Dear Editor,This letter presents a prescribed-instant stabilization approach to high-order integrator systems by the Lyapunov method. Under the presented controller, the settling time of controlled systems is independ...
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Dear Editor,This letter presents a prescribed-instant stabilization approach to high-order integrator systems by the Lyapunov method. Under the presented controller, the settling time of controlled systems is independent of the initial conditions and equals the prescribed time instant.
The core component of the navigation system is the process of multi-sensor fusion localization in a challenging environment with limited GNSS constraints. This process is designed to robustly and precisely estimate th...
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The core component of the navigation system is the process of multi-sensor fusion localization in a challenging environment with limited GNSS constraints. This process is designed to robustly and precisely estimate the system state and map the traversed area. In this paper, we propose a low-drift, highly resilient multilevel navigation system with completely decoupled odometry and adaptive environmental mapping. A novel method for visual-LiDAR-inertial frame-to-frame odometry is presented to leverage the complementary strengths of these sensors. This odometry approach involves decoupling the 6-degree-of-freedom (6-DoF) state to allocate each state component to an appropriate submodule for estimation. An adaptive environmental mapping module that aims to align the target frame with the local map is proposed to refine the rough odometry pose. This module is achieved through the utilization of an adaptive keyframe strategy and the feature consistency constraint. Enhance the matching of keyframes to the map by reducing the point-to-feature error between frame points and map features. Additionally, the state is further refined by minimizing the feature consistency error on lines in the adjacent corner map and the local map at each keyframe. Our proposed algorithm is validated using both public and self-collected datasets, demonstrating superior results compared to state-of-the-art algorithms. IEEE
This study examines the stabilization issue of extended chained nonholonomic systems(ECNSs)with external *** the existing approaches,we transform the considered system into a fully actuated system(FAS)model,simplifyin...
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This study examines the stabilization issue of extended chained nonholonomic systems(ECNSs)with external *** the existing approaches,we transform the considered system into a fully actuated system(FAS)model,simplifying the stabilizing controller *** implement a separate controller design and propose exponential stabilization controller and finite-time stabilization controller under finite-time disturbance observer(FTDO)for the two system *** addition,we discuss the specifics of global stabilization control *** approach demonstrates that two system states exponentially or asymptotically converge to zero under the provided switching stabilization control strategy,while all other system states converge to zero within a finite time.
In this paper, a parametric design approach for stabilizing a quasi-linear second-order system with partitioned eigenstructure assignment(PESA) is investigated through output feedback control. The PESA approach is est...
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In this paper, a parametric design approach for stabilizing a quasi-linear second-order system with partitioned eigenstructure assignment(PESA) is investigated through output feedback control. The PESA approach is established by partitioning the desired eigenvalue matrix into two parts to separate the associated right and left eigenvectors into a subset of the generalized eigenvectors simultaneously. A parametric controller is established by solving two second-order generalized Sylvester matrix equations, and a certain form with the desired eigenstructure can be derived with the established quasi-linear output feedback controller. Unlike the prevailing approach that assigns the entire set of generalized eigenvectors, which is difficult to satisfy a large number of complicated constraints in practical systems by the normalized pair of right and left eigenvector matrices, a subset of the generalized eigenvectors is considered. In addition, the proposed PESA approach provides less computational load and is easy to use. A numerical example and application in spacecraft rendezvous are provided to verify the numerical economy and high efficiency of the proposed approach.
Conventional model predictive current control of permanent magnet synchronous machines (PMSMs) relies heavily on a precise mathematical model, which may be challenging to obtain in certain cases. To address this issue...
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Focusing on the non-concave trajectory constraint,a sliding-mode-based nonsingular feedback fast fixed-time three-dimensional terminal guidance of rotor unmanned aerial vehicle landing,planetary landing and spacecraft...
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Focusing on the non-concave trajectory constraint,a sliding-mode-based nonsingular feedback fast fixed-time three-dimensional terminal guidance of rotor unmanned aerial vehicle landing,planetary landing and spacecraft rendezvous and docking terminal phase with external disturbance is investigated in this ***,a fixed-time observer based on real-time differentiator is developed to compensate for the external disturbance,whose estimation error can converge to zero after a time independent of the initial ***,a sliding surface ensuring fixed-time convergence is *** sliding surface can guarantee that the vehicle achieves a non-concave trajectory,which is better for avoiding collision and maintaining the visibility of the landing site or docking ***,the nonsingular guidance ensuring the fixed-time convergence of the sliding surface is proposed,which is continuous and chatter *** last,three numerical simulations of Mars landing are performed to validate the effectiveness and correctness of the designed scheme.
