Search Results (381)

This study presents a comparative evaluation of path-tracking controllers designed from reduced-order state-space vehicle models. A four-state state-space model is formulated from the bicycle-model dynamics and target-path geometry, where the state variables are the previewed lateral error, heading error, side-slip angle, and yaw rate. To reduce the dependence on variables that are difficult to obtain in practice, a three-state model is derived by eliminating the explicit side-slip dynamics, and a two-state model is further obtained by replacing the yaw-rate dynamics with a kinematic approximation. Based on these three models, linear-quadratic regulator (LQR) controllers are designed. In addition, two linear quadratic static output-feedback (LQ SOF) controllers are constructed from the original four-state model by using reduced output sets. The five controllers are evaluated by vehicle simulations carried out in CarSim under front-wheel-steering and four-wheel-steering configurations. The results clarify the influence of controller structure and model order on path-tracking performance and identify the controller–actuator combination that provides the most favorable performance under the conditions considered. Full article

To address the issues of slow dynamic response and poor disturbance rejection in three-phase interleaved parallel buck converters under disturbance conditions such as voltage or load transients, an improved sliding mode auto-disturbance rejection control (SM-ADRC) strategy is proposed. Firstly, the traditional ADRC algorithm suffers from reduced disturbance observation accuracy in the extended state observer (ESO) due to discontinuous switching of the nonlinear function at segment boundaries. To address this, a novel nonlinear function is designed using an interpolation fitting method. Concurrently, an improved ESO is constructed based on deviation-control principles, utilising the deviation between each state variable and its observed value. Secondly, an enhanced state error feedback law combines an improved exponential approach law with an integral sliding mode surface, thereby enhancing the control system’s robustness. Finally, simulation comparisons of output voltage fluctuations and power response speeds under various operating conditions validate the superiority and feasibility of the proposed SM-ADRC strategy over both the conventional ADRC strategy and PI control strategy. Full article

This paper studies prescribed-time (PT) stabilization for a heat equation with an unstable term at the uncontrolled boundary, subject to external disturbances and internal unknown mode uncertainties at the controlled boundary. A boundary time-varying output feedback control scheme based on disturbance estimation is developed, where the convergence time is independent of the initial condition and can be specified a priori. A disturbance estimator using boundary measurements and a time-varying tuning function enables prescribed-time estimation of both the disturbance and the system state. By integrating active disturbance rejection control with the backstepping technique, a boundary output feedback controller is derived. A simulation example from the burning process of a solid propellant rocket demonstrates the effectiveness of the proposed approach. Full article

This study proposes a 300 W class Gallium Nitride (GaN) Solid-State Power Amplifier (SSPA)-based microwave plasma generator system for implementing next-generation light sources with high brightness and color rendering at 2.45 GHz. To overcome the lifetime limitations and control constraints of conventional magnetron systems, the proposed system introduces custom packaging technology utilizing GaN-on-SiC Bare-dies fabricated via the Win-semiconductor’s NP25 process. This approach minimizes parasitic components and significantly reduces thermal resistance compared to standard packages, ensuring reliability during high-power operation. A stable RF output of 300 W was achieved through two-stage power combining. For the plasma source, an Ar-InBr-Hg gas mixture was employed to optimize optical characteristics. This gas mixture is commonly used in electrodeless plasma lamps due to its high luminous efficacy and stable discharge characteristics. To analyze the rapid impedance discontinuity during gas ignition, numerical analysis based on the Drude model was performed, theoretically identifying the complex permittivity transition of the medium and the resulting resonant frequency up-shift mechanism. To mitigate system instability during this transition, an adaptive frequency tracking and feedback control loop based on real-time VSWR monitoring was implemented. Experimental results demonstrate precise tracking within a 100 MHz frequency variable range, achieving a system efficiency of over 53% and maintaining a VSWR below 1.15:1. These results validate the practical feasibility of GaN SSPA technology in electrodeless lighting and industrial plasma applications utilizing high-power RF energy. Full article

