Search Results (1,017)

This paper presents a comparative evaluation of quarter-car-model-based modular synthesis (QCMS) and full-car-based centralized synthesis (FCCS) for active suspension control in full-car systems. FCCS explicitly accounts for the coupled vertical, pitch, and roll dynamics by incorporating the geometric configuration of the sprung mass; however, this centralized formulation increases model complexity and controller–synthesis effort. In contrast, QCMS reduces the synthesis complexity by designing local suspension controllers using a quarter-car model and applying them modularly to the four suspension corners of a full-car system. Within both synthesis frameworks, linear quadratic (LQ) static output feedback (SOF) controllers and recursive-least-squares/extended-Kalman-filter (RLS/EKF)-based controllers are developed under comparable but structurally different control objectives. In particular, the proposed FCCS framework uses the geometric symmetry of the sprung mass not merely as a modeling assumption but as an explicit force-allocation structure that transforms the desired vertical force, roll moment, and pitch moment into four suspension actuator forces. Thus, four controllers are considered: LQSOF-QCMS and RLS/EKF-QCMS as modular quarter-car-based controllers, and LQSOF-FCCS and RLS/EKF-FCCS as centralized full-car-based controllers. In addition, the computational complexity of the LQSOF- and RLS/EKF-based controllers is compared in terms of their implementation burden. The main contribution of this study is not merely to show that the full-car-based FCCS improves the suppression of coupled body motions, but to clarify, under identical control and simulation conditions, the quantitative trade-off between the modular simplicity of QCMS and the symmetry-based centralized performance of FCCS. These controllers are evaluated through CarSim-based simulations under selected representative road-profile conditions in terms of ride comfort, motion-sickness mitigation, sensor requirements, and implementation complexity. The simulation results show that QCMS offers a low-complexity and modular implementation with acceptable ride-comfort performance, whereas FCCS justifies its increased synthesis and implementation burden when the suppression of coupled vertical, pitch, and roll motions and motion-sickness-related responses is required. Full article

Abiotic stresses, including drought, salinity, alkalinity, temperature extremes, flooding, heavy metals, and emerging pollutants, challenge plant growth and productivity by disturbing water relations, ion balance, redox homeostasis, membrane stability, energy metabolism, and developmental progression. Although substantial progress has been made in the identification of stress-responsive hormones, second messengers, kinases, transcription factors, transporters, and metabolic regulators, plant stress adaptation cannot be fully explained by linear signaling cascades or single tolerance genes. A major unresolved question is how early molecular events are reorganized into coordinated physiological and developmental outputs that support survival, recovery, and productivity. In this review, we propose an intermolecular interaction-driven adaptive remodeling framework for plant abiotic stress responses. This framework emphasizes that stress tolerance emerges from dynamic changes in receptor–ligand recognition, protein–protein interactions, calcium decoding, redox-sensitive modification, phosphorylation networks, transcriptional regulation, chromatin-associated control, and metabolite-mediated feedback. We further emphasize ROS as integrative redox switches that connect stress sensing, defense activation, senescence-related transitions, and recovery, and chromatin-associated mechanisms as regulators that may stabilize primed or memory-like adaptive states. We discuss how these interaction networks converge on core signaling hubs, including abscisic acid, reactive oxygen species, Ca²⁺, and kinase/phosphatase systems, and how they remodel stomatal behavior, root architecture, ion and pH homeostasis, redox buffering, metabolism, development, and reproductive resilience. We further highlight how natural variation, multi-omics, genome editing, high-throughput phenotyping, and field validation can translate interaction-centered stress biology into crop resilience. This perspective provides a conceptual bridge between molecular stress perception, network behavior, physiological adaptation, and climate-resilient agriculture. Full article

