Control Signal Research Articles

Time-varying formation tracking of multi-agent systems with multiple leaders based on aperiodic time-triggered intermittent control

In this article, the distributed aperiodic time-triggered intermittent control protocol (DATICP) is presented for the time-varying formation tracking of multi-agent systems (MASs) with multiple leaders. Firstly, we present the mathematical description of DATICP. The characteristic of DATICP lies in that for each agent, its control signal is updated at aperiodic sampling instants on work time intervals and disappears on rest time intervals. Secondly, based on a mixed time-dependent Lyapunov functional (MTLF), the exponential stability theorem of the derived system is established. Thirdly, according to the theorem, sufficient conditions are given to achieve time-varying formation tracking for MASs with multiple leaders by DATICP. As a result, a corollary is obtained which compromises conservativeness and complexity. Finally, two simulation examples are offered for illustration.

Dynamic event-triggered prescribed performance disturbance rejection fault-tolerant trajectory tracking for surface vehicles

In this article, a dynamic event-triggered prescribed performance disturbance rejection fault-tolerant scheme is designed for the trajectory tracking of surface vehicles. The external time-varying disturbances and loss-of-effectiveness thruster faults are addressed separately. By constructing the separate observers, the external time-varying disturbances and the loss-of-effectiveness thruster faults are on-line estimated, respectively. The prescribed performance and error transformation functions enable the transient and steady-state performance to settle within pre-specified function values. The dynamic event-triggering is introduced so that commanded control signals are temporarily kept under predefined conditions to reduce wear and tear on the thrusters especially in harsh environmental condition. With the aid of the vectorial backstepping technique, the finial control law is derived such that the position and heading of vehicles can be forced to track the reference trajectories in disturbance and fault conditions. Application example on a 1:70 scaled model vessel in the different cases clarify the scheme.

Approximation-based adaptive two-bit-triggered bipartite tracking control for nonlinear networked MASs subject to periodic disturbances

Purpose This paper aims to investigate the problem of adaptive bipartite tracking control in nonlinear networked multi-agent systems (MASs) under the influence of periodic disturbances. It considers both cooperative and competitive relationships among agents within the MASs. Design/methodology/approach In response to the inherent limitations of practical systems regarding transmission resources, this paper introduces a novel approach. It addresses both control signal transmission and triggering conditions, presenting a two-bit-triggered control method aimed at conserving system transmission resources. Additionally, a command filter is incorporated to address the problem of complexity explosion. Furthermore, to model the uncertain nonlinear dynamics affected by time-varying periodic disturbances, this paper combines Fourier series expansion and radial basis function neural networks. Finally, the effectiveness of the proposed methodology is demonstrated through simulation results. Findings Based on neural networks and command filter control method, an adaptive two-bit-triggered bipartite control strategy for nonlinear networked MASs is proposed. Originality/value The proposed control strategy effectively addresses the challenges of limited transmission resources, nonlinear dynamics and periodic disturbances in networked MASs. It guarantees the boundedness of all signals within the closed-loop system while also ensuring effective bipartite tracking performance.

Effects of the restrictive cardiomyopathy-associated cardiac troponin I K178E mutation on cAMP signalling at the myofilament

