Search Results (31)

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

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

To address the issue that current ripple in traditional switching power amplifiers (SPA) for active magnetic bearing (AMB) systems is constrained by the switching frequency, this paper proposes a novel LCL filter-based switching power amplifier (LCL-SPA) along with its parameter design and high-performance control strategy. Without altering the original full-bridge topology or the switching frequency, the proposed scheme achieves superior ripple suppression. To tackle the inherent resonance problem of the LCL filter, a sensorless capacitor current feedback active damping (CCFAD) strategy is proposed. This approach effectively suppresses resonance without additional hardware sensors and ensures system stability under digital control delays. Furthermore, to overcome the limitations of traditional PI controllers in terms of the dynamic performance and parameter tuning of the LCL-SPA, a high-performance LESO-based control algorithm within the Linear Active Disturbance Rejection Control (LADRC) framework is designed. By utilizing a Linear Extended State Observer (LESO) to estimate and compensate for total lumped disturbances in real-time, the algorithm simplifies the parameter tuning process and achieves rapid current tracking with nearly zero overshoot. Experimental results demonstrate that the proposed LCL-SPA achieves extremely low current ripple across various reference currents, with the ripple minimized to 20 mA at a 3 A load. Frequency response tests confirm that the system possesses a closed-loop bandwidth of up to 2 kHz, satisfying the high dynamic requirements of magnetic bearings. Full article

Maximum Power Point Tracking (MPPT) in permanent-magnet synchronous generator (PMSG)-based wind energy conversion systems (WECS) remains challenging owing to strong nonlinearities, parametric uncertainties, and external disturbances. Conventional sliding mode control (SMC) strategies, while robust, suffer from chattering, dependence on full-state measurements, and degraded performance under model mismatch, limiting their practical deployment. To address these issues, this study proposes a neuroadaptive fuzzy fractional-order super-twisting sliding mode control (Fuzzy-FOSTSMC) integrated with a high-gain observer (HGO) and a radial basis function neural network (RBFNN). The HGO estimates unmeasurable higher-order states (e.g., angular acceleration), enabling output-feedback implementation. In contrast, the RBFNN online approximates unknown nonlinear system dynamics $L_f^{2}h\left(x\right)$ and $L_g{L}_{f}h\left(x\right)$, rendering the controller model-free. Chattering is eliminated by replacing the discontinuous signum function with an adaptive fuzzy boundary layer that dynamically modulates the slope near the sliding surface. Stability is theoretically confirmed by Lyapunov analysis. Extensive MATLAB/Simulink simulations verify that the proposed approach yields a tracking precision of 99.96%, a steady-state speed error of 0.018 rad/s, and a 58.2% reduction in the integral absolute error (IAE) compared to the traditional FOSTSMC. It achieves the optimal power coefficient ($C_p=0.4762$) via TSR control at $7.000\pm 0.002$, under ±30% parametric uncertainties, demonstrating excellent robustness and MPPT effectiveness. Full article

This paper addresses the tracking control problem for pure-feedback stochastic nonlinear systems subject to full state constraints, actuator faults, and backlash-like hysteresis. An adaptive finite-time control strategy is proposed, using radial basis function neural networks to approximate unknown system dynamics. By integrating barrier Lyapunov functions with a backstepping design, the method guarantees semi-global practical finite-time stability of all closed-loop signals. The strategy ensures that all states remain within prescribed limits while achieving accurate tracking of the reference signal in finite time. The effectiveness and superiority of the proposed approach are demonstrated through simulations, including a numerical example and a rigid robot manipulator system, with comparisons to existing methods highlighting its advantages. Full article

The increasing global energy demand is driving the deployment of renewable energy in the electrical power infrastructure, which emphasizes the critical importance of grid-connected inverters (GCIs). As the power injected into the utility grid increases, GCIs commonly operate in parallel. However, interactions between multiple GCIs and the presence of LC grid impedance pose significant challenges to the stable operation of GCIs. Existing control strategies to deal with multiple GCIs often neglect the capacitive component of grid impedance, which results in instability and deteriorated power quality in a complex grid condition. To overcome these problems, this study proposes a current control scheme and stability assessment of multiple GCIs. To effectively mitigate high-frequency resonance, the proposed method is achieved by an incomplete state feedback control which eliminates the feedback control terms for unmeasurable states. Furthermore, resonant and integral control terms are incorporated to reduce steady-state error as well as to improve harmonic compensation induced by the PCC voltages. A full-state observer is employed to reduce sensing requirements and simplify system complexity. Multiple-GCI behavior is comprehensively analyzed under complex grid environments. A comprehensive stability assessment is also conducted to evaluate the interactions of multiple GCI systems with LC grid impedance. The effectiveness of the designed controller in enhancing power quality and guaranteeing system stability is validated by theoretical analysis, PSIM simulations, and experimental tests on a DSP-controlled 2 kW prototype system. Full article