Parallel grid-forming energy storage converters based on virtual synchronous generator (VSG) control are prone to active power oscillation and interphase circulating current under load disturbance, unit switching, and parameter mismatch conditions. To address these problems, this paper proposes a dual-layer damping control strategy that combines adaptive virtual damping in the power loop with capacitor current feedback damping in the current loop. First, the small-signal models of the LCL filter, VSG power loop, and parallel converter system are established, and the dominant oscillation modes are analyzed using eigenvalue and participation factor methods. Then, an adaptive damping coefficient is designed according to the active power deviation and frequency dynamic response to suppress low-frequency power oscillation, while a capacitor current feedback branch is introduced to reshape the LCL filter’s resonant poles and attenuate circulating current resonance. Compared with the conventional fixed-damping VSG control, the proposed method reduces active power overshoot and accelerates power redistribution under load step and unit switching conditions. In the traditional control case, the active power peaks of VSG1 and VSG2 reach approximately 30 kW and 40 kW, with an oscillation period of about 1.8 s, whereas the proposed strategy suppresses the oscillatory process and enables the output powers to rapidly reach the preset sharing ratio. In addition, the system frequency can recover to the rated value of 50 Hz without obvious steady-state deviation, and the high-frequency component of the grid-connected current and the interphase circulating current are significantly attenuated. MATLAB/Simulink simulation results verify that the proposed dual-layer damping strategy provides better power oscillation suppression, circulating current mitigation, and frequency dynamic performance than the conventional VSG control. Full article

Motoneurons are under strong pressure to maintain stable motor output throughout an individual life, through homeostatic regulation of their electrical properties. Dysregulated spinal motoneuron excitability has long been implicated in the pathogenesis of amyotrophic lateral sclerosis (ALS). Recent work in SOD1^G93A mice suggests that the homeostatic response of motoneurons becomes dysregulated as cellular processes are disrupted by the disease, causing fluctuations in motoneuron electrical properties. Yet, few studies directly test whether ALS motoneurons respond differently than wild-type motoneurons to a common chronic perturbation. Here, we used in vivo electrophysiology to test whether motoneurons from pre-symptomatic SOD1^G93A mice modulate excitability differently than wild-type motoneurons in response to the same homeostatic perturbation: chronic inhibition exerted by the benzodiazepine diazepam. Using linear mixed-effects statistical models, we assessed whether diazepam treatment differentially modulated passive properties, firing behavior, spike properties, and/or synaptic inputs in SOD1^G93A versus wild-type motoneurons. We identified a significant genotype × treatment interaction effect selectively for properties related to passive membrane integration and spike initiation, including membrane time constant, peak input resistance, and recruitment current. In contrast, firing gain, spike waveform characteristics, and synaptic inputs were largely unaffected. These findings indicate that sustained inhibitory perturbation selectively triggered overactive intrinsic compensatory mechanisms in SOD1^G93A motoneurons rather than inducing widespread changes in firing or synaptic transmission. Together, our results provide direct evidence for over-active homeostatic control of motoneuron excitability and support a view of motoneuron dysfunction in ALS as a problem of altered feedback regulation rather than simply hyper- or hypo-excitability. Full article

This paper investigates some ${\mathcal{H}}_{\infty }$ control problems for linear continuous-time singular systems. The objective is to design controllers that guarantee the admissibility of the closed-loop system and simultaneously achieve a prescribed ${\mathcal{H}}_{\infty }$ disturbance attenuation level. To this end, a framework based on novel strict LMIs (Linear Matrix Inequalities) is developed using a Lyapunov function approach for the analysis of admissibility and ${\mathcal{H}}_{\infty }$ performance. In particular, an additional scalar parameter $\alpha$ is introduced to generalize the condition reported in previous results in the literature, providing greater flexibility. Then, sufficient LMI conditions are derived for the synthesis of both state-feedback and static output-feedback controllers. Finally, some numerical examples demonstrate the effectiveness of the proposed method. Full article

