Search Results (1,263)

This research develops and implements a novel reinforcement learning (RL) architecture to address the trajectory-tracking problem in bipedal robotic systems under articulated-joint constraints. The proposed RL framework extends previously designed adaptive controllers characterized by state-dependent gain structures. The learning mechanism comprises two hierarchical adaptation layers: the first employs an adaptive dynamic programming (ADP) formulation to approximate the Bellman value function using a class of continuous-time dynamic neural networks. In contrast, the second uses an iterative optimization scheme based on the deep deterministic policy gradient (DDPG) algorithm. The resulting control strategy minimizes a robust performance index defined over the tracking trajectories of a system with uncertain and nonlinear dynamics representative of bipedal locomotion. The dynamic programming formulation ensures robustness to bounded parametric uncertainties and external perturbations. By approximating the Hamilton–Jacobi–Bellman (HJB) value function using neural network structures, a closed-loop controller design is systematically established. Numerical simulations demonstrate the convergence of the tracking error to a region centered at the origin with a size that depends on the approximation quality of the selected neural network. To assess the effectiveness of the proposed approach, a conventional state-feedback control design is adopted as a benchmark, revealing that the suggested method produces a lower cumulative tracking error norm (0.023 vs. 0.037 rad·s) in the trajectory-tracking control problem for all robotic joints while simultaneously reducing the control effort required to complete motion tasks. Full article

We propose a compact, beaconless inter-satellite laser communication tracking system based on direct fiber control to address the complexity and resource demands of conventional pointing, acquisition, and tracking (PAT) architectures. Unlike traditional sensor-based or beacon-assisted schemes, the proposed method employs a piezoelectric ceramic tube (PCT) to generate high-frequency, small-amplitude nutation of the single-mode fiber (SMF) tip, enabling real-time alignment correction using only the coupled optical power of the communication signal. This fully closed-loop tracking approach operates without position sensors and eliminates the need for beam splitting, external beacon sources, or auxiliary position detectors. A theoretical model is developed to analyze the influence of algorithm parameters and optical spot jitter on dynamic tracking performance. Experimental results show that the closed-loop system reliably converges to the optical spot center, achieving a fine-tracking accuracy of 4.6 $\mathsf{\mu }$rad and a disturbance suppression bandwidth of 200 Hz. By significantly simplifying the terminal architecture, the proposed approach provides an efficient and SWaP-optimized solution for inter-satellite and satellite-to-ground optical communication links. Full article

This paper presents a novel multi-loop control strategy for Vienna rectifiers that eliminates coordinate transformations while achieving superior performance under adverse grid conditions. Unlike conventional $dq$-frame controllers that suffer from computational complexity and degraded performance during unbalanced conditions, the proposed $abc$-frame scheme achieves a power factor of $98\%$ with total harmonic distortion (THD) below $5\%$ across all operating conditions. The system exhibits a settling time under 120 μs for $90\%$ load transients and ensures robust operation during Type A voltage sags while maintaining a $94\%$ power factor. Furthermore, it guarantees zero steady-state neutral point deviation. The controller employs a dual-loop architecture with high-gain current tracking and PI-based voltage regulation, validated through extensive PSIM/C++ co-simulations at 120 kw. Comparative analysis demonstrates a $35\%$ reduction in computational burden relative to $dq$-frame alternatives, while fully complying with IEEE-519:2022 standards. These results highlight the proposed method as a practical and robust solution for industrial rectification applications requiring grid-fault tolerance. Full article

This paper presents a comprehensive and hybrid control framework for the real-time regulation of drillstring systems that are subject to complex nonlinear dynamics, including torsional stick–slip oscillations, coupled axial vibrations, and intricate bit–rock interactions. The model also accounts for parametric uncertainties and external disturbances typically encountered during rotary drilling operations. A robust sliding mode controller (SMC) is designed for inner-loop regulation to ensure accurate state tracking and strong disturbance rejection. This is complemented by an outer-loop model predictive control (MPC) scheme, which optimizes control trajectories over a finite horizon while balancing performance objectives such as rate of penetration (ROP) and torque smoothness, and respecting actuator and operational constraints. To address the challenges of partial observability and noise-corrupted measurements, an Ensemble Kalman Filter (EnKF) is incorporated to provide real-time estimation of both internal states and external disturbances. Simulation studies conducted under realistic operating scenarios show that the hybrid MPC–SMC framework substantially enhances drilling performance. The controller effectively suppresses stick–slip oscillations, provides smoother and more stable bit-speed behavior, and improves the consistency of ROP compared with both open-loop operation and SMC alone. The integrated architecture maintains robust performance despite uncertainties in model parameters and downhole disturbances, demonstrating strong potential for deployment in intelligent and automated drilling systems operating under dynamic and uncertain conditions. Full article

