’s parameters like small-signal DC gain and unity-gain bandwidth. The gain enhancement also improves the linearity in the closed-loop structure. Extensive analytical calculations and simulation results using TSMC 0.18-µm CMOS process are provided to examine the usefulness of the proposed OTA. The simulated neural recording amplifier achieves 3.55 µVrms input-referred noise over 1 Hz-10 kHz bandwidth with 3.03 and 1.22 noise efficiency factor for local field potential (LFP) and action potential (AP) bands, respectively. The total harmonic distortion (THD) is −41.7 dB for a 1 mVpp, 1 kHz sinusoidal input signal. The total power consumption is 1.70 µW from a single 1.8-V power supply.

Enhancing national audit through addressing the quality improvement capabilities of feedback recipients: a multi-phase intervention development study

BackgroundNational audits are a common, but variably effective, intervention to improve services. This study aimed to design an intervention to increase the effectiveness of national audit.MethodsWe used interviews, documentary analysis, observations, co-design and stakeholder engagement methods. The intervention was described in an intervention manual and illustrated using a logic model. Phase 1 described the current hospital response to a national audit. Phase 2 identified potential enhancements. Phase 3 developed a strategy to implement the enhancements. Phase 4 explored the feasibility of the intervention alongside the National Audit of Dementia and refined the intervention. Phase 5 adapted the intervention to a second national audit (National Diabetes Audit). Phase 6 explored the feasibility and fidelity of the intervention alongside the National Diabetes Audit and used the findings to further refine the intervention.ResultsThe developed intervention is a quality improvement collaborative (QIC), containing virtual educational workshop, virtual outreach for local team leads and virtual facilitation of a learning collaborative delivered after feedback has been received. The QIC aims to support national audit recipients to undertake improvement actions tailored to their local context. The target audience is clinical and clinical governance leaders. We found that actions from national audit were constrained by what the clinical lead perceived they deliver personally, these actions were not aligned to identified influences upon performance. We found that the hospital response could be enhanced by targeting low baseline performance, identifying and addressing influences upon to performance, developing trust and credibility, addressing recipient priorities, presenting meaningful comparisons, developing a conceptual model, involving stakeholders and considering the opportunity cost. Phase 3 found that an educational workshop and outreach strategy could support implementation of the enhancements through developing coherence and cognitive participation. We found feasibility could be increased by revising the content, re-naming the intervention, amending activities to address time commitment, incorporating a more structured analysis of influences, supporting collaboration and developing local feedback mechanisms. Phase 5 found adaptation to a second national audit involved reflecting differences in the clinical topic, context and contractual requirements. We found that the behaviour change techniques identified in the manual were delivered by facilitators. Participants reported positive attitudes towards the intervention and that the intervention was appropriate.ConclusionsThe QIC supports local teams to tailor their actions to local context and develop change commitment. Future work will evaluate the effectiveness of the intervention as an adjunct to the National Diabetes Audit.

Clouds drive differences in future surface melt over the Antarctic ice shelves

Abstract. Recent warm atmospheric conditions have damaged the ice shelves of the Antarctic Peninsula through surface melt and hydrofracturing and could potentially initiate future collapse of other Antarctic ice shelves. However, model projections with similar greenhouse gas scenarios suggest large differences in cumulative 21st-century surface melting. So far it remains unclear whether these differences are due to variations in warming rates in individual models or whether local feedback mechanisms of the surface energy budget could also play a notable role. Here we use the polar-oriented regional climate model MAR (Modèle Atmosphérique Régional) to study the physical mechanisms that would control future surface melt over the Antarctic ice shelves in high-emission scenarios RCP8.5 and SSP5-8.5. We show that clouds enhance future surface melt by increasing the atmospheric emissivity and longwave radiation towards the surface. Furthermore, we highlight that differences in meltwater production for the same climate warming rate depend on cloud properties and particularly cloud phase. Clouds containing a larger amount of supercooled liquid water lead to stronger melt, subsequently favouring the absorption of solar radiation due to the snowmelt–albedo feedback. As liquid-containing clouds are projected to increase the melt spread associated with a given warming rate, they could be a major source of uncertainties in projections of the future Antarctic contribution to sea level rise.