In robotic teleoperation, the simultaneous realization of rapid tracking, compliance, and safe interaction presents a fundamental control challenge. This challenge stems from a critical trade-off: high-stiffness controllers achieve rapid tracking but compromise safety during physical interactions, whereas low-stiffness impedance controllers ensure compliant and safe interactions at the expense of responsiveness. To address this conflict, this study proposes a bio-inspired teleoperation control method (BITC) that integrates human withdrawal reflex mechanisms and the nonlinear stiffness characteristics of shear-thickening fluids. BITC features a dynamic force-feedback-driven collision reflex strategy, enabling rapid detection and disengagement from unintended contacts. Additionally, a nonlinear compliance control module is proposed to achieve both force fidelity during initial contact and adaptive stiffness modulation during progressively deeper contact in an emergency. By integrating full-state feedback tracking, the BITC teleoperation control framework is implemented to unify the performance of rapid tracking, compliance, and safety. Three experiments are conducted to demonstrate that the BITC method achieves accurate tracking performance, ensures compliant behavior during deep contact while maintaining force fidelity during initial contact, and enables safe reflexion for collision, respectively. The method is also validated to reduce peak contact forces by approximately 60% and minimizes contact duration to less than 120 ms, presenting comprehensive teleoperation performance. Full article

The control stability and accuracy of quad tiltrotor UAVs is improved when encountering external disturbances during automatic flight by an active disturbance rejection control (ADRC) parameter self-tuning control strategy based on a radial basis function (RBF) neural network. Firstly, a nonlinear flight dynamics model of the quad tiltrotor UAV is established based on the approach of component-based mechanistic modeling. Secondly, the effects of internal uncertainties and external disturbances on the model are eliminated, whilst the online adaptive parameter tuning problem for the nonlinear active disturbance rejection controller is addressed. The superior nonlinear function approximation capability of the RBF neural network is then utilized by taking both the control inputs computed by the controller and the system outputs of the quad tiltrotor model as neural network inputs to implement adaptive parameter adjustments for the Extended State Observer (ESO) component responsible for disturbance estimation and the Nonlinear State Error Feedback (NLSEF) control law of the active disturbance rejection controller. Finally, an adaptive attitude control system for the quad tiltrotor UAV is constructed, centered on the ADRC-RBF controller. Subsequently, the efficacy of the attitude control system is validated through simulation, encompassing a range of flight conditions. The simulation results demonstrate that the Integral of Absolute Error (IAE) of the pitch angle response controlled by the ADRC-RBF controller is reduced to 37.4° in comparison to the ADRC controller in the absence of external disturbance in the full-states mode state of the quad tiltrotor UAV, and the oscillation amplitude of the pitch angle response controlled by the ADRC-RBF controller is generally reduced by approximately 50% in comparison to the ADRC controller in the presence of external disturbance. In comparison with the conventional ADRC controller, the proposed ADRC-RBF controller demonstrates superior performance with regard to anti-disturbance capability, adaptability, and tracking accuracy. Full article

This paper presents a non-fragile observer-based dissipative control strategy for the suspension systems of electric vehicles equipped with in-wheel motors, accounting for input delays, actuator faults, and observer gain uncertainty. Traditional control approaches—such as $H_{\infty }$, passive control, and robust feedback schemes, often address these challenges in isolation and with increased conservatism. In contrast, this work introduces a unified framework that integrates fault-tolerant control, delay compensation, and robust state estimation within a dissipativity-based setting. A novel dissipativity analysis tailored to Electric Vehicle Active Suspension Systems (EV-ASSs) is developed, with nonzero delay bounds explicitly incorporated into the stability conditions. The observer is designed to ensure accurate state estimation under gain perturbations, enabling robust full-state feedback control. Stability and performance criteria are formulated via Linear Matrix Inequalities (LMIs) using advanced integral inequalities to reduce conservatism. Numerical simulations validate the proposed method, demonstrating effective fault-tolerant performance, disturbance rejection, and precise state reconstruction, thereby extending beyond the capabilities of traditional control frameworks. Full article

