Auxiliary Signal Research Articles

Adaptive practical prescribed-time control for uncertain nonlinear systems with time-varying parameters

In this paper, adaptive practical prescribed-time (PPT) control is proposed for a class of uncertain nonlinear systems with time-varying parameters and unmodeled dynamics. By constructing a novel time-varying scaling function and utilizing nonlinear mapping, the PPT control is successfully resolved. The dynamical uncertainties resulting from unmodeled dynamics are estimated by employing an auxiliary available signal, and the unknown continuous terms are handled by the aid of radial basis function neural networks (RBFNNs). A novel adaptive control method is developed by introducing the compensating signals and dynamic surface control as well as practical prescribed-time control. All the signals involved are proved to be semi-globally uniformly ultimately bounded, and the tracking error could enter the pre-specified convergence region within a pre-specified time. The robotic manipulator system is used to demonstrate the effectiveness of the proposed control approach.

Enhancing the Reliability of NO2 Monitoring Using Low-Cost Sensors by Compensating for Temperature and Humidity Effects

The study investigates methods to enhance the reliability of NO2 monitoring using low-cost electrochemical sensors to measure gaseous pollutants in air by addressing the impacts of temperature and relative humidity. The temperature within a plastic container was controlled using an internal mica heater, an external hot air blower, or cooling packs, while relative humidity was adjusted using glycerine solutions. Findings indicated that the auxiliary electrode signal is susceptible to temperature and moderately affected by relative humidity. In contrast, the working electrode signal is less affected by temperature and relative humidity; however, adjustments are still required to determine gas concentrations accurately. Tests involving on/off cycles showed that the auxiliary electrode signal experiences exponential decay before stabilizing, requiring the exclusion of initial readings during monitoring activities. Additionally, calibration experiments in zero air allowed the determination of the compensation factor nT across different temperatures and humidity levels. These results highlight the importance of compensating for temperature and humidity effects to improve the accuracy and reliability of NO2 measurements using low-cost electrochemical sensors. This refinement makes the calibration applicable across a broader range of environmental conditions. However, the experiments also show a lack of repeatability in the zero air calibration.

Research on the Technology and Application of Remote Wireless Transmission of Digital Image Signal

Abstract. With the increasing demand for remote real-time high-quality video transmission, especially in the context of large area applications of unmanned aerial vehicle (UAV) and remote location instant live broadcast, traditional wired transmission methods have been surpassed by digital transmission methods due to their superior performance in image quality, transmission distance and delay. Therefore, this study focuses on the application of digital image wireless transmission in remote scenarios such as UAVs, including key digital transmission technologies such as Orthogonal Frequency Division Multiplexing (OFDM) and Coded Orthogonal Frequency Division Multiplexing (COFDM), as well as public network link communication technologies such as 4G and 5G, by means of a literature study and a case study. It also analyzes the development of WiFi, Lightbridge and OcuSync graphics transmission systems using DJI brand products as case studies. The analysis shows that digital image wireless transmission, especially COFDM and advanced proprietary systems such as DJI's OcuSync, has significant advantages in terms of transmission distance, interference immunity, encryption techniques, and reusability. However, factors such as building obstruction, frequency interference, and weather conditions can affect the transmission quality. To mitigate these challenges, solutions are proposed, such as auxiliary signal enhancement systems, adaptive frequency hopping and software-defined radio technologies.

Fault Impulse Inference and Cyclostationary Approximation: A feature-interpretable intelligent fault detection method for few-shot unsupervised domain adaptation

With the advancement of intelligent detection for rotating machinery, numerous domain adaptation methods have been devised to transfer detection knowledge from one source domain working condition to another target domain working condition, involving extensive transfer scenarios including labeled, few-shot labeled, and unlabeled target conditions. Yet, learning from sparsely labeled signals in the source domain working condition and transferring to unlabeled target conditions, termed few-shot unsupervised domain adaptation (FUDA), is closer to reality but almost unexplored. Diverging from the intuition of combining existing transfer and few-shot learning technologies, this paper pioneers a novel single learning principle focusing on the cyclostationary mechanism (CT) of fault signals. In its implementation, named cyclically enhanced cyclostationary variational autoencoder (CCTVAE), the CT principle motivates the encoder to infer domain-shared representations with fault impulses, and the decoder approximates the cyclostationary structure containing the clear fault and working condition information. Then, auxiliary samples for few-shot expansion are generated by adjusting cyclic parameters of the posterior distribution of representations. Experimentally, CCTVAE achieves commendable results on simulated and real fault datasets. Even for compound faults, domain-shared representations and generated auxiliary signals manifest interpretable fault-indicating spectral lines in the frequency domain, underscoring method reliability.