Robust Data-Driven and Fully Distributed Volt/VAR Control for Active Distribution Networks With Multiple Virtual Power Plants

This paper proposes a data-driven and fully distributed volt/var control (VVC) method for active distribution networks (ADNs) with multiple virtual power plants (VPPs), which is model-free and robust against bad data in measurements. The proposed scheme combines the advantages of a robust regression based feedback optimization algorithm and a revised alternating direction multiplier method (ADMM). Each VPP continuously obtains local system feedback to correct control strategies, and exchanges only limited information with its neighbors. Furthermore, faster convergence is achieved by applying Nesterov’s accelerated gradient method to the dual update in ADMM. The effectiveness of the method is demonstrated via numerical simulations using several distribution systems.

A Pre-Charge Tracking Technique in the 40 MHz High-Speed Switching 48-to-5 V GaN-Based DC–DC Buck Converter for Reducing Large Self-Commutation Loss and Achieving a High Efficiency of 95.4%

For automobile applications, a high-speed switching 48-to-5 V dc&#x2013;dc buck converter is implemented in the 0.5-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> CMOS and 0.5-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> gallium nitride (GaN)-on-Si processes. This proposed converter includes a pre-charge tracking technique, a temperature-dependent controlled gate driver, and an adaptive relaxation gate drive control technique. The measurement results of this article show that the efficiency can be increased by about 10.3&#x0025; and can ensure the driving current capacity of enhance GaN (E-GaN). The built-in local negative feedback loop improves the reliability and reduces <inline-formula> <tex-math notation="LaTeX">$R_{\mathrm{\scriptscriptstyle ON}}$ </tex-math></inline-formula> by 21&#x0025; to reach the peak efficiency as high as 95.4&#x0025;.

Sub-6 GHz Noise-cancelling Balun-LNTA with Dual-band Q-enhanced LC Notch Filter for 5G New Radio Cellular Applications

This paper presents a blocker-tolerant broadband balun-low-noise transconductance amplifier (LNTA) with a high-Q dual-band LC notch filter for 5G new radio (NR) sub-6GHz cellular applications. The proposed balun-LNTA employs a noise-cancelling 1:5 CG-CS balun-LNA topology with local feedback gm-boosting and modified current-bleeding techniques to support entire bands of sub-6 GHz 5G NR application with less than 5 dB NF performance and balanced output of balun-LNTA. The dual-band Q-boosted LC notch filter with a band-switchable differential inductor is also proposed to enhance blocker tolerance of the receiver by removing out-of-band blockers and strong transmitter leakage signals. Simulated in a 65-nm CMOS process, the designed balun-LNTA achieves a minimum noise figure of 2.38 dB, a maximum transconductance gain of 44 mS, an S11 of less than -10 dB over the frequency range of 50 MHz-6 GHz, an input-referred third-order intercept point of 1.82 dBm, and blocker rejection ratios of more than 15 dB. It consumes 5.9 mA from a nominal supply voltage of 1 V. Its active die area is 0.55 mm².

An Assessment of Short-term Global and East Asian Local Climate Feedbacks using New Radiative Kernels

An Assessment of Short-term Global and East Asian Local Climate Feedbacks using New Radiative Kernels