Abstract Background Catecholaminergic stimulation of cardiac β-adrenergic receptors (β-ARs) and subsequent cAMP signalling control cardiac inotropy and lusitropy. cAMP signalling is known to be compartmentalised to optimise sympathetic control by producing heterogeneous cAMP signals and protein kinase A (PKA) activation at distinct subcellular sites. This signalling may be disrupted in diseases such as restrictive cardiomyopathy (RCM) characterised by diastolic dysfunction, which is associated with certain mutations including the K178E missense mutation in cardiac troponin I (TPNI). The impaired ventricular relaxation associated with this mutation is thought to arise from myofilament calcium hypersensitivity, which can be modulated by PKA-dependent TPNI phosphorylation. However, a detailed understanding of the pathological effects mediated by the K178E-TPNI mutation is lacking. Purpose This study aims to determine whether the K178E-TPNI mutation alters local cAMP/PKA signalling at TPNI, potentially affecting myofilament calcium sensitivity and cardiac myocyte relaxation. Methods Real-time imaging of cAMP was carried out using the Fluorescence Resonance Energy Transfer (FRET)-based sensor CUTie (cAMP Universal Tag for imaging experiments) [1] fused to wild type (WT)- or K178E-TPNI. The sensors were expressed by transfection in neonatal rat ventricular myocytes (NRVMs) and FRET-changes were recorded at the myofilament on application of the β-AR stimulant isoproterenol (ISO). We further assessed the involvement of cAMP hydrolysing phosphodiesterases (PDEs) in the regulation of cAMP levels selectively at TPNI. Results The peak FRET-change and FRET-change after 5mins on ISO application were significantly higher in NRVMs expressing the mutant compared to the WT sensor, as was the rate of cAMP accumulation. A strong trend showed that the rise in FRET following PDE4 inhibition was larger in NRVMs expressing the WT compared to the mutant sensor, suggesting the involvement of this isoform in degrading cAMP locally at TPNI and reduced PDE4 activity in the presence of the K178E mutation. Co-immunoprecipitation analysis in HEK293 cells demonstrated interaction between WT-TPNI and a PDE4D isoform, and a weaker interaction of this isoform with the K178E-TPNI mutant. Conclusions This study shows that the K178E-TPNI mutation attenuates the TPNI/PDE4D interaction and alters the magnitude and kinetics of the local cAMP response at the myofilament. This mechanistic insight may aid the development of therapeutics targeting the TPNI/PDE4D interaction to treat RCM-associated diastolic dysfunction.The TPNI-CUTie SensorFRET Response Trace

Compliant Grasp Control Method for the Underactuated Prosthetic Hand Based on the Estimation of Grasping Force and Muscle Stiffness with sEMG

Human muscles can generate force and stiffness during contraction. When in contact with objects, human hands can achieve compliant grasping by adjusting the grasping force and the muscle stiffness based on the object’s characteristics. To realize humanoid-compliant grasping, most prosthetic hands obtain the stiffness parameter of the compliant controller according to the environmental stiffness, which may be inconsistent with the amputee’s intention. To address this issue, this paper proposes a compliant grasp control method for an underactuated prosthetic hand that can directly obtain the control signals for compliant grasping from surface electromyography (sEMG) signals. First, an estimation method of the grasping force is established based on the Huxley muscle model. Then, muscle stiffness is estimated based on the muscle contraction principle. Subsequently, a relationship between the muscle stiffness of the human hand and the stiffness parameters of the prosthetic hand controller is established based on fuzzy logic to realize compliant grasp control for the underactuated prosthetic hand. Experimental results indicate that the prosthetic hand can adjust the desired force and stiffness parameters of the impedance controller based on sEMG, achieving a quick and stable grasp as well as a slow and gentle grasp on different objects.

Thermal modelling and layout optimization of GaN half-bridge IC with integrated drivers and power HEMTs

The paper presents the results of thermal modeling of a half-bridge monolithic integrated circuit (IC) with integrated drivers and enhanced mode power high electron mobility transistors, based on a GaN-on-SOI heterostructure. It had been established that the main heat sources in the IC were the half-bridge GaN HEMTs. The heat from the half-bridge GaN HEMTs propagates in the chip and leads to heating of the logic block and gate drivers. Heating of half-bridge GaN HEMTs leads to increased channel resistance and IC output current drop. Heating of the gate drivers reduces driving current, as a result, increases the switching time of the half-bridge GaN HEMTs. Heating of the logic block increases the rise and fall times of the generated control signals, which worsens the dynamic characteristics of the IC. A comparative analysis of heat propagation for IC dies based on GaN-on-SOI and GaN-on-Si heterostructures showed that GaN-on-SOI structure has a 40% greater junction-to-backside thermal resistivity compared to GaN-on-Si structure. In this case, the specific thermal resistance in the direction of heat propagation from the hotspot of the transistor to the backside of the die for the GaN-on-SOI structure is almost two orders of magnitude greater than in the direction of its propagation to the frontside of the chip. The results obtained were used for IC layout optimization. The rearrangement of GaN-on-SOI IC functional blocks, as well as to introduction of additional heat-spreading elements on the frontside of chip were carried out during the optimization.