Soft robotics enables inherently safe, compliant interaction, yet integrating brain–computer interfaces (BCIs) remains hindered by a fundamental mismatch: BCIs typically output low-bandwidth, discrete commands, whereas soft robots possess high-dimensional, nonlinear dynamics. In this position paper, we argue that BCI–soft robot integration must move beyond direct decoder-to-actuator mapping. We propose a unified, application-oriented compatibility framework that structurally decouples hierarchical control and formally allocates authority between human neural input and local soft robotic autonomy. Crucially, we introduce verifiable, quantitative design principles that define integration as a matching problem across neural bandwidth, update frequency, latency tolerance, and control dimensionality. Through these testable hypotheses, we demonstrate that active, reactive, and passive BCIs serve distinct, complementary roles. We conclude that shared-control strategies—where the BCI provides high-level intent, target selection, or user-state feedback, while the soft robot manages low-level physical execution and interaction—offer the most practical pathway forward. We argue that future progress depends on the co-design of paradigm, decoding, control, and embodiment for neuro-adaptive and human-centred soft robotic systems. Full article

This paper extends the active disturbance rejection control (ADRC) approach to a class of nonlinear hybrid systems with uncertain fractional disturbances and unknown parameters, aiming to investigate the applicability of the ADRC approach in the presence of abrupt state changes and complex noise structures. By employing the fractional Wick–Itô–Skorohod (fWIS) integral, an extended state observer (ESO) together with an ESO-based control strategy is developed. It is shown that the resulting closed-loop hybrid stochastic system achieves mean-square stability under fractional noise. Furthermore, the proposed approach is generalized to enable the observation, tracking, and compensation of white noise disturbances with abrupt changes. Numerical simulations are presented to demonstrate the effectiveness of the proposed method. Full article

Aggressive technology scaling has led to a significant increase in manufacturing process variations and transistor aging effects, which critically degrade the performance of radio frequency (RF) circuits. These reliability challenges are particularly pronounced in voltage-controlled oscillators (VCOs), where phase noise and operating frequency stability are compromised. While design strategies incorporating micro-electromechanical systems (MEMS) actuators enhance VCO performance by leveraging MEMS varactors or inductors with substantially higher quality factors (Q), this benefit is progressively undermined over time by process variations and aging-induced shifts in the threshold voltage and carrier mobility of the VCO’s transistors. This work presents an ultra-low-power current-reuse voltage-controlled oscillator (VCO) designed to maintain stable performance under process variability and reliability-induced parameter shifts. Robust operation is achieved using a self-detecting–correcting (SDC) bias scheme that senses performance drift and applies corrective feedback through body-bias control in the VCO core. Analytical relations are derived to describe the impact of threshold voltage and mobility variations, and the approach is validated via post-layout simulations in a 130 nm complementary metal-oxide semiconductor (CMOS). Under 18% variations in threshold voltage and carrier mobility, the proposed SDC scheme preserves oscillation frequency, phase noise, and figure of merit (FoM) while also mitigating the intrinsic output amplitude imbalance of conventional current-reuse VCOs. Monte Carlo analysis (500 runs) demonstrates low sensitivity to fabrication uncertainty, with a standard deviation below 0.14 dBc/Hz for phase noise, 210 kHz for oscillation frequency, and 0.4 dBc/Hz for FoM. The VCO operates from a 0.9 V supply, consumes 175 μW, and achieves −124 dBc/Hz phase noise at 1 MHz offset near 2.4 GHz (FoM ≈ −199 dBc/Hz). Full article