Sequence learning, prediction, and replay in networks of spiking neurons

Sequence learning, prediction and replay have been proposed to constitute the universal computations performed by the neocortex. The Hierarchical Temporal Memory (HTM) algorithm realizes these forms of computation. It learns sequences in an unsupervised and continuous manner using local learning rules, permits a context specific prediction of future sequence elements, and generates mismatch signals in case the predictions are not met. While the HTM algorithm accounts for a number of biological features such as topographic receptive fields, nonlinear dendritic processing, and sparse connectivity, it is based on abstract discrete-time neuron and synapse dynamics, as well as on plasticity mechanisms that can only partly be related to known biological mechanisms. Here, we devise a continuous-time implementation of the temporal-memory (TM) component of the HTM algorithm, which is based on a recurrent network of spiking neurons with biophysically interpretable variables and parameters. The model learns high-order sequences by means of a structural Hebbian synaptic plasticity mechanism supplemented with a rate-based homeostatic control. In combination with nonlinear dendritic input integration and local inhibitory feedback, this type of plasticity leads to the dynamic self-organization of narrow sequence-specific subnetworks. These subnetworks provide the substrate for a faithful propagation of sparse, synchronous activity, and, thereby, for a robust, context specific prediction of future sequence elements as well as for the autonomous replay of previously learned sequences. By strengthening the link to biology, our implementation facilitates the evaluation of the TM hypothesis based on experimentally accessible quantities. The continuous-time implementation of the TM algorithm permits, in particular, an investigation of the role of sequence timing for sequence learning, prediction and replay. We demonstrate this aspect by studying the effect of the sequence speed on the sequence learning performance and on the speed of autonomous sequence replay.

A Programmable Ontology Encompassing the Functional Logic of the Drosophila Brain.

The Drosophila brain has only a fraction of the number of neurons of higher organisms such as mice and humans. Yet the sheer complexity of its neural circuits recently revealed by large connectomics datasets suggests that computationally modeling the function of fruit fly brain circuits at this scale poses significant challenges. To address these challenges, we present here a programmable ontology that expands the scope of the current Drosophila brain anatomy ontologies to encompass the functional logic of the fly brain. The programmable ontology provides a language not only for modeling circuit motifs but also for programmatically exploring their functional logic. To achieve this goal, we tightly integrated the programmable ontology with the workflow of the interactive FlyBrainLab computing platform. As part of the programmable ontology, we developed NeuroNLP++, a web application that supports free-form English queries for constructing functional brain circuits fully anchored on the available connectome/synaptome datasets, and the published worldwide literature. In addition, we present a methodology for including a model of the space of odorants into the programmable ontology, and for modeling olfactory sensory circuits of the antenna of the fruit fly brain that detect odorant sources. Furthermore, we describe a methodology for modeling the functional logic of the antennal lobe circuit consisting of a massive number of local feedback loops, a characteristic feature observed across Drosophila brain regions. Finally, using a circuit library, we demonstrate the power of our methodology for interactively exploring the functional logic of the massive number of feedback loops in the antennal lobe.

Consensus reaching with consistency control in group decision making with incomplete hesitant fuzzy linguistic preference relations

Due to complicated decision environment and limited knowledge of decision makers, decision makers may provide incomplete hesitant fuzzy linguistic preference relations (IHFLPRs) in group decision making (GDM) problems. Furthermore, to guarantee that decision makers are non-random and logical and obtain decision results that are accepted by most decision makers, it is necessary to consider individual consistency control in consensus reaching processes (CRPs) for GDM problems. In this paper, some models are developed to manage incomplete information and consensus for GDM with IHFLPRs. First, we develop an optimization model to impute missing elements for an IHFLPR, in which the best additive consistency index (BCI) and the average additive consistency index (ACI) are utilized to measure the additive consistency level of an IHFLPR. Afterwards, for CRPs in GDM with IHFLPRs, some local adjustment strategy-based feedback adjustment rules are proposed to assist decision makers in modifying HFLPRs by considering the BCI and the ACI. Moreover, an iterative consensus reaching algorithm for GDM with IHFLPRs is further proposed. Eventually, a numerical example of 3D visualization management system selection for an automobile manufacturing enterprise is presented to elaborate the feasibility of the consensus reaching algorithm, and detailed numerical results are also provided to justify the algorithm.