Major depressive disorder (MDD) lacks reliable laboratory tests for diagnosis and treatment monitoring, underscoring the need for robust molecular readouts in blood. Beyond symptom-based classification, MDD can also be viewed as a condition involving impaired homeostatic regulation across stress-responsive, immune, metabolic, and neural systems. Consistent with this perspective, altered intron retention (IR) patterns have been observed in peripheral blood in depression-related and treatment-response contexts, supporting the translational relevance of this RNA-processing layer to mood disorders. A key observation underpinning this review is that IR can function as a reversible, intervention-responsive readout of physiological state. In a pre-symptomatic stress-like state in klotho mutant mice (a premature-aging model), widespread IR increases revert toward a healthy pattern upon treatment, suggesting that IR is embedded in a controllable homeostatic layer. Against the backdrop of limited cross-cohort transferability of differential gene expression (DGE) signatures, we propose that IR provides a mechanistically grounded biomarker layer because it reports regulated RNA processing states rather than context-fragile abundance endpoints. We operationalize IR as a post-transcriptional “throttle” on effective gene output, with increased IR/detained intron (DI) states acting as a reversible brake and decreased IR acting as an accelerator that increases translation-competent mRNA supply. Mechanistic exemplars across immune, metabolic, and neuronal systems (e.g., IFNG, OGT, MAT2A, neuronal activity-triggered intron excision, and intron detention-mediated stemness/differentiation switching in adult neural stem cells) show that defined inputs can switch IR/DI states to tune output kinetics. Integrating these findings, we propose an “Intron Retention Homeostat” (IR-Homeostat) model in which cells sense deviations from physiological set points and implement feedback control of gene output through switchable IR/DI regulation. This framework positions IR not only as a robust state readout for stratification, treatment response prediction, and pharmacodynamic profiling, but also as a tractable entry point to identify the molecular sensors and mediators that couple homeostatic signals to RNA processing control. Full article

Multi-agent perception systems must operate under fundamental asymmetries: some agents provide fast but unreliable observations, while others deliver higher-quality evidence with delay and uncertain correspondence. Traditional deterministic orchestration and rule-based fusion struggle to manage these trade-offs, often producing brittle or unstable behavior. We introduce a probabilistic orchestration framework that treats coordination as an epistemic generation problem—constructing and updating belief states under uncertainty—rather than a selection problem. Instead of committing to a single agent’s output, the orchestrator constructs a belief state that explicitly represents uncertainty, evidential provenance, and temporal relevance. Decisions are produced through latency-aware, association-weighted fusion, and uncertainty itself becomes a first-class signal governing action, deferral, and learning. Crucially, the orchestrator enables controlled teacher–student adaptation: high-confidence, well-associated stationary observations are gated into a feedback loop that improves ego perception over time while mitigating error amplification. We demonstrate the approach on an infrastructure-assisted dual-camera obstacle-recognition task. Experimental results show improved robustness to distance, occlusion, and delayed evidence compared to ego-only and deterministic orchestration baselines. By operationalizing orchestration as epistemic generation, this work provides a unifying framework for robust decision-making and safe adaptation in multi-agent systems, with implications that extend beyond perception to agentic and generative AI architectures. Full article

This study introduces an intelligent output feedback hybrid $H_2/H_{\infty }$ robust controller for a pumped storage unit regulation system (PSURS), utilizing an enhanced salp swarm algorithm (ISSA). A linearized PSURS model is developed through transfer function analysis. Utilizing this model, a robust controller design is executed using linear matrix inequalities (LMIs) to craft an output feedback hybrid $H_2/H_{\infty }$ controller that aims for both optimal and robust performance. The $H_2/H_{\infty }$ controller designed in this paper boasts a straightforward structure that eliminates the need for multiple-state feedback, simplifying its integration into practical PSURS applications. In addition, the ISSA plays a critical role in the design phase by optimally tuning the weight parameters of the controller to ensure its effectiveness. Simulation tests have demonstrated that this newly developed intelligent output feedback hybrid $H_2/H_{\infty }$ robust controller markedly enhances the stability of the PSURS. It shows superior control quality and robustness compared to traditional controllers. Furthermore, when applied to a multi-machine power system within PSURS simulations, this controller effectively improves system damping and helps mitigate frequency fluctuations. Full article

Active vibration control is crucial for mitigating harmful resonant vibrations in structures subjected to harmonic loads. While antiresonant (zero-placement) methods are effective for this purpose, existing state-feedback solutions require full state measurement, and output-feedback approaches often prioritize resonance assignment over direct harmonic cancellation. This work bridges this gap by proposing a novel systematic design for a proportional–derivative (PD) output-feedback controller to achieve antiresonance for second-order linear systems with a time delay in the actuators. The method first computes a homogeneous gain solution. It then leverages the parametrization of all antiresonant solutions as a constraint within a genetic algorithm optimization. The algorithm optimizes both the stability margin, characterized by an $M_s$-disk criterion, and the number of encirclements of the critical point $(-1,0)$ in the complex plane, as assessed by the Generalized Nyquist Stability Criterion. The proposed approach provides a practical, optimized output-feedback strategy for precise rejection of harmonic disturbances, as demonstrated through a collection of numerical examples from real-world applications. The results confirm the method’s effectiveness in synthesizing stabilizing controllers that enforce antiresonance while ensuring robust stability margins. Full article