This paper proposes a safety-constrained interaction control scheme for robotic manipulators by integrating model predictive control (MPC) and active disturbance rejection control (ADRC). The proposed method is specifically designed for manipulators with complex nonlinear dynamics. To ensure that the control system satisfies safety constraints during human–robot interaction, MPC is incorporated into the impedance control framework to construct a model predictive impedance controller (MPIC). By exploiting the prediction and constraint-handling capabilities of MPC, the controller provides guaranteed safety throughout the interaction process. Meanwhile, ADRC is employed to track the target joint control signals generated by the MPIC, where an extended state observer is utilized to compensate for dynamic modeling errors and nonlinear disturbances within the system, thereby achieving accurate trajectory tracking. The proposed method is validated through both simulation and real-world experiments, achieving high-performance interaction control with safety constraints at a 2 ms control cycle. The controller exhibits active compliant interaction behavior when the interaction stays within the constraint boundaries, while maintaining strict adherence to the safety constraints when the interaction tends to violate them. Full article

Reliable in situ quantification of interlayer mechanics in CRTS-II ballastless track slabs remains limited by the poor instrumentability of the CA mortar layer. This study implements a quasi-distributed fiber-optic sensing scheme by encapsulating FBGs in PVC conduits and embedding them within the CA mortar to track strain evolution under vertical loading. Four 1:3 scaled slabs were tested using stepwise load control (200 kN per step) to failure, and fiber measurements were cross-validated against conventional strain gauges on the reinforcement. The two systems showed consistent load–strain trends, while the fiber approach exhibited near-zero baseline offset and higher temporal resolution, enabling detection of small-amplitude strain changes that the gauges missed. The CA mortar displayed a clear tension-to-compression transition with increasing load; with two vertical rebars the ultimate load of the mortar layer reached 1400 kN, representing a 75% improvement over the rebar-free configuration and delaying compressive crushing through enhanced interlayer cooperation. Increasing the rebar diameter further restrained deformation and elevated the load level at which the transition occurred. The results demonstrate a practical interlayer monitoring route for CA mortar and quantify the strengthening role of vertical rebars, offering actionable guidance for design optimization and long-term condition assessment of CRTS-II slab tracks. Full article

The safe and efficient operation of high-speed trains is highly dependent on the reliability of their actuation systems, where actuator faults and input saturation pose significant challenges to control performance. Existing centralized control strategies often lack the flexibility to handle asymmetric actuator degradation and saturation across different carriages. To overcome this limitation, this paper leverages the inherent distributed structure of a train consist and proposes an distributed adaptive fault-tolerant control (DAFTC) scheme based on a multi-body dynamics model. The controller is designed at the carriage level to explicitly handle unknown actuator faults, input saturation, and parametric uncertainties. It incorporates an adaptive law for online parameter estimation and a second-order auxiliary system—a dynamic compensator—to mitigate saturation effects. Simulation results demonstrate the controller’s effectiveness in achieving accurate dual-loop tracking of both speed and position. Quantitative comparisons show that the proposed method reduces the average speed and position-tracking errors to 0.021 km/h and 0.426 m, respectively, outperforming conventional centralized approaches. Full article

This work focuses on the development of a diagnostic approach for detecting and localizing sensor faults in an autonomous photovoltaic system. The considered system is composed of a photovoltaic module and a resistive load. However, an adaptation stage formed by a DC/DC voltage boost converter is necessary to transfer energy from the source to the load. The diagnostic scheme is based on a sliding mode observer (SMO) that is robust to uncertainties and parametric variations. The SMO incorporates adaptive gains optimized via parametric adaptation laws, with stability rigorously verified through Lyapunov analysis. The method effectively identifies both independent and simultaneous sensor faults, employing an optimized threshold selection strategy to balance detection sensitivity and false alarm resistance. Simulation results under varying environmental conditions, system parameter fluctuations, and noisy measurement demonstrate the approach’s superior performance, achieving a 20% reduction in mean absolute percentage error (MAPE) and 90% faster settling time compared to existing techniques. These enhancements immediately increase the dependability, efficiency, and lifetime of the PV system, which are critical for lowering carbon emissions and ensuring the economic feasibility of solar energy investments. Key innovations include a novel residual generation mechanism, seamless integration with backstepping sliding mode maximum power point tracking (MPPT) control, and enhanced transient response characteristics. Full article