Retrieval-augmented generation improves the factual consistency, knowledge timeliness, and scenario adaptability of large model inference services by incorporating external knowledge. However, it also introduces structural privacy risks, including private-knowledge leakage, prompt injection, and progressive information extraction in multi-turn interactions. To address these issues, this paper proposes Private-RAG, a privacy-preserving retrieval-augmented generation method for large model inference. The method constructs a composite threat model and a quantitative evaluation framework for the RAG pipeline, and further develops a layered collaborative defense mechanism consisting of controlled retrieval, sensitivity-aware context minimization, structured prompt isolation, and multi-criterion output gating. In addition, a risk feedback-driven budget accounting method is introduced to enable dynamic risk control in multi-turn interaction scenarios. Experimental results show that Private-RAG effectively reduces private-knowledge leakage, improves robustness against prompt injection, and suppresses cumulative privacy exposure while maintaining question-answering utility and a controllable deployment latency (e.g., 1165 ms), demonstrating superior privacy protection and inference robustness. Full article

This paper studies the problem of controller design for polynomial fuzzy-model-based (PFMB) systems. To make full use of the information of membership functions (MFs), the operating space is partitioned into several subspaces. According to the information of partitions, switched state feedback and output feedback controllers are designed, respectively, for the system. By employing the approximated membership function method and a new relaxation technique, relaxed stabilization criteria in the form of the sum of squares are derived without the need to impose any constraints on system matrices. Simulation examples are provided to illustrate the validity of the presented method. Full article

This paper introduces an electrocardiogram (ECG) noise removal front-end amplifier circuit based on a current-feedback operational amplifier (CFOA) that uses the current feedback to detect error signals and control the output. This ECG circuit focuses on denoising the ECG noise to accentuate the ECG electrical signals from the heart. Noises in ECG refer to baseline wander (BW), powerline interference (PLI) and motion artifacts. We proposed a CFOA-based ECG pre-amplifier using the AD844 commercial operational amplifier built inside with a positive second-generation current conveyor (CCII+) and a voltage follower circuit. This work introduces an ECG noise removal front-end amplifier based on a CFOA. The primary innovation lies in the balancing instrumentation amplifier architecture that utilizes the high-speed and robust properties of the AD844 commercial operational amplifier to achieve superior noise rejection. To protect against high-frequency interference, we introduce a novel cascaded low-pass filter (LPF) stage that ensures a sharper cut-off compared to traditional single-stage designs. Experimental results validate the design’s effectiveness, achieving a high common-mode rejection ratio (CMRR) of 75.4 dB and a mid-band gain of 46.5 dB. These performance metrics, combined with the circuit’s ability to eliminate BW and PLI, confirm its robust suitability for high-fidelity wearable ECG monitoring. Full article

All real control systems are subject to uncertainties and disturbances, so feedback controllers have to be designed such that closed-loop systems possess the desired dynamic and steady-state responses in the presence of any allowable uncertainties and disturbances. It is common practice to address the effects of uncertainties and disturbances via bounding techniques and the almost disturbance decoupling approach, respectively. Linear, affine, and power growth conditions on uncertainties are required for almost disturbance decoupling. However, when these conditions are not satisfied, almost disturbance decoupling is not possible. A new concept called weak disturbance decoupling is introduced to mitigate the effects of disturbances. A weak disturbance decoupling problem is to find a feedback controller so that the close-loop system has the weak disturbance decoupling performance, that is, the closed-loop system is stable and the norm of the output is not greater than the sum of a positive constant and the product of the norm of the disturbance and a positive constant. Full article