Grid-connected inverter (GCI) plays a crucial role in facilitating stable and efficient power delivery, especially under severe and complex grid conditions. Harmonic distortions and imbalance of the grid voltages may degrade the grid-injected current quality. Moreover, inductive-capacitance (LC) grid impedance and the grid frequency fluctuation also degrade the current control performance or stability. In order to overcome such an issue, this study presents a frequency-adaptive current control strategy of a GCI based on incomplete state observation under severe grid conditions. When LC grid impedance exists, it introduces additional states in a GCI system model. However, since the state for the grid inductance current is unmeasurable, it yields a limitation in the state feedback control design. To overcome such a limitation, this study adopts a state feedback control approach based on incomplete state observation by designing the controller only with the available states. The proposed control strategy incorporates feedback controllers with ten states, an integral controller, and resonant controllers for the robustness of the inverter operation. To reduce the reliance on additional sensing devices, a discrete-time full-state current observer is utilized. Particularly, with the aim of avoiding the grid frequency dependency of the system model, as well as the complex online discretization process, observer design is developed in the stationary reference frame. Additionally, a moving average filter (MAF)-based phase-locked loop (PLL) is incorporated for accurate frequency detection against distortions of grid voltages. For evaluating the performance of the designed control strategy, simulations and experiments are executed with severe grid conditions, including grid frequency changes, unbalanced grid voltage, harmonic distortion, and LC grid impedance. Full article

Background: Interdialytic weight gain (IDWG), defined as the accumulation of salt and water intake between dialysis sessions, is a critical parameter of fluid management and a marker of adherence to dietary and fluid restrictions in hemodialysis patients. Excessive IDWG has been strongly associated with increased cardiovascular risk, including left ventricular hypertrophy, cardiac dysfunction, and cerebrovascular complications. Additionally, it necessitates more aggressive ultrafiltration, potentially compromising hemodynamic stability, impairing quality of life, and escalating healthcare costs. Despite international guidelines recommending an IDWG target of <4–4.5% of body weight, many patients struggle to achieve this due to barriers in adhering to dietary and fluid restrictions. This review explores the current state-of-the-art strategies to mitigate IDWG and evaluates emerging diagnostic and therapeutic perspectives to improve fluid management in dialysis patients. Methods: A literature search was conducted in PubMed/MEDLINE, Scopus, and Web of Science to identify studies on IDWG in hemodialysis. Keywords and MeSH terms were used to retrieve peer-reviewed articles, observational studies, RCTs, meta-analyses, and systematic reviews. Non-English articles, case reports, and conference abstracts were excluded. Study selection followed PRISMA guidelines, with independent screening of titles, abstracts, and full texts. Data extraction focused on IDWG definitions, risk factors, clinical outcomes, and management strategies. Due to study heterogeneity, a narrative synthesis was performed. Relevant data were synthesized thematically to evaluate both established strategies and emerging perspectives. Results: The current literature identifies three principal strategies for IDWG control: cognitive–behavioral interventions, dietary sodium restriction, and dialysis prescription adjustments. While educational programs and behavioral counseling improve adherence, their long-term effectiveness remains constrained by patient compliance and logistical challenges. Similarly, low-sodium diets, despite reducing thirst, face barriers to adherence and potential nutritional concerns. Adjustments in dialysate sodium concentration have yielded conflicting results, with concerns regarding hemodynamic instability and intradialytic hypotension. Given these limitations, alternative approaches are emerging. Thirst modulation strategies, including chewing gum to stimulate salivation and acupuncture for autonomic regulation, offer potential benefits in reducing excessive fluid intake. Additionally, technological innovations, such as mobile applications and telemonitoring, enhance self-management by providing real-time feedback on fluid intake. Biofeedback-driven dialysis systems enable dynamic ultrafiltration adjustments, improving fluid removal efficiency while minimizing hemodynamic instability. Artificial intelligence (AI) is advancing predictive analytics by integrating wearable bioimpedance sensors and dialysis data to anticipate fluid overload and refine individualized dialysis prescriptions, driving precision-based volume management. Finally, optimizing dialysis frequency and duration has shown promise in achieving better fluid balance and cardiovascular stability, suggesting that a personalized, multimodal approach is essential for effective IDWG management. Conclusions: Despite decades of research, IDWG remains a persistent challenge in hemodialysis, requiring a multifaceted, patient-centered approach. While traditional interventions provide partial solutions, integrating thirst modulation strategies, real-time monitoring, biofeedback dialysis adjustments, and AI-driven predictive tools represent the next frontier in fluid management. Future research should focus on long-term feasibility, patient adherence, and clinical efficacy, ensuring these innovations translate into tangible improvements in quality of life and cardiovascular health for dialysis patients. Full article