Optogenetics and Its Transformative Role in Tissue Engineering and Regenerative Medicine

ABSTRACTOptogenetics, a revolutionary technique leveraging light stimulation for precise cellular control, holds immense potential in regenerative medicine. Offering unparalleled spatial and temporal accuracy, the entrance of optogenetics into tissue engineering and regenerative medicine (TERM) empowers researchers to modify genes precisely and reversibly, control signal pathways and antibodies, as well as alter biomaterial properties. Optogenetics provides unprecedented control in the manipulation of physiological regeneration, replication of intricate developmental processes, and tissue engineering in a laboratory setting. Although further investigation is required for the safe and feasible injection of optogenetic systems into human bodies, the use of optogenetics has already led to a great amount of research on several tissues and systems. It has found diverse applications in TERM of skin and connective tissue, endothelium, bone, cartilage, and muscle; with researchers leveraging optogenetics for precise control over the in vitro or in vivo production of these tissues, the investigation of critical proteins and pathways, the creation of light‐controlled wound coverings and even as a tool for directional tissue growth in living subjects. Optogenetics emerges as a transformative force in shaping the future of medical science, demonstrating a pivotal role in advancing regenerative medicine and paving the way for innovative therapeutic strategies.

Coordination of distributed adaptive signal control and advisory speed optimization based on shockwave theory

AbstractThis paper presents a distributed adaptive signal control and advisory speed coordination method based on shockwave theory, which accommodates diverse traffic conditions. In order to assess signal control efficiency under various scenarios, an innovative evaluation index termed synthetic delay is introduced based on the analysis of traffic dynamics at intersections. Considering the formation and dissipation of queue, and flow fluctuation of incoming traffic, it automatically evaluates control delay and throughput with distinctive significances. The distributed adaptive control method calculates the optimal green time in real time to minimize total synthetic delay at intersections. Furthermore, the coordination of advisory speed with the signal control schemes is addressed to ensure smooth progressions for vehicles. The proposed method considers the saturation of traffic and upstream traffic flow changes, leading to adaptability to changing traffic scenarios and effective coordination of traffic control. Several simulations were conducted and compared with the proposed method with other control methods. The results demonstrate that the proposed methods reduce the control delay and increase intersection throughput remarkably under different traffic saturations, confirming their effectiveness.

Practical guidelines and challenges in the isolation and characterization of microplastics/microfibers by Raman microscopy

We address some challenges usually encountered in the analysis of microplastics (MPs) and microfibers (MFs) using Raman microscopy. Those issues are examined considering that the researchers that carry out the collection and analysis of MP contamination may not have necessarily specialized expertise in Raman microscopy or polymer chemistry. Topics such as effective particle isolation or the use of adequate substrates are approached on the base of the impact they have on the spectroscopic characterization. Issues as the control of background signal, the influence of sample digestion, and the presence of internal interferences such as pigments, dyes, and fillers, are discussed. Spectral features of the polymer families found as MP/MF contaminants are presented based upon polymer structure, properties, and applications. The use of open-source libraries to complement chemical identification is also discussed. Overall, this work aims to enhance the practice and understanding of Raman microscopy for researchers engaged in characterizing MP/MF contaminants.

An Improved Vehicle Path-Tracking Model Based on Adaptive Nonlinear Model Predictive Control via Online Big Bang—Big Crunch Algorithm and Artificial Neural Network

<div>In this article, a novel tuning approach is proposed to obtain the best weights of the discrete-time adaptive nonlinear model predictive controller (AN-MPC) with consideration of improved path-following performance of a vehicle at different speeds in the NATO double lane change (DLC) maneuvers. The proposed approach combines artificial neural network (ANN) and Big Bang–Big Crunch (BB–BC) algorithm in two stages. Initially, ANN is used to tune all AN-MPC weights online. Vehicle speed, lateral position, and yaw angle outputs from many simulations, performed with different AN-MPC weights, are used to train the ANN structure. In addition, set-point signals are used as inputs to the ANN. Later, the BB–BC algorithm is implemented to enhance the path-tracking performance. ANN outputs are selected as the initial center of mass in the first iteration of the BB–BC algorithm. To prevent control signal fluctuations, control and prediction horizons are kept constant during the simulations. The results showed that all AN-MPC weights are successfully tuned online and updated during the maneuvers, and the path-following performance of the ego vehicle is improved at different NATO DLC speeds using the proposed ANN + BB–BC, compared to the method where ANN is used only.</div>