Command filter‐based adaptive finite‐time control of fractional‐order nonlinear constrained systems with input saturation

AbstractIn this article, the command filter‐based finite‐time tracking control issue is explored for fractional‐order nonlinear constrained systems (FONCSs) with saturated input. Firstly, a novel fractional‐order Lyapunov stability lemma is put forward, thereby offering a reliable strategy for finite‐time convergence of FONCSs. By employing an improved error compensation system and introducing auxiliary signal, a new finite‐time command filtered backstepping technique is developed. The proposed control strategy guarantees the finite‐time convergence characteristic of filtering signals, in addition to alleviate the impacts produced by saturated input. After that, a modified nonlinear state‐dependent function (NSDF) is developed to solve the state constraint problem while fulfilling the concavity condition. In comparison to the existing constrained control techniques in FONCSs, this approach not only ensures finite‐time convergence of the tracking error but also gets rid of feasibility demands. Ultimately, two simulation scenarios are supplied to illustrate the feasibility and usefulness of the suggested strategy.

GRNet:Geometry Restoration for G-PCC Compressed Point Clouds Using Auxiliary Density Signaling.

The lossy Geometry-based Point Cloud Compression (G-PCC) inevitably impairs the geometry information of point clouds, which deteriorates the quality of experience (QoE) in reconstruction and/or misleads decisions in tasks such as classification. To tackle it, this work proposes GRNet for the geometry restoration of G-PCC compressed large-scale point clouds. By analyzing the content characteristics of original and G-PCC compressed point clouds, we attribute the G-PCC distortion to two key factors: point vanishing and point displacement. Visible impairments on a point cloud are usually dominated by an individual factor or superimposed by both factors, which are determined by the density of the original point cloud. To this end, we employ two different models for coordinate reconstruction, termed Coordinate Expansion and Coordinate Refinement, to attack the point vanishing and displacement, respectively. In addition, 4-byte auxiliary density information is signaled in the bitstream to assist the selection of Coordinate Expansion, Coordinate Refinement, or their combination. Before being fed into the coordinate reconstruction module, the G-PCC compressed point cloud is first processed by a Feature Analysis Module for multiscale information fusion, in which kNN-based Transformer is leveraged at each scale to adaptively characterize neighborhood geometric dynamics for effective restoration. Following the common test conditions recommended in the MPEG standardization committee, GRNet significantly improves the G-PCC anchor and remarkably outperforms state-of-the-art methods on a great variety of point clouds (e.g., solid, dense, and sparse samples) both quantitatively and qualitatively. Meanwhile, GRNet runs fairly fast and uses a smaller-size model when compared with existing learning-based approaches, making it attractive to industry practitioners.

SMALNet: Segment Anything Model Aided Lightweight Network for Infrared Image Segmentation

Infrared based visual perception is important for night vision of autonomous vehicles, unmanned aerial vehicles (UAVs), etc. Semantic segmentation based on deep learning is one of the key techniques for infrared vision-based perception systems. Currently, most of the advanced methods are based on Transformers, which can achieve favorable segmentation accuracy. However, the high complexity of Transformers prevents them from meeting the real-time requirement of inference speed in resource constrained applications. In view of this, we suggest several lightweight designs that significantly reduce existing computational complexity. In order to maintain the segmentation accuracy, we further introduce the recent vision big model — Segment Anything Model (SAM) to supply auxiliary supervisory signals while training models. Based on these designs, we propose a lightweight segmentation network termed SMALNet (Segment Anything Model Aided Lightweight Network). Compared to existing state-of-the-art method, SegFormer, it reduces 64% FLOPs while maintaining the accuracy to a large extent on two commonly-used benchmarks. The proposed SMALNet can be used in various infrared based vision perception systems with limited hardware resources.

A Synchronization Algorithm for MBOC Signal Based on Reconstructed Correlation Function

In order to address the ambiguous synchronization problem caused by the multi-peak nature of the autocorrelation function of the modulated signal of the Multiplexed Binary Offset Carrier (MBOC) in the Global Navigation Satellite System (GNSS), a new synchronization algorithm for MBOC signals is presented in this paper, which uses a reconstruction correlation function to effectively handle synchronization ambiguities associated with multi-peak signals. The paper proposes an algorithm for reconstructing the correlation function of the MBOC signal by analyzing its characteristics. The algorithm generates three local auxiliary signals, namely, pseudo-random codes (PRN), BOC(1,1) signals, and MBOC signals, which are correlated with the received signal. By combining the three correlation functions, the algorithm produces a reconstructed correlation function based on reconstruction rules, eliminating side peaks and achieving unambiguous synchronization. Simulation results show that the proposed algorithm in this paper eliminates all side peaks while maintaining a high detection probability, and its deblurring capability is optimal compared to other algorithms. In addition, the discriminant curve shows that the algorithm in this paper successfully eliminates all the mis-locked points, and the slope gain is improved by more than 2.5 dB compared with other algorithms, and the anti-multipath performance of the algorithm in this paper is better than that of other traditional algorithms, such as ASPeCT (Autocorrelation Side-Peak Cancellation Technique).

