Search Results (43)

Using cable transmission in underwater manipulators helps to reduce the mass and rotational inertia of distal moving components, but the control performance of cable-driven joints is affected by flexible cable transmission, equivalent joint-side friction, hydrodynamic effects, and external disturbances. This paper proposes a control method combining time-delay estimation (TDE) with gain-scheduled sliding mode control (GSMC) for a cable-driven joint of an underwater manipulator. TDE uses delayed control-input and joint-acceleration data to estimate and compensate for the lumped dynamic term in the equivalent joint model online. GSMC employs a composite sliding surface and an error-dependent gain-scheduling mechanism to suppress trajectory-tracking errors in the presence of the TDE estimation residual. In joint-level MATLAB/Simulink R2024b simulations, smooth-step, sinusoidal-trajectory-tracking, and ablation results under predefined combined-uncertainty conditions, together with the results of 50 paired Monte Carlo runs, show that TDE-GSMC achieves the lowest major tracking-error indices among the four methods for the smooth-step and $0.35\mathrm{Hz}$ sinusoidal trajectories and also yields the lowest mean tracking error and 95th percentile of the disturbance peak in the Monte Carlo simulations; the ablation results further characterize the performance differences among the tested controller configurations. Full article

An adaptive proximate fixed-time terminal sliding mode control (FTTSMC) based on time-delay estimation (TDE) is proposed to ensure high-precision trajectory tracking of robot manipulators subject to unknown dynamics and external disturbances. The controller employs TDE to reconstruct system dynamics online, requiring only the inertia matrix bounds rather than full precise nominal models. Crucially, it replaces the conventional constant bound assumption for the TDE error with a robust state-dependent one, thereby enhancing robustness against discontinuous disturbances. Rigorous Lyapunov stability analysis confirms the fixed-time convergence of the sliding variable and the proximate fixed-time convergence of the tracking error, providing explicit upper bounds on the convergence time. Comparative simulations and experiments on a SCARA robotic platform demonstrate that the developed strategy maintains transient performance comparable to baseline fixed-time approaches while achieving superior steady-state accuracy. Characterized by a compact structure and low computational complexity, the proposed controller exhibits strong potential for high-performance real-time robotic applications. Full article

This paper presents a control framework that integrates adaptive sliding mode control (ASMC), time-delay control (TDC), and admittance filtering to achieve robust force and position tracking in robot manipulators. TDC is employed to estimate unmodeled dynamics using delayed measurements, while ASMC enhances robustness by compensating for time-delay estimation (TDE) errors and mitigating chattering effects. An adaptive law incorporating a decline-rate reduction factor is introduced to explicitly regulate the decay of the adaptive gain inside the boundary layer, thereby preserving compensation capability against time-delay estimation errors and external disturbances for a longer duration while improving position tracking performance. In addition, the admittance mechanism converts force-tracking errors into position correction signals, enabling force tracking without modifying the underlying position control structure. The stability of the closed-loop system is analyzed based on Lyapunov theory, ensuring bounded tracking performance in the presence of estimation errors and uncertainties. Simulation results demonstrate that the proposed method improves position tracking accuracy—reducing the root mean square error (RMSE) from 0.0522 mm to 0.019 mm—while maintaining reliable force tracking performance. Full article

Tendon-driven manipulators possess obvious advantages compared to rigid-link manipulators, such as lighter weight, greater flexibility, and adaptability to confined spaces. To solve the problems of backlash and improve the accuracy of motion in specific application environments, this paper proposes a novel single-tendon-driven design for each joint of the manipulator. Kinematic modeling of the manipulator is systematically derived. Then, a decoupling algorithm is designed to mitigate motion coupling effects and enable accurate mapping between motor inputs and joint motions. Moreover, to improve the accuracy of trajectory tracking control for the tendon-driven manipulator, this paper proposes a nonsingular terminal sliding mode (NTSM) control scheme based on time-delay estimation (TDE). TDE is used to estimate unknown disturbances. An adjustable parameter was introduced based on TDE technology, which can enhance the system’s robustness against uncertainties and external disturbances. The stability of the closed-loop control system is verified through Lyapunov stability theory. Finally, decoupling experiments are conducted to validate the kinematic model and the feasibility of the proposed design. And comparative experiments are performed to prove the advantages of the proposed control scheme. Full article