Stepwise release of Activin-A from its inhibitory prodomain is modulated by cysteines and requires furin coexpression to promote melanoma growth

The Activin-A precursor dimer can be cleaved by furin, but how this proteolytic maturation is regulated in vivo and how it facilitates access to signaling receptors is unclear. Here, analysis in a syngeneic melanoma grafting model shows that without furin coexpression, Activin-A failed to accelerate tumor growth, correlating with failure of one or both subunits to undergo cleavage in signal-sending cells, even though compensatory processing by host cells nonetheless sustained elevated circulating Activin-A levels. In reporter assays, furin-independent cleavage of one subunit enabled juxtacrine Activin-A signaling, whereas completion of proteolytic maturation by coexpressed furin or by recipient cells stimulated contact-independent activity, crosstalk with BMP receptors, and signal inhibition by follistatin. Mechanistically, Activin-A processing was modulated by allosteric disulfide bonds flanking the furin site. Disruption of these disulfide linkages with the prodomain enabled Activin-A binding to cognate type II receptors independently of proteolytic maturation. Stepwise proteolytic maturation is a novel mechanism to control Activin-A protein interactions and signaling.

Selective collaboration in distributed FxLMS active noise control systems

This paper addresses the selection of the collaboration configuration on distributed active noise control (ANC) systems. ANC systems aim to cancel out acoustic noise within a listening area. In distributed systems, the control task is delegated among multiple acoustic control nodes that generate the control signals by filtering a noise reference signal. The coefficients of each node filter are iteratively calculated using the filtered-X LMS algorithm. The stability is achieved when the adaptive filters computed in each node converge to finite values. However, acoustic coupling among nodes could lead to instability (i.e., divergence). Collaboration among selected nodes may avoid this phenomenon, although not just any collaboration configuration guarantees network stability. On the other hand, a collaborative distributed system presents two drawbacks: the stability assessment is computationally expensive, and communication requirements increase with the number of collaborations among nodes. In this paper, we propose and discuss several methods to establish a collaboration configuration that ensures system stability. The optimal configuration, which is characterized by the minimal number of necessary collaborations between nodes, can be identified through exhaustive search. However, this approach incurs a high computational cost, particularly in networks with many nodes. To address this challenge, we introduce several heuristic methods aimed at efficiently obtaining stable configurations.

Deep predictive data representation model control for photovoltaic maximum power point tracking under partial shading conditions

As deep learning progresses in recent years, deep neural networks (DNNs) have penetrated applications in various industries. The global maximum power point (GMPP) tracking issue exists in photovoltaic (PV) systems. This study proposes a deep predictive data representation model control (PDRC)-based PV GMPP tracking method. The PDRC approach establishes a data representation control model centered on deep prediction by extracting the data features such as the current, voltage, and duty cycle of PV systems controllers on a time series basis. The proposed PDRC method can receive signals of voltage and current, then output duty cycle control signals. Thereby PDRC method can control PV output power to track GMPP efficiently. Finally, the proposed PDRC approach is evaluated with the perturbation observation method (P&O), the improved particle swarm algorithm (PSO-P&O), and the modified cuckoo algorithm (CS-P&O) to compare the PV GMPP tracking performance. In the simulation experiments, the PDRC approach increases the average tracking efficiency over the comparison algorithms by at least 7.729 % and decreases the average tracking time by at least 0.0155 s under partial shading conditions (PSCs). In the physical simulation validation, the proposed PDRC approach possesses minimal power perturbation and increases the average tracking efficiency over the comparison algorithms by at least 0.407 %.