Distributed avoidance-accommodating flexible performance approach for adaptive formation tracking of underactuated surface vehicles against moving obstacles

A novel strategy for formation tracking of multiple uncertain underactuated surface vehicles is introduced in this study, to maintain the prescribed formation performance in the presence of moving obstacles with unknown velocities. Unlike the existing methods for prescribed formation performance control, our key contribution is the development of distributed avoidance-accommodating flexible performance functions (FPFs). These functions prevent deviation from the prescribed performance bounds while navigating around moving obstacles, considering input saturation and model uncertainties. We design local adaptive formation controllers by using distributed posture errors and avoidance-accommodating FPFs. Auxiliary signals that facilitate adjustment of the posture errors and performance functions during obstacle avoidance are derived using a recursive design that ensures Lyapunov stability. Additionally, command filters for virtual control laws are redesigned as saturation-compensating filters to solve the input saturation problem. Consequently, the proposed controller ensures the prescribed distributed formation performance, even in scenarios involving moving obstacles and input saturation, a capability lacking in conventional prescribed performance-based formation controllers, which can become unstable in such scenarios. The theoretical findings of this study are validated through comparative simulations.

A modified sidelobe blanking beamforming method for ultrasound plane wave imaging.

In this paper, an ultrasound beamforming method for plane wave (PW) imaging based on modified sidelobe blanking (MSLB) is proposed to improve image resolution and contrast ratio (CR). In this framework, PWs from various angles were designed to create main and auxiliary beamformer signals. Specifically, the PW signals from all angles were first coherently combined to serve as the main beamformer output signals. To prevent excessive clutter and noise, output signals in the main beamformer were weighted by the generalized coherence factor. Subsequently, the PW signals were split into positive and negative angles to perform a subtraction, creating the auxiliary beamformer. Finally, signals in the main beamformer were compared with the signals in the auxiliary beamformer point by point to further eliminate the noises and clutters. Compared with the delay and sum, full width at half maximum of the MSLB for point targets was reduced by an average of 54.17% and 51.65% in simulations and experiments, respectively; and the corresponding CR was improved by 55.38% and 18.40% on average. The MSLB method provided better imaging quality in human carotid arteries. In conclusion, the proposed method can effectively improve image resolution and CR with low computational complexity.

Neuro-adaptive path following control of autonomous ground vehicles with input deadzone

This article investigates the path-following control problem of an autonomous ground vehicle (AGV) with unknown external disturbances and input deadzones. Neural networks are used to estimate unknown external disturbances, dead zones, and nonlinear functions. The minimum learning parameter scheme is employed to adjust the neural network to reduce the computational load. A backstepping control is proposed to facilitate the tracking of the target path. The steady-state path-following error is decreased by adding an integral error term to the backstepping controller. Command filtering is employed to address the explosion of the complexity issue of the conventional backstepping approach, and the filtering error is compensated via an auxiliary signal. Lyapunov stability study indicates that the AGV closed-loop system is bounded by the proposed control with reasonable accuracy. At last, simulations are given to demonstrate the potential of the proposed scheme in path-following control.

Neural adaptive performance guaranteed formation control for USVs with event-triggered quantized inputs

ABSTRACT This study presents an adaptive fault-tolerant performance guaranteed formation control strategy with event-triggered quantised inputs (EDQI) for unmanned surface vessels (USVs) in the presence of model uncertainties and actuator faults. Firstly, a prescribed performance formation guidance control law is proposed to allow formation tracking errors to evolve within the performance envelope and convergence to prescribed steady regions at an appointed time. Then, minimal-parameter-learning-based neural networks (MLP-NN) are used to tackle model uncertainties. To reduce the frequency of actuator updates and the communication burden on the channel, a dynamic memory event-triggered quantised input strategy is investigated. In addition, adaptive estimators and auxiliary design signals are built to compensate for the effects caused by input quantisation and actuator faults. It is proved that all closed-loop signals are bounded and formation errors can converge to the prescribed region at an appointed time. Finally, the effectiveness of the proposed controllers is verified by numerical simulations.