As typical underactuated systems, overhead cranes are widely utilized in heavy-load transportation. However, their strong nonlinear coupling and underactuated characteristics complicate precise positioning and payload swing suppression. Furthermore, model uncertainties and external disturbances in practical environments increase control complexity and degrade system performance. To address these issues, this paper develops a trajectory tracking control scheme based on time delay estimation (TDE). Specifically, some transformations are made for the dynamic model and the TDE mechanism is used to estimate unknown nonlinear dynamics and external disturbances. Then, a sliding mode trajectory tracking controller, along with the TDE mechanism, is proposed for the trajectory tracking control and uncertainties estimation of the overhead crane system. Rigorous mathematical analysis is provided to demonstrate the asymptotic stability of the closed-loop system. Finally, simulation results verify the effectiveness of the proposed method in comparison with the existing control methods. Full article

This paper presents a synchronous sliding-mode control with time-delay estimation (SSMC-TDE)-based adaptive control framework for coordinated motion control of dual-arm robotic manipulators operating under system uncertainties. The baseline SSMC-TDE scheme is constructed using synchronization and cross-coupling errors to ensure precise coordinated motion among robot joints, while sliding-mode control effectively handles strong nonlinearities, and the time-delay estimation technique approximates lumped uncertainties arising from external disturbances, modeling errors, and payload variations. The stability of the closed-loop system is rigorously analyzed and guaranteed using the Lyapunov theory. To overcome performance degradation caused by manually tuned control gains, a deep reinforcement learning-assisted parameter adaptation mechanism is integrated into the SSMC-TDE structure. Specifically, a Deep Deterministic Policy Gradient (DDPG) algorithm is employed to adapt selected control gains online through a reward function designed to simultaneously enhance motion synchronization and reduce trajectory-tracking errors, while preserving the stability properties of the underlying controller. Simulation studies are conducted within a co-simulation framework integrating MATLAB/Simulink and ROS/Gazebo for a dual-arm robotic platform. Quantitative evaluations based on the root mean square error (RMSE) of trajectory-tracking and synchronization errors across all six joints demonstrate that, averaged over both scenarios, the proposed DDPG-assisted SSMC-TDE achieves an overall RMSE reduction of 35.52% and 99.3% compared with conventional SSMC and SSMC-TDE controllers, respectively, confirming its superior performance and robustness under system uncertainties. Full article

Micro–nano-sized processing equipment requires high levels of precision, necessitating residual stress measurement to maintain stability. Ultrasonic stress measurement is an effective method but is hindered by high sampling-rate requirements, leading to excessive power consumption and hardware costs. This study presents a low-sampling-rate method based on the novel Frequency-domain Zero-padded Cross-correlation Time Delay Estimation (FZC-TDE) algorithm. Tensile validation experiments determined the minimum hardware sampling-rate requirement: rates below 25 MSps (even with interpolation) fail to characterize temporal delay variations effectively, and a rate of at least 20 times the signal frequency is required for ±10 MPa accuracy. The proposed FZC-TDE utilizes a frequency-domain fusion operation (frequency-domain zero-padding interpolation combined with cross-correlation) to enable real-time, high-resolution delay measurement at low rates. Comparative experiments show that time-domain interpolation methods (Linear, PCH, Cubic Spline) achieve similar stress estimation accuracy at the same rate (e.g., 7.4–8.7 MPa error at 100 MSps), while FZC-TDE (10.3 MPa error) offers superior computational efficiency. At 100 MSps, FZC-TDE maintains a stable computation time (~2.8 ms), while those of interpolation methods increase significantly (20–30 ms) due to higher oversampling factors. Furthermore, FZC-TDE reduces the number of arithmetic operations by 75% (2.26 million vs. ≥9.18 million for 128× oversampling on 1024 points) and exhibits slower computational load growth with oversampling ratios. Thus, FZC-TDE provides an optimal balance of acceptable accuracy and significantly enhanced efficiency, particularly for real-time or resource-constrained applications. This work reduces sampling-rate constraints and supports advancements in micro–nano-sized processing equipment and device performance. Full article

To accommodate the contact force between a robot and its environment, this paper presents an adaptive control framework for the impedance control of a manipulator with time delay estimation (TDE). To simplify the complex system model and yield adaptive feedback compensation, a voltage-based motor control approach was presented. Compared to the torque-based control model, the voltage-based control model is computationally more efficient and practically feasible. The proposed adaptive law was designed to compensate for the errors produced by the TDE. Through a stability analysis, the control framework was verified by semi-global uniform ultimate boundedness (SGUUB) stability. Experimental results are discussed, and the effectiveness of the adaptive control framework is demonstrated. Full article