Asynchronous decentralized traffic signal coordinated control in urban road network

AbstractThis study introduces an asynchronous decentralized coordinated signal control (ADCSC) framework for multi‐agent traffic signal control in the urban road network. The controller at each intersection in the network optimizes its signal control decisions based on a prediction of the future traffic demand as an independent agent. The asynchronous framework decouples the entangled interdependence between decision‐making and state prediction among different agents in decentralized coordinated decision‐making problems, enabling agents to proceed with collaborative decision‐making without waiting for other agents’ decisions. Within the proposed ADCSC framework, each controller dynamically optimizes its signal timing strategy with a unique rolling horizon scheme. The scheme's individualized parameters for each controller are determined based on the vehicle travel time between the adjacent intersections, ensuring that controllers can make informed control decisions with accurate arrival flow information from upstream intersections. The signal optimization problem is formulated as a mixed integer linear program model, which adopts a flexible signal scheme without a fixed phase structure and sequence. Simulation results demonstrate that the proposed ADCSC strategy significantly outperforms the benchmark signal coordination methods in terms of average delay, travel speed, stop numbers, and energy consumption. Experimental analysis on computation time validates the applicability of the proposed optimization model for real‐time implementation. Sensitivity analysis on key parameters in the framework is conducted, offering insights for parameter selection in practice. Furthermore, the ADCSC framework is extended to a road network in Qinzhou City, China, with 45 signalized intersections, demonstrating its effectiveness and scalability in the real‐world road network.

Droplet Impedance Feedback-Enabled Microsampling Microfluidic Device for Precise Chemical Information Monitoring.

Microelectrodes have transformed our understanding of spatiotemporal responses to electrical stimulation. However, biological signals are often molecular, complicating the capture of intricate chemical signals. The microfluidic chip developed in this paper accurately measures droplet volume by using impedance analysis. The utilization of droplet volume as a feedback signal for precise microsampling pressure control ensures that microsampling remains unaffected by droplet volume influence. Once the microsampling is complete, chemiluminescence detection enables high temporal resolution and continuous and sensitive monitoring of chemical information within the droplets. Experimental verification shows that the chip can avoid volume influence through impedance feedback, achieving consistent and stable microampling at the nanoliter level (0-3 nL). In just 0.3 s, it can perform sensitive chemiluminescence detection of H2O2 and glucose within droplets. The linear detection ranges for these analytes are 10-50,000 and 20-600 μM, respectively, with the limit of detection being 0.648 and 0.334 μM. The significance of this chip lies in its ability to reveal changes in both electrical and chemical signals during transient biological processes. Its potential applications are numerous, encompassing a wide range of emerging areas such as single-cell analysis, cell communication, and cellular immunity.

EIF4F controls ERK MAPK signaling in melanomas with BRAF and NRAS mutations

The eIF4F translation initiation complex plays a critical role in melanoma resistance to clinical BRAF and MEK inhibitors. In this study, we uncover a function of eIF4F in the negative regulation of the rat sarcoma (RAS)/rapidly accelerated fibrosarcoma (RAF)/mitogen-activated protein kinase kinase (MEK)/extracellular signal-regulated kinase (ERK) mitogen-activated protein kinase (MAPK) signaling pathway. We demonstrate that eIF4F is essential for controlling ERK signaling intensity in treatment-naïve melanoma cells harboring BRAF or NRAS mutations. Specifically, the dual-specificity phosphatase DUSP6/MKP3, which acts as a negative feedback regulator of ERK activity, requires continuous production in an eIF4F-dependent manner to limit excessive ERK signaling driven by oncogenic RAF/RAS mutations. Treatment with small-molecule eIF4F inhibitors disrupts the negative feedback control of MAPK signaling, leading to ERK hyperactivation and EGR1 overexpression in melanoma cells in vitro and in vivo. Furthermore, our quantitative analyses reveal a high spare signaling capacity in the ERK pathway, suggesting that eIF4F-dependent feedback keeps the majority of ERK molecules inactive under normal conditions. Overall, our findings highlight the crucial role of eIF4F in regulating ERK signaling flux and suggest that pharmacological eIF4F inhibitors can disrupt the negative feedback control of MAPK activity in melanomas with BRAF and NRAS activating mutations.

Renewable Energy-Based Electric Drive with a Novel Control Technique for Smooth Power-Sharing

Energy management between different power sources, which are used to feed Electric vehicles (EVs) is one of the complex scenarios. Normally, power management is done based on the Electric vehicle load requirements. In this work, three different energy sources are considered, in that one is an active energy source and two are passive energy storage elements. The Photo Voltaic (PV) based energy source is considered an active element, on the other hand, the Battery sand supercapacitor (SCap) is treated as a passive type of element. To manage the power flow between two energy sources, a novel control technique is adopted by considering the speed and current of the electric motor as major input parameters known as the Measurement of the parameter-based controller (MPBC). The Measurement of a parameter-based controller is used to control the pulse signals of the Bidirectional converters connected at the battery and supercapacitor ends, and the required pulse signals can be generated by the Conventional Proportional Integral (PI) controller. The main circuit is implemented with a novel hybrid controller which is used to provide controlled pulse signals to the bidirectional converters connected to the battery and supercapacitor. Finally, the MATLAB/Simulink model was developed with the novel hybrid controller and examined the performance at different load conditions of the motor by considering three different states of power delivered by the PV array.