A novel auxiliary signal design algorithm for weak fault isolation based on zonotopic optimization

A novel active isolation method for weak faults based on zonotopes is proposed. First, a zonotopic filter is designed to estimate the system state; subsequently, an auxiliary signal is designed based on the obtained state set, and the auxiliary signal is added to both the normal and fault models to ensure that the intersection of their state sets is empty. Then, the intersection solution process is transformed into an optimization problem. Finally, simulation results from a numerical simulation and an experiment on a DC–DC circuit platform verify that the auxiliary signal designed using this method is more optimal and less conservative compared with other auxiliary signal design methods.

Square Parametric Excitation: A Digital Resonant Method for the Quadrupole Ion Trap.

Digital ion trap technology is an alternate method for driving quadrupole ion traps and mass filters using variable frequency, fixed amplitude RF square waves in place of variable amplitude, fixed frequency RF sine waves. This technique offers some advantages such as an increase in the high mass analysis range by varying frequency and lower overall voltage requirements. Here, we present a complex square waveform developed for resonant parametric excitation in a quadrupole linear ion trap. Unlike traditional resonance methods, the driving RF square wave and auxiliary square wave are coupled using the same digital circuitry without the need for transformer coupling. In this work, we use this complex waveform to selectively excite the first order parametric resonances of ion motion. The square parametric excitation method presented here employs a simple and repetitive circuit design consisting of a low-voltage waveform generator followed by a series of high-voltage MOSFET switches. This design allows for resonance methods to be easily implemented in the all-digital quadrupole. The complex square waveform can perform the same useful functions as sine wave auxiliary signals, such as selective mass elimination and mass isolation. We also demonstrate that the mass resolution performance and S/N of our digital mass spectrometer is improved by applying the complex square waveform during ion ejection.

Dynamic event-based cooperative control of virtually coupled high-speed trains: an ADP-based optimal control scheme

This paper is concerned with the cooperative control issue of virtually coupled high-speed trains (VCHSTs) under an adaptive dynamic programming (ADP) framework, where a dynamic event-triggered mechanism is employed to govern the communication between sensors and observers. A learning-based observer, equipped with a robust term, is exploited to mitigate the effect of the unknown nonlinear dynamics of the VCHSTs. Furthermore, to realise the distributed design, an auxiliary control signal is employed to simply the closed-loop system and also integrated into the cost function to handle the coupled nature of the investigated systems. The Hamilton–Jacobi–Bellman (HJB) equation is approximated by actor and critic networks in the ADP framework, which iteratively acquires the desired control strategy. By resorting to the common Lyapunov stability, some sufficient conditions are derived to achieve the desired observer gain and learning rates of NNs. Ultimately, the control scheme's efficiency is confirmed by simulations of VCHSTs.

Deep deterministic policy gradient for adaptive power system stabilization and voltage regulation

This paper focuses on the challenges posed by the time-variable and nonlinear nature of energy systems. In this systems set points fluctuate due to load variations, generator losses, and disturbances such as short circuit faults. Maintaining power system stability and effectively damping low-frequency oscillations are crucial objectives. To enhance damping and dynamic stability in modern systems power system stabilizers (PSSs) are typically employed. These facilities generate auxiliary control signals for the generator excitation system, diminishing oscillations. In this study, by involving a deep deterministic policy gradient (DDPG) based on a synergetic control approach to the excitation system of synchronous generators, a novel approach to achieving fast and adaptive stability improvement and voltage regulation is suggested. The presented technique incorporates an intelligent synergetic PSS that effectively stabilizes the voltage response of a single machine connected to an infinite bus power system. To leverage its adaptive capability in controller parameter adjustments, an auxiliary deep DDPG algorithm with an Actor-Critic architecture is designed. The suggested procedure offers a promising avenue for effectively handling electromechanical oscillations and achieving improved voltage regulation and transient stability in power systems.

Input Design for Active Fault Detection: Reconciling System Control Objectives.

Active fault detection (AFD) is the newest frontier in the field of fault detection and has drawn increasing amounts of research attention. AFD technology can enhance fault detection performance by injecting a predesigned auxiliary input signal for a specific fault. In most existing studies, system control objectives are not fully considered in the auxiliary input design of AFD. This article investigates a new reconciliatory input design problem for both achieving control objectives and improving fault detection performance. An exemplary algorithm for the reconciliatory input design is proposed, by using a trajectory optimization approach. The proposed algorithm consists of three parts: 1) residual generation; 2) trajectory optimization; and 3) input design. A state observer is designed to obtain residual signals used as fault indicators. Considering the optimization index composed of the fault indicators, a trajectory optimization technique is carried out to find an optimal system trajectory which can improve the fault detection ability to the greatest extent. The control input is designed to track this optimal trajectory while complying with system physical constraints. In order to demonstrate the effectiveness of the proposed methodology, simulation cases on an underwater manipulator are conducted.