The real nature of little red dots (LRDs), a class of very compact galaxies in the early Universe recently discovered by the James Webb Space Telescope, is still poorly understood. The most popular theories competing to interpret the phenomena include active galactic nuclei and enhanced star formation in dusty galaxies. To date, however, neither model gives a completely satisfactory explanation to the population as a whole; thus, alternative theories have arisen, including tidal disruption events (TDEs). By considering observational constraints on the radio emission of LRDs, we discuss whether TDEs are adequate alternatives solving these high-redshift enigmas. We utilise radio flux density upper limits from LRD stacking analyses, TDE peak radio luminosities, and volumetric density estimates. We find that the characteristic values of flux densities and luminosities allow radio-quiet TDEs as the underlying process of LRDs in any case, while the less common radio-loud TDEs are compatible with the model under special constraints only. Considering other factors, such as volumetric density estimates, delayed and long-term radio flares of TDEs, and cosmological time dilation, TDEs appear to be a plausible explanation for LRDs from the radio point of view. Full article

This study presents an adaptive sliding mode control strategy tailored for robotic manipulators, featuring a quasi-convex function-based control gain and a time-delay estimation (TDE) enhanced by neural networks. To compensate for TDE errors, the proposed method utilizes both the previous TDE error and radial basis function neural networks with a weight update law that includes damping terms to prevent divergence. Additionally, a continuous gain function that is quasi-convex function dependent on the magnitude of the sliding variable is proposed to replace the traditional switching control gain. This continuous function-based gain has effectiveness in suppressing chattering phenomenon while guaranteeing the stability of the robotic manipulator in terms of uniform ultimate boundedness, which is demonstrated through both simulation and experiment results. Full article

High-frequency (HF) communication is critical for applications such as over-the-horizon positioning and ionospheric detection. However, precise time-delay estimation in complex HF channels faces significant challenges from multipath fading, Doppler shifts, and noise. This paper proposes a Modulation Signal-based Adaptive Time-Delay Estimation (MATE) algorithm, which effectively decouples carrier and modulation signals and integrates phase-locked loop (PLL) and delay-locked loop (DLL) techniques. By leveraging the autocorrelation properties of 8PSK (Eight-Phase Shift Keying) signals, MATE compensates for carrier frequency deviations and mitigates multipath interference. Simulation results based on the Watterson channel model demonstrate that MATE achieves an average time-delay estimation error of approximately 0.01 ms with a standard deviation of approximately 0.01 ms, representing a 94.12% reduction in mean error and a 96.43% reduction in standard deviation compared to the traditional Generalized Cross-Correlation (GCC) method. Validation with actual measurement data further confirms the robustness of MATE against channel variations. MATE offers a high-precision, low-complexity solution for HF time-delay estimation, significantly benefiting applications in HF communication systems. This advancement is particularly valuable for enhancing the accuracy and reliability of time-of-arrival (TOA) detection in HF-based sensor networks and remote sensing systems. Full article

This paper introduces a novel model-free nonsingular fixed-time sliding mode control (MF-NFxTSMC) strategy for precise trajectory tracking in robot arm systems. Unlike conventional sliding mode control (SMC) approaches that require accurate dynamic models, the proposed method leverages the time delay estimation (TDE) approach to effectively estimate system dynamics and external disturbances in real-time, enabling a fully model-free control solution. This significantly enhances its practicality in real-world scenarios where obtaining precise models is challenging or infeasible. A significant innovation of this work lies in designing a novel fixed-time control framework that achieves faster convergence than traditional fixed-time methods. Building on this, a novel MF-NFxTSMC law is developed, featuring a novel singularity-free fixed-time sliding surface (SF-FxTSS) and a novel fixed-time reaching law (FxTRL). The proposed SF-FxTSS incorporates a dynamic proportional term and an adaptive exponent, ensuring rapid convergence and robust tracking. Notably, its smooth transition between nonlinear and linear dynamics eliminates the singularities often encountered in terminal and fixed-time sliding mode surfaces. Additionally, the designed FxTRL effectively suppresses chattering while guaranteeing fixed-time convergence, leading to smoother control actions and reduced mechanical stress on the robotic hardware. The fixed-time stability of the proposed method is rigorously proven using the Lyapunov theory. Numerical simulations on the SAMSUNG FARA AT2 robotic platform demonstrate the superior performance of the proposed method in terms of tracking accuracy, convergence speed, and control smoothness compared to existing strategies, including conventional SMC, finite-time SMC, approximate fixed-time SMC, and global fixed-time nonsingular terminal SMC (NTSMC). Overall, this approach offers compelling advantages, i.e., model-free implementation, fixed-time convergence, singularity avoidance, and reduced chattering, making it a practical and scalable solution for high-performance control in uncertain robotic systems. Full article