Semi-active vibration control via a magnetorheological damper and active disturbance rejection control

This work provides an alternative for the semi-active vibration control problem via state feedback plus an active disturbance rejection control (K-ADRC) for a multistory building structure equipped with one magnetorheological damper (MRD) located between the ground and the first story. This control law introduces a disturbance observer (DOB) to estimate the total disturbance in real time, produced by the earthquake perturbation, unmodeled dynamics, and parametric uncertainties, and then cancel their effect using a part of the control signal. Moreover, using a diffeomorphism, the K-ADRC provides a proportional derivative (PD) controller designed to improve internal stability. A prominent feature of the active disturbance rejection control (ADRC) algorithm is that the value of the DOB gain is selected according to the system bandwidth. The stability of the system is verified via Lyapunov analysis. Moreover, the building structure model and the MRD are accomplished by incorporating a nonlinearity state that represents the hysteresis effect to perform real behavior in addition to assuming that acceleration is the only available measurement of building structures. Thus, integral filters are provided based on the appropriate cut-off frequency to estimate displacement and velocity, avoiding bias, offset, and phase shifts. Experimental results from a reduced-scale two-story building prototype demonstrate that the proposed K-ADRC is promising for real applications, and it has better performance than the state feedback (K) and the linear quadratic regulator (LQR) control techniques.

Evaluating the environmental benefits of autonomous vehicles in urban intersections: a microscopic simulation approach

This research delves into the environmental and energy implications of incorporating autonomous vehicles (AVs) into urban traffic systems, mainly focusing on emissions and fuel efficiency. The study employed PTV VISSIM simulation software to model a four-leg signalized intersection in Balgat, Ankara, under varying levels of AV integration and driving behaviors. A total of 21 scenarios were simulated, assessing the impact of cautious, normal, aggressive, and platooning AV behaviors on emissions of CO, NOx, and VOCs, as well as fuel consumption, across different traffic signal cycle durations. The results indicate that shorter signal cycle times consistently significantly reduce emissions and fuel consumption, irrespective of AV driving behavior. The most notable improvements were observed in platooning scenarios, attributed to their optimized traffic flow. In contrast, longer cycle times increased emissions and fuel consumption, especially with human-driven and cautious AVs, due to more frequent idling and stop-and-go traffic patterns. This study highlights the importance of refining AV driving algorithms and optimizing signal control systems to reduce environmental impacts and improve fuel efficiency in urban settings, providing crucial insights for advancing sustainable urban mobility and traffic management strategies.

Modular Plasmonic Nanopore for Opto‐Thermal Gating

AbstractSolid‐state nanopore gating inspired by biological ion channels is gaining increasing traction due to a large range of applications in biosensing and drug delivery. Integration of stimuli‐responsive molecules such as poly(N‐isopropylacrylamide) (PNIPAM) inside nanopores can enable temperature‐dependent gating, which so far has only been demonstrated using external heaters. In this work, plasmonic resonators are combined inside the nanopore architecture with PNIPAM to enable optical gating of individual or multiple nanopores with micrometer resolution and a switching speed of a few milliseconds by thermo‐plasmonics effects. A temperature change of 40 kelvin per millisecond is achieved and demonstrates the efficacy of this method using nanopore ionic conductivity measurements that enable selective activation of individual nanopores in an array. Moreover, the selective gating of specific nanopores in an array can set distinct ionic conductance levels: low, medium, and high (i.e., “0,” “1,” and “2”), which can be exploited for logical gating with optical signal control. Such selective optical gating in nanopore arrays marks a breakthrough in nanofluidics, as it paves the way toward smart devices that offer multifunctional applications including biosensing, targeted drug delivery, and fluidic mixing.