Chronic crop diseases caused by uncultured, obligate, or host-dependent pathogens challenge traditional pathogen-centric paradigms that often interpret symptoms as direct outcomes of pathogen toxins, effectors, or tissue colonization. Here, we advance a host-centric immune framework that reframes disease as an emergent consequence of dysregulated host immune network activity, including prolonged activation, signaling miscoordination, and systemic physiological disruption. Using citrus huanglongbing (HLB) as a primary exemplar and canola clubroot as a parallel system, we synthesize evidence that persistent immune stimulation can drive self-damaging outputs, including sustained reactive oxygen species accumulation, chronic vascular and transport dysfunction, hormone imbalance, and growth–defense trade-offs. While many observations derive from transcriptomic, physiological, and genetic studies conducted under controlled experimental conditions, the available evidence collectively suggests that persistent immune activation may contribute substantially to disease-associated decline in these systems. We argue that pattern-triggered immunity (PTI) and effector-triggered immunity (ETI) operate as an integrated immune network whose feedback structure can become destabilized under chronic infection, generating immune states that are simultaneously harmful and often ineffective at pathogen clearance. We further discuss how panomic profiling, spatially resolved analyses, and network inference can diagnose host immune states at tissue and cell-type resolution, and how genome editing enables causal tests and rational immune tuning strategies that optimize defense amplitude, timing, and localization rather than indiscriminately amplifying resistance. By centering the host immune system as both a source of protection and pathology, this framework provides a conceptual and practical roadmap for understanding and engineering resilience in HLB, clubroot, and other chronic crop diseases in which pathogen biology remains experimentally opaque. Full article

Fixed-wing unmanned aerial vehicles, with their advantages of long endurance and substantial payload capacity, are poised to be a key platform for the future low-altitude economy. However, the challenge of achieving precise attitude tracking control under unknown time-varying disturbances persists. To tackle this difficulty, this article introduces a soft-sign function-based active disturbance rejection control (SSADRC) method, and develops a hybrid grey wolf optimizer (HGWO) with balanced exploration–exploitation mechanisms for intelligent parameter tuning. Specifically, SSADRC utilizes a novel smooth nonlinear function with saturation constraints to reconstruct the nonlinear feedback controller and the extended state observer, ensuring smooth and stable control output. Subsequently, HGWO integrates the good point set-based initialization strategy, the fitness-based dynamic-weight strategy, the diversity-based adaptive-mutation strategy, and the logistic chaotic map-based survival-of-the-fittest strategy, addressing the tuning of multiple coupled parameters in SSADRC. Additionally, the SSADRC-based pitch attitude controller is designed for a fixed-wing unmanned aerial vehicle, and an HGWO and seven other swarm optimization algorithms are employed to tune the parameters. The results demonstrate that the HGWO exhibits the best convergence accuracy in the SSADRC parameter optimization task, and SSADRC illustrates better command tracking performance and state estimation accuracy than typical ADRC. Full article

Wheeled bipedal robots are promising for industrial mobility because they combine tight turning, agile balancing, and efficient rolling. Their inherently unstable and underactuated dynamics make reliable reference tracking challenging, particularly in the presence of sustained external disturbances and modeling errors. This paper presents a systematic modeling and control study using a three-degrees-of-freedom sagittal plane representation derived from the original six-degrees-of-freedom dynamics. Two linear tracking controllers are designed and compared: a full state feedback tracking controller and a linear quadratic servo controller with integral action. Practical performance is validated through real-time hardware in the loop simulation, where the controller runs on embedded hardware and the plant is executed on a real-time target including discrete time-sampling effects and analog input output communication noise associated with signal transmission. The results show that both controllers achieve stabilization, while the comparative HILS results reveal a trade-off rather than a uniformly superior controller. The full state feedback controller often yields lower finite-horizon position tracking errors, whereas the linear quadratic servo controller provides tighter body-pitch regulation and the more reliable removal of steady-state offset under sustained constant disturbances. These results demonstrate the feasibility of optimal servo control on cost-effective embedded platforms and indicate that controller selection should depend on the desired balance, considering tracking accuracy, disturbance rejection, convergence behavior, and actuator usage. Full article