The current shafting alignment process primarily relies on manual adjustments, resulting in low efficiency and limited precision. This study designs an automatic alignment system for multi-support shafting to improve alignment efficiency and accuracy, advancing the development of intelligent systems. The overall design of the automatic alignment system is proposed, including the establishment of shafting and actuator models. The alignment performance of two control strategies under different shafting deviations is validated through simulations. The results indicate that both the full-state feedback control strategy and the model predictive control strategy can regulate bearing load errors to within 1%. Compared to manual alignment, both strategies demonstrate significantly higher efficiency. The proposed automatic alignment system is feasible and lays a foundation for the development of high-precision shafting alignment systems. Full article

Robotic needle steering has become a topic of interest in intervention surgery. Yet, this surgical procedure poses challenges due to external disturbances and tissue movement. To address these challenges, several novel steering algorithms have been developed to guide the needle precisely from the entry point to the target point. However, some of these algorithms may cause additional trauma to patients. In this paper, we present a 3D optimal control algorithm for a curvature-controllable steerable (CCS) needle, aiming to achieve effective operations with minimal trauma. We derive a kinematics without duty cycle control strategy (needle shaft spin), propose a novel intraoperative motion planner for path replanning, and design a full-state feedback controller for accurate path tracking. A dynamic environment was simulated, and the optimal controller showed a better result (0.01 ± 0.01 mm) than the case (3.86 ± 1.32 mm) using a full-state feedback controller. The demonstration indicates that the optimal control system can safely, effectively, and accurately steer the needle to the target point in a dynamic environment. Full article

Hybrid physics-based data-driven models, namely, augmented physics-based models (APBMs), are capable of learning complex state dynamics while maintaining some level of model interpretability that can be controlled through appropriate regularizations of the data-driven component. In this article, we extend the APBM formulation for high-order Markov models, where the state space is further augmented with past states (AG-APBM). Typically, state augmentation is a powerful method for state estimation for a high-order Markov model, but it requires the exact knowledge of the system dynamics. The proposed approach, however, does not require full knowledge of dynamics, especially the Markovity order. To mitigate the extra computational burden of such augmentation we propose an approximated-state APBM (AP-APBM) implementation leveraging summaries from past time steps. We demonstrate the performance of AG- and AP-APBMs in an autoregressive model and a target-tracking scenario based on the trajectory of a controlled aircraft with delay-feedback control. The experiments showed that both proposed strategies outperformed the standard APBM approach in terms of estimation error and that the AP-APBM only degraded slightly when compared to AG-APBM. For example, the autoregressive (AR) model simulation in our settings showed that AG-APBM and AP-APBM reduced the estimate error by $31.1$% and $26.7$%. The time cost and memory usage were reduced by $37.5$% and 20% by AP-APBM compared to AG-APBM. Full article

Wind turbines experience significant unbalanced loads during operation, exacerbated by external disturbances that challenge the stability of the pitch control system and affect output power. This paper proposes an independent pitch adaptive control strategy integrating state feedback and disturbance accommodating control (DAC). Initially, nonlinear wind turbine dynamics are globally linearized, and DAC is applied to mitigate the impact of wind disturbances dynamically. Subsequently, the entire range of wind speeds is segmented, and controllers are individually designed to optimize gain settings according to specific control objectives at each wind speed interval. Scheduling parameters such as collective pitch angle and tower fore-aft displacement are identified and trained using Radial Basis Function Neural Networks (RBFNN). Finally, based on the output gain values determined by RBFNN, the full-state feedback controller group is adaptively adjusted, and the optimal controller is selected for the final output. Simulations conducted on the NREL 5MW reference wind turbine model using FAST and Simulink demonstrate that compared to the ROSCO controller, the proposed strategy ensures smoother output power and effectively reduces blade and tower loads, thereby extending the turbine’s operational lifespan. Full article