Closed-Kinematic Chain Manipulators (CKCM) have gained attention due to their precise Cartesian motion capability through coordinated active joint movements. Furthermore, ensuring synchronization among the joints of CKCMs is critical for reliable operation. An advanced control scheme for CKCMs that combines Nonsingular Fast Terminal Sliding Mode Control (NFTSMC) with Time Delay Estimation (TDE) while utilizing synchronization errors, namely Syn-TDE-NFTSMC, to effectively address joint errors in CKCMs was developed. NFTSMC enables fast convergence through nonlinear terminal sliding while TDE eliminates the need for prior knowledge of the robot’s dynamics, thereby simplifying its implementation and reducing its computational requirements. It is known that the inclusion of TDE reduces about 98% of the computational requirement of control schemes without TDE. The newly developed control scheme was rigorously evaluated using computer simulation and its control performance was compared with that of existing control methods. This paper presents an experimental study where the newly developed control scheme and other existing control schemes were applied to a real CKCM with 2 degrees of freedom (DOF). The experimental results confirm that the control scheme performed much better than other existing control schemes in terms of synchronization and control performance, achieving a reduction in maximum tracking errors of up to 81% as compared to other existing control schemes. The results confirm the efficacy of the newly developed control scheme in enhancing control precision and system stability, making it a promising solution for improving CKCM control strategies in real-world applications. Full article

In underwater signal processing, accurate time delay estimation (TDE) is of crucial importance for ensuring the reliability of data transmission. However, the complex propagation of sound waves and strong noise interference in the underwater environment make this task extremely challenging. Especially under the condition of low signal-to-noise ratio (SNR), the existing methods based on cross-correlation and deep learning struggle to meet requirements. Aiming at this core issue, this paper proposed an innovative solution. Firstly, a multi-sub-window reconstruction is performed on the frequency-sliding generalized colorboxpinkcross-correlation (FS-GCC) matrix between signals to capture the time delay characteristics from different frequency bands and conduct the enhancement and extraction of features. Then, the grayscale image corresponding to the generated FS-GCC matrix is used, and the multi-level noise features are extracted by the multi-layer convolution of denoising convolutional neural network (DnCNN), effectively suppressing the noise and improving the estimation accuracy. Finally, the model-agnostic meta-learning (MAML) framework is introduced. Through training tasks under various SNR conditions, the model is enabled to possess the ability to quickly adapt to new environments, and it can achieve the desired estimation accuracy even when the number of underwater training samples is limited. Simulation validation was conducted under the NOF and NCS underwater acoustic channels, and results demonstrate that our proposed approach exhibits lower estimation errors and greater stability compared with existing methods under the same conditions. This method enhances the practicality and robustness of the model in complex underwater environments, providing strong support for the efficient and stable operation of underwater sensor networks. Full article

Due to the difficulty in ensuring launch safety under unfavorable launch site conditions, restrictions regarding the selection of launch sites significantly weaken the maneuverability of the unsupported random vertical launch (URVL) mode. In this paper, a nonlinear robust control strategy is proposed to control the missile attitude by actively adjusting the oscillation of the launcher through the hydraulic actuator, enhancing the launching safety and the adaptability of the VMLS to the launching site. Firstly, considering the interaction among the launch canister, adapters, and missile, a 6-DOF dynamic model of the launch system is established, in combination with the dynamics of the hydraulic actuator. Then, in order to facilitate the nonlinear controller design, the seventh-order state-space equation is constructed, according to the dynamic model of the launch system. Subsequently, in view of the problem of “differential explosion” in the backstepping controller design of the seventh-order nonlinear system, a nonlinear dynamic surface control (DSC) framework is proposed. Meanwhile, the time delay estimation (TDE) technique is introduced to suppress the influence of the complex nonlinearities of the launch system on the missile attitude control, and a nonlinear robust control (NRC) is introduced to attenuate the TDE error. Both of these are integrated into the DSC framework, which can achieve asymptotic output tracking. Finally, numerical simulations are conducted to validate the superiority of the proposed control strategy in regards to missile launch response control. Full article