This study presents a command-filtered fuzzy adaptive control method for nonlinear thyristor controlled series compensation (TCSC) systems subject to actuator faults, unknown nonlinearities, and unmeasurable states. To enhance applicability, the TCSC-based single-machine infinite-bus (SMIB) system is first transformed into a nonlinear form preserving the inherent nonlinear characteristics of the power system. A state observer is then designed to estimate the unmeasurable states. Using these estimated states, a fuzzy control algorithm approximates the uncertain nonlinearities. By integrating command filtering techniques, an adaptive output feedback controller is developed, which ensures system stability and avoids the “explosion of complexity” issue. Simulation results verify the effectiveness of the proposed control approach. Full article

Low altitude logistics and aerial transport increasingly rely on multirotor unmanned aerial vehicles (UAVs) carrying slung payloads, where cable flexibility and load swing can degrade safety and delivery accuracy. This paper studies payload trajectory tracking for a dual-quadrotor cooperative slung-load system, targeting accurate tracking with swing suppression under thrust, attitude, and cable-tension limits. First, a payload-layer dynamic model is derived from d’Alembert’s principle with geometric cable constraints, and explicit tension reconstruction formulas are provided to enable direct enforcement of tension bounds. Building on this model, a payload-layer DEA nominal tracking controller is designed by applying dynamic extension to the tension-scalar channels and enforcing output-level linear error dynamics. To ensure real-time feasibility, a convex quadratic-programming (QP) projection layer minimally corrects the nominal command to satisfy thrust saturation, attitude-cone constraints, and cable-tension bounds. Moreover, an adaptive tuning control layer updates the DEA feedback gain and the projection weighting matrix within preset constraint limits based on energy residual and constraint-activation information, improving robustness and reducing manual tuning. Input-to-state stability is established under bounded disturbances and constraint-activation switching via a composite Lyapunov analysis. ROS–PX4–Gazebo simulations show low tracking error, suppressed swing, and sustained tension-limit compliance, validating the fusion controller. Full article

PID (proportional integral derivative) control has been widely used in industry due to its simplicity in implementation and satisfactory performance. However, the controller tuning is very troublesome when used in complex and nonlinear systems. The full bridge boost converter (FBBC) is a nonlinear system, so the PID control application in this converter should be further explored. This paper introduces a control approach that integrates PID control with a Linear Quadratic Regulator (LQR) for FBBC. To enable linear control design, the FBBC is linearized around its steady state operating points. The control architecture is structured into four cases: Case 1: PI-LQR Output Feedback, Case 2: PI-LQR State Feedback, Case 3: PID-LQR Output Feedback, and Case 4: PID-LQR State Feedback. The analysis aims to identify the most reliable system performance under input voltage change and load variation. The simulation results indicate that under the input voltage and load changes, cases 2 and 4 produce faster settling times, each with a settling time of 0.025 s and 0.015 s, respectively. However, both controllers produce negligible steady state error (less than 1%). Overall, Case 4 (PID-LQR State Feedback) consistently delivers the best performance, characterized by faster settling time, negligible steady state error, optimal control signal, and significantly reduced oscillation in both the inductor current and output voltage. Full article

The accelerating adoption of electric vehicles (EVs) has positioned them among the fastest-growing sectors in the electricity market. Since reliability, energy efficiency, and robustness are the fundamental criteria in motor drive selection, the permanent magnet synchronous motor (PMSM) has emerged as a preferred choice for EV applications. Nevertheless, achieving high-performance control of PMSM systems remains challenging due to nonlinear dynamics, parameter uncertainties, and external disturbances. To address these issues, this paper proposes a predefined-time output-feedback tracking control strategy for PMSMs subject to full-state error constraints, unknown nonlinear dynamics, external disturbances, and unmeasured states. Multi-dimensional Taylor Networks (MTNs) are employed to approximate unknown nonlinearities, while MTN-based observers are designed to estimate unmeasured states. The proposed controller integrates predefined-time stability theory, a general potential Lyapunov function, dynamic surface control (DSC), and backstepping to guarantee constraint satisfaction and rapid convergence. A hyperbolic tangent function is incorporated to eliminate singularities, and a predefined-time filter is introduced to mitigate the computational complexity of recursive backstepping. Theoretical analysis based on Lyapunov methods proves that all closed-loop signals remain bounded and that the tracking error converges to zero within a prespecified time. Simulation results confirm the effectiveness, robustness, and practical feasibility of the proposed approach for PMSM-driven EV applications. Full article