This paper focuses on the issues of tracking controller enhancement, input delay rectification, and disturbance elimination for dynamical systems characterized as fractional-order semi-Markovian jump processes. In particular, the design of the modified repetitive control technique integrated with the filtered Smith predictor scheme based on the Majhi–Atherton approach guarantees exact tracking performance and disturbance elimination. To be more specific, the active rectification of both external disturbances and delays is safeguarded by the construction of a modified proportional derivative-based active disturbance estimator along with the traditional Smith predictor framework. Also, the modified repetitive control in this framework is able to track the reference signals with multiple periodicities. In accordance with the Lyapunov stability criterion, a group of applicable principles is produced in the structure of matrix inequality constraints. Furthermore, the parameters of the controller block are designed concurrently by way of elucidating the stated matrix inequality constraints. Finally, the simulation results and the comparison analysis between the developed control technique and existing works such as equivalent input disturbance and the truncated predictor feedback control method validate the advantage of the recommended control framework. Full article

Active-control coils on Keda Torus eXperiment (KTX) are used to suppress error fields and mitigate MHD instabilities, thereby extending discharge duration and improving plasma confinement quality. Achieving effective active MHD control imposes stringent requirements on the coil power supplies: wide-bandwidth and high-precision current regulation, deterministic low-latency response, and tightly synchronized operation across 136 independently driven coils. Specifically, the supplies must deliver up to ±200 A with fast slew rates and bandwidths up to several kilohertz, while ensuring sub-100 μs control latency, programmable waveforms, and inter-channel synchronization for real-time feedback. These demands make the power supply architecture a key enabling technology and motivate this work. This paper presents the design and simulation of the KTX active-control coil power supply. The system adopts a modular AC–DC–AC topology with energy storage: grid-fed rectifiers charge DC-link capacitor banks, each H-bridge IGBT converter (20 kHz) independently drives one coil, and an EMC filter shapes the output current. Matlab/Simulink R2025b simulations under DC, sinusoidal, and arbitrary current references demonstrate rapid tracking up to the target bandwidth with ±0.5 A ripple at 200 A and limited DC-link voltage droop (≤10%) from an 800 V, 50 mF storage bank. The results verify the feasibility of the proposed scheme and provide a solid basis for real-time multi-coil active MHD control on KTX while reducing instantaneous grid loading through energy storage. Full article

This paper addresses the consensus control challenge for multiple autonomous underwater vehicles’ (AUVs) formation operating under external disturbances and communication delays. A fixed-time disturbance observer (FxTDO) is developed to precisely estimate external disturbances within a fixed time. A fixed-time state observer (FxTSO) is designed to reconstruct the leader’s position and velocity states, effectively compensating for communication delays. Building upon these observer estimates, an event-triggered sliding mode controller is proposed to achieve formation consensus with guaranteed convergence time while significantly reducing communication frequency through its triggering mechanism. The entire approach ensures fixed-time convergence of the closed-loop system, and rigorous theoretical proof of this stability is provided. Simulation results confirm the effectiveness of the proposed scheme in handling external disturbances and delays, achieving accurate formation tracking with improved communication efficiency. This work provides a robust solution for multi-AUV coordination in challenging environments. Full article

A hybrid robust $H_{\infty }$ tracking-control design method is studied for linear stochastic systems in which the parameters of the reference system are unknown but inferred from discrete-time observations. First, the reference system parameters are estimated by the least-squares method, and a corresponding data-dependent augmented system is constructed. Second, a Riccati matrix inequality is established for these systems, and a state-feedback $H_{\infty }$ controller is designed to improve tracking performance. Third, to mitigate large tracking errors, an error-feedback control scheme is introduced to compensate for dynamic tracking deviations. These results yield a hybrid control framework that integrates data observation, state-feedback $H_{\infty }$ control, and error-feedback $H_{\infty }$ control to address the tracking problem more effectively. Two numerical examples and one practical example demonstrate the effectiveness of the proposed method. Full article