This paper presents a hybrid algorithmic framework for fault detection and isolation (FDI) in nonlinear quadrotor unmanned aerial vehicle (UAV) systems operating under closed-loop conditions. The proposed method integrates a Linear Quadratic Control (LQC) strategy, synthesized through Linear Matrix Inequalities (LMIs), with a Conditional Long Short-Term Memory Autoencoder (CLSTM-AE) and an adaptive residual-based decision mechanism. The LQC scheme provides robust trajectory tracking through regional pole-placement constraints, while the CLSTM-AE learns the nominal closed-loop input–output temporal behavior of the UAV using only fault-free data. In contrast to conventional symmetric autoencoder-based detectors, the proposed CLSTM-AE uses the control inputs together with the available attitude estimates, represented by the Euler angles yaw, pitch, and roll, as conditioning information, while reconstructing only the monitored attitude outputs. This asymmetric structure allows the residuals to capture inconsistencies between the commanded control effort and the observed attitude response, which is particularly relevant in closed-loop nonlinear systems where feedback compensation may attenuate fault signatures. Deviations from nominal behavior are detected through reconstruction residuals computed using a smoothed Mean Squared Error (MSE) criterion and evaluated against an adaptive $3\sigma$ threshold. The framework is validated in three-dimensional flight simulations considering abrupt, transient, and incipient actuator fault scenarios. The obtained results show that the proposed approach outperforms representative conventional machine-learning methods, achieving an average accuracy of 98.2%, an average recall of 97.8%, and an average false positive rate of 1.4%. These results suggest that the proposed hybrid algorithm provides an effective and interpretable solution for closed-loop fault diagnosis in nonlinear UAV systems under measurement noise and system variability. Full article

To address the rigid temporal constraints and high-precision trajectory tracking requirements in modern industrial automation (e.g., high-speed pick-and-place or collaborative assembly), this paper proposes a novel composite control strategy for robotic manipulators that integrates Actor–Critic reinforcement learning with predefined-time sliding mode control (PTC-RLC). Existing predefined-time control (PTC) schemes usually rely on excessively large switching gains when dealing with strong disturbances, which easily triggers severe chattering in the system’s actuators and degrades dynamic performance. To this end, a novel predefined-time sliding surface based on artificial delay feedback is designed, ensuring that the position tracking error can strictly converge within a user-explicitly set time $T_c$ regardless of the system’s initial states, thereby significantly enhancing temporal determinism. Meanwhile, a reinforcement learning agent based on the Actor–Critic architecture is constructed to approximate and dynamically compensate for the system’s lumped unknown dynamics and external disturbances online, minimizing the control law’s reliance on large robust gains. Based on Lyapunov stability theory, the semi-global uniform ultimate boundedness of the closed-loop system is strictly proved. Numerical simulation results demonstrate that under severe operating conditions with parameter mismatches and time-varying disturbances, the proposed control strategy not only achieves high-precision and singularity-free trajectory tracking within the predefined time, but also effectively suppresses high-frequency chattering phenomena compared to the traditional non-singular terminal sliding mode control (NTSMC), outputting a smoother control torque and demonstrating strong potential for practical engineering implementations. Full article

The electromechanical transmission (EMT) systems of hybrid special vehicles are highly susceptible to severe transient torsional resonance under frequent start-stop operating conditions. Traditional entire-frequency domain H_∞ active vibration reduction strategies are often limited by insufficient gain, failing to achieve ultimate suppression within the core resonance frequency band. To address this issue, this paper proposes a finite-frequency H_∞ active torsional vibration suppression strategy based on a motor dual-loop control architecture. This strategy achieves a profound physical decoupling between torsional vibration suppression and steady-state driving tasks. Furthermore, by introducing the Generalized Kalman–Yakubovich–Popov (GKYP) lemma and Linear Matrix Inequalities (LMIs) into the secondary loop, the control degrees of freedom are precisely concentrated on the 8–30 Hz frequency band, where the transient resonance energy is highly localized. This thoroughly eliminates the conservatism inherent in entire-frequency designs. To mitigate the instability risks caused by unmeasurable states and actuator response lags in practical engineering applications, a robust controller integrating input time-delay compensation and dynamic output feedback is subsequently constructed. Numerical case studies and hardware-in-the-loop (HIL) test results based on a specific EMT configuration demonstrate that the proposed strategy effectively overcomes the instability induced by system delays. It achieves an outstanding resonance peak attenuation of up to 93% and strictly constrains output shaft torque fluctuations within a safe threshold of 50 N·m. Ultimately, this study provides an efficient and robust closed-loop engineering solution for the transient vibration management of high-power electromechanical transmission systems and the enhancement of overall vehicle NVH performance. Full article