A double-emission molecularly imprinted ratiometric fluorescent sensor based on carbon quantum dots and fluorescein isothiocyanate for visual detection of p-nitroaniline.

A novel molecularly imprinted ratiometric fluorescent sensor CQDs@MIP/FITC@SiO2 for the detection of p-nitroaniline (p-NA) was constructed through the mixture of CQDs@MIP and FITC@SiO2 in the ratio of 1:1 (VCQDs@MIP:VFITC@SiO₂). The polymers of CQDs@MIP and FITC@SiO2 were prepared by sol-gel method and reversed-phase microemulsion method, respectively. CQDs@MIP was used as the auxiliary response signal and FITC@SiO2 was used as the reference enhancement signal. The signal was measured at excitation/emission wavelengths of 365/438, 512nm. The sensor showed good linearity in the concentration range 0.14-40.00 µM (R2 = 0.998) with a detection limit of 0.042 µM for p-NA. The color change of "blue-cyan-green" could be observed by the naked eye under 365nm UV light, thus realizing the visual detection of p-NA. The sensor presented comparable results compared with high-performance liquid chromatography (HPLC) method for the detection of p-NA in hair dye paste and aqueous samples with recoveries of 96.8-103.7% and 95.8-104.4%, respectively. It was demonstrated that the constructed sensor possesses the advantages of simplicity, excellent selectivity, superior sensitivity, and outstanding stability.

Distributed Adaptive Formation Tracking for a Class of Uncertain Nonlinear Multiagent Systems: Guaranteed Connectivity Under Moving Obstacles.

This article explores a guaranteed network connectivity problem during moving obstacle avoidance within a distributed formation tracking framework for uncertain nonlinear multiagent systems with range constraints. We investigate this problem based on a new adaptive distributed design using nonlinear errors and auxiliary signals. Within the detection range, each agent regards other agents and static or dynamic objects as obstacles. The nonlinear error variables for formation tracking and collision avoidance are presented, and the auxiliary signals in formation tracking errors are introduced to maintain network connectivity under the avoidance mechanism. The adaptive formation controllers using command-filtered backstepping are constructed to ensure closed-loop stability with collision avoidance and preserved connectivity. Compared with the previous formation results, the resulting features are as follows: 1) the nonlinear error function for the avoidance mechanism is considered an error variable, and an adaptive tuning mechanism for estimating the dynamic obstacle velocity is derived in a Lyapunov-based control design procedure; 2) network connectivity during dynamic obstacle avoidance is preserved by constructing the auxiliary signals; and 3) owing to neural networks-based compensating variables, the bounding conditions of time derivatives of virtual controllers are not required in the stability analysis.

A model of the improved NOMA algorithm based on representation and modulation.

For the successful implementation of mobile communication networks in the spectral range of 30-300 GHz, it is necessary to solve several problems related to terminal subscriber devices. And one of the main problems is to be blocked by small line-of-sight transmission objects. Ways to solve this problem are densification of cellular infrastructure in the terahertz range with the help of mobile mobile communication nodes, as well as the use of simultaneous connection of subscriber terminals to two or more cells at the same time [1; 2]. In this work, an improved NOMA algorithm based on relaying and modulation called M-СО-NOMA is proposed to solve this problem. Unlike the known ones, it differs in that, firstly, before broadcasting into the radio air in the first time slot, terminal devices are clustered in the service area of the base station of the mobile network using the algorithm for estimating the fractal dimension and, secondly, during the formation of the general transport signal for terminal devices of subscribers, the base station modulates the signals of remote users on the quadrature, and the signals of nearby users on the real component of the QPSK constellation. The proposed improved M-CO-NOMA algorithm eliminates the main drawbacks of NOMA by introducing orthogonality at the stage of signal preparation instead of including it in the spectrum. It is shown that the proposed improved algorithm is effective in terms of such indicators as the reduction of SER, computational complexity, and interference compared to the usual NOMA. In addition, the integrated solution based on M-NOMA and СО-NOMA made it possible to obtain new innovative opportunities for ensuring data transmission when blocking direct transmission in the terahertz frequency range due to auxiliary signal relaying between subscribers. Calculation of the information efficiency of the developed integrated solution showed that for systems with NOMA and M-СО-NOMA, this indicator is equal to 4.50 e-2 and 6.00 e-2, respectively. Thus, the calculated information efficiency value of the M-CO-NOMA system is 1.5 times greater than that of NOMA.