Thepaper presents an efficient model predictive control framework formulated as a linear programming problem to control a servo drive with model uncertainty considerations from the viewpoint of the control performance. The model predictive framework is used to adopt $L_1$-type cost functions using absolute tracking errors, providing computational efficiency and enabling real-time implementation. A key contribution is the deployment of this approach on real hardware in a hardware-in-the-loop setting, supported by fully open-source code for Simulink Coder and C environments, verifying the solution scheme in real time. Experimental validation on a servo drive demonstrates the system’s tolerance for parameter uncertainties with slight performance degradation, resulting in an up to 18% increase in the considered control quality measure, between nominal parameters’ values and the worst configuration. The proposed linear programming approach enables constraint handling imposed on control signals and supports the arbitrary choice of prediction horizons and sampling intervals. The paper also includes a comprehensive derivation of the control law, controller implementation details, and stepwise experimental results showcasing the impact of uncertainties on control performance. This work and the attached code enable the authors to easily reproduce the proposed approach and extend it in their applications. Full article

Stable attitude control of unmanned or autonomous operations of vehicles moving in three spatial dimensions is essential for safe and reliable operations. Rigid body attitude control is inherently a nonlinear control problem, as the Lie group of rigid body rotations is a compact manifold and not a linear (vector) space. Prior research has shown that the largest possible domain of convergence is provided by smooth attitude feedback control laws are obtained using a Morse function on $\mathrm{SO}\left(3\right)$ as a measure of the attitude stabilization or tracking error. A polar Morse function on $\mathrm{SO}\left(3\right)$ has four critical points, which precludes the possibility of global convergence of the attitude state. When used as part of a Lyapunov function on the state space (the tangent bundle $\mathrm{TSO}\left(3\right)$) of attitude and angular velocity, it gives a globally continuous state-dependent feedback control scheme with the minimum of the Morse function as the almost globally asymptotically stable (AGAS) attitude state. In this work, we explore the use of explicitly time-varying gains for Morse functions for rigid body attitude control. This strategy leads to discrete switching of the indices of the three non-minimum critical points that correspond to the unstable equilibria of the feedback system. The resulting time-varying feedback controller is proved to be AGAS, with the additional desirable property that the time-varying gains destabilize the (locally) stable manifolds of these unstable equilibria. Numerical simulations of the feedback system with appropriate time-varying gains show that a trajectory starting from an initial state close to the stable manifold of an unstable equilibrium, converges to the desired stable equilibrium faster than the corresponding feedback system with constant gains. Full article

Background: Eye-tracking technology enables the objective quantification of oculomotor behavior, providing key insights into visuocognitive performance. This study presents a comparative analysis of visual attention patterns between rhythmic gymnasts and school-aged students using an optical eye-tracking system combined with machine learning algorithms. Methods: Eye movement data were recorded during controlled visual tasks using the DIVE system (sampling rate: 120 Hz). Spatiotemporal metrics—including fixation duration, saccadic amplitude, and gaze entropy—were extracted and used as input features for supervised models: Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), Decision Tree (CART), Random Forest, XGBoost, and a one-dimensional Convolutional Neural Network (1D-CNN). Data were divided according to a hold-out scheme (70/30) and evaluated using accuracy, F1-macro score, and Receiver Operating Characteristic (ROC) curves. Results: XGBoost achieved the best performance (accuracy = 94.6%; F1-macro = 0.945), followed by Random Forest (accuracy = 94.0%; F1-macro = 0.937). The neural network showed intermediate performance (accuracy = 89.3%; F1-macro = 0.888), whereas SVM and k-NN exhibited lower values. Gymnasts demonstrated more stable and goal-directed gaze patterns than students, reflecting greater efficiency in visuomotor control. Conclusions: Integrating eye-tracking with artificial intelligence provides a robust framework for the quantitative assessment of visuocognitive performance. Ensemble algorithms demonstrated high discriminative power, while neural networks require further optimization. This approach shows promising applications in sports science, cognitive diagnostics, and the development of adaptive human–machine interfaces. Full article