Numerous studies have been conducted on microfluidic mixers in various microanalysis systems, which elucidated the manipulation and control of small fluid volumes within microfluidic chips. These studies have demonstr...
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Numerous studies have been conducted on microfluidic mixers in various microanalysis systems, which elucidated the manipulation and control of small fluid volumes within microfluidic chips. These studies have demonstrated the ability to control fluids and samples precisely at the microscale. Microfluidic mixers provide high sensitivity for biochemical analysis due to their small volumes and high surface-to-volume ratios. A promising approach in drug delivery is the rapid microfluidic mixer-based extraction of elemental iodine at the micro level, demonstrating the versatility and the potential to enhance diagnostic imaging and accuracy in targeted drug delivery. Micro-mixing inside microfluidic chips plays a key role in biochemical analysis. The experimental study describes a microfluidic mixer for extraction of elemental iodine using carbon tetrachloride with a gas bubble mixing process. Gas bubbles are generated inside the microcavity to create turbulence and micro-vortices resulting in uniform mixing of samples. The bubble mixing of biochemical samples is analyzed at various pressure levels to validate the simulated results in computational fluid dynamics(CFD). The experimental setup includes a high-resolution camera and an air pump to observe the mixing process and volume at different pressure levels with time. The bubble formation is controlled by adjusting the inert gas flow inside the microfluidic chip. Microfluidic chip-based gas bubble mixing effects have been elaborated at various supplied pressures.
Ultrasound computed tomography (USCT) is a noninvasive and non-ionizing imaging technique for soft tissue and limb bones. Full waveform inversion (FWI) has received increased interest due to its high resolution. The d...
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Real-time 6 Degree-of-Freedom(DoF)pose estimation is of paramount importance for various on-orbit *** from the development of deep learning,Convolutional Neural Networks(CNNs)in feature extraction has yielded impressi...
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Real-time 6 Degree-of-Freedom(DoF)pose estimation is of paramount importance for various on-orbit *** from the development of deep learning,Convolutional Neural Networks(CNNs)in feature extraction has yielded impressive achievements for spacecraft pose *** improve the robustness and interpretability of CNNs,this paper proposes a Pose Estimation approach based on Variational Auto-Encoder structure(PE-VAE)and a Feature-Aided pose estimation approach based on Variational Auto-Encoder structure(FA-VAE),which aim to accurately estimate the 6 DoF pose of a target *** methods treat the pose vector as latent variables,employing an encoder-decoder network with a Variational Auto-Encoder(VAE)*** enhance the precision of pose estimation,PE-VAE uses the VAE structure to introduce reconstruction mechanism with the whole ***,FA-VAE enforces feature shape constraints by exclusively reconstructing the segment of the target spacecraft with the desired *** evaluation against leading methods on public datasets reveals similar accuracy with a threefold improvement in processing speed,showcasing the significant contribution of VAE structures to accuracy enhancement,and the additional benefit of incorporating global shape prior features.
The interception probability of a single missile is the basis for combat plan design and weapon performance evaluation,while its influencing factors are complex and mutually *** calculation methods have very limited a...
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The interception probability of a single missile is the basis for combat plan design and weapon performance evaluation,while its influencing factors are complex and mutually *** calculation methods have very limited analysis of the influence mechanism of influencing factors,and none of them has analyzed the influence of the guidance *** paper considers the influencing factors of both the interceptor and the target more *** parameters include speed,guidance law,guidance error,fuze error,and fragment killing ability,while target performance includes speed,maneuverability,and *** this paper,an interception model is established,Monte Carlo simulation is carried out,and the influence mechanism of each factor is analyzed based on the model and simulation ***,this paper proposes a classification-regression neural network to quickly estimate the interception probability based on the value of influencing *** proposed method reduces the interference of invalid interception data to valid data,so its prediction accuracy is significantly better than that of pure regression neural networks.
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