Search Results (90)

The suppression of Rayleigh backscattering noise in a resonant fiber optic gyro (RFOG) is accompanied by the emergence of residual amplitude modulation (RAM) effects, which impact the bias performance of the RFOG output. In this paper, we propose a double demodulation technique that integrates reciprocal modulation and RAM feedback. By utilizing reciprocal modulation–demodulation along with a RAM feedback control method, we effectively suppress both RAM and laser frequency noise. Furthermore, the inherent suppression characteristics of the double modulation–demodulation scheme facilitate effective backscatter noise reduction. As a result, the gyro angular random walk of the RFOG has improved to $3^{°}/\sqrt{\mathrm{h}}$, and the long-term bias instability has been enhanced to 0.1°/h over a test duration of 10 h. Full article

In DC microgrids, optimizing the hybrid energy storage system (HESS) current control to meet the power requirements of the load is generally a difficult and challenging task. This is because the HESS always operates under various load conditions, which are influenced by measurement disturbances and parameter uncertainties. Therefore, in this paper, we propose the H_∞ state feedback control based on the reference modulation to improve the current tracking errors of the battery (Bat) and supercapacitor (SC) in the HESS for power tracking performance. Without altering the system control signal, the reference modulation technique combines the feedforward channel and output feedback signal directly to modulate the required currents of the Bat and SC derived from the required load power. The H_∞ state feedback control based on the required Bat and SC currents modulated by the reference modulation technique is proposed to improve the current tracking errors under the influence of measurement disturbances and parameter uncertainties without a disturbance observer. The ability of the reference modulation technique to attenuate the disturbance without the use of a disturbance observer is one advantage for improving transient performance. The improvement of the HESS’s power tracking performance in DC microgrids is confirmed by study results presented under the influence of measurement disturbances for nominal parameters and parameter uncertainties. Full article

In this paper, we tackle the decentralized-output feedback sampled-data disturbance rejection control for a dual-drive H-Gantry (DDHG) system with a symmetrical structure. For the DDHG system with disturbances, only the position information of the system at the sampling points can be utilized, such that the traditional control methods based on full state information of the DDHG system could not be used. To this end, a linear discrete-time generalized-proportional-integral observer (GPIO) based on the position information and reference trajectory of DDHG at the sampling points is constructed first, such that unmeasured states and disturbance can be estimated simultaneously. Then, a GPIO-based decentralized-output feedback sampled-data control (GPIO-DOFC) method is proposed by utilizing the estimations of the unmeasured states and disturbance. A strict theoretical analysis of the closed-loop system is carried out, which demonstrates that the desired trajectory could be tracked under the proposed GPIO-DOFC method. Finally, comparative studies are carried out between the proposed GPIO-DOFC method and the extended-state observer-based decentralized-output feedback sampled-data control (ESO-DOFC) method. These confirm the efficacy and feasibility of the proposed control scheme. Full article

In recent years, the use of video content has experienced exponential growth. The rapid growth of video content has led to an increased reliance on various video codecs for efficient compression and transmission. However, several challenges are associated with codecs such as H.265/High Efficiency Video Coding and H.266/Versatile Video Coding (VVC) that can impact video quality and performance. One significant challenge is the trade-off between compression efficiency and visual quality. While advanced codecs can significantly reduce file sizes, they introduce artifacts such as blocking, blurring, and color distortion, particularly in high-motion scenes. Different compression tools in modern video codecs are vital for minimizing artifacts that arise during the encoding and decoding processes. While the advanced algorithms used by these modern codecs can effectively decrease file sizes and enhance compression efficiency, they frequently find it challenging to eliminate artifacts entirely. By utilizing advanced techniques such as post-processing after the initial decoding, this method can significantly improve visual clarity and restore details that may have been compromised during compression. In this paper, we introduce a Deep Residual Enhanced Feature Generative Adversarial Network as a post-processing method aimed at further improving the quality of reconstructed frames from the advanced codec VVC. By utilizing the benefits of Deep Residual Blocks and Enhanced Feature Blocks, the generator network aims to make the reconstructed frame as similar as possible to the original frame. The discriminator network, a crucial element of our proposed method, plays a vital role in guiding the generator by evaluating the authenticity of generated frames. By distinguishing between fake and original frames, the discriminator enables the generator to improve the quality of its output. This feedback mechanism ensures that the generator learns to create more realistic frames, ultimately enhancing the overall performance of the model. The proposed method shows significant gain for Random Access (RA) and All Intra (AI) configurations while improving Video Multimethod Assessment Fusion (VMAF) and Multi-Scale Structural Similarity Index Measure (MS-SSIM). Considering VMAF, our proposed method can obtain 13.05% and 11.09% Bjøntegaard Delta Rate (BD-Rate) gain for RA and AI configuration, respectively. In the case of the luma component MS-SSIM, RA and AI configurations get, respectively, 5.00% and 5.87% BD-Rate gain after employing our suggested proposed network. Full article

This paper presents a method for designing a static output feedback integrated path tracking controller for autonomous vehicles. For path tracking, state–space model-based control methods, such as linear quadratic regulator, H_∞ control, sliding mode control, and model predictive control, have been selected as controller design methodologies. However, these methods adopt full-state feedback. Among the state variables, the lateral velocity, or the side-slip angle, is hard to measure in real vehicles. To cope with this problem, it is desirable to use a state estimator or static output feedback (SOF) control. In this paper, an SOF control is selected as the controller structure. To design the SOF controller, a linear quadratic optimal control and sliding mode control are adopted as controller design methodologies. Front wheel steering (FWS), rear wheel steering (RWS), four-wheel steering (4WS), four-wheel independent braking (4WIB), and driving (4WID) are adopted as actuators for path tracking and integrated as several actuator configurations. For better performance, a lookahead or preview function is introduced into the state–space model built for path tracking. To verify the performance of the SOF path tracking controller, simulations are conducted on vehicle simulation software. From the simulation results, it is shown that the SOF path tracking controller presented in this paper is effective for path tracking with limited sensor outputs. Full article

This paper studies the composite anti-disturbance static output feedback control problem of networked nonlinear Markov jump systems with general transition probabilities subject to multiple disturbances and deception attacks. The transition probabilities cover the known, uncertain with known bounds, and unknown cases. The unmatched disturbance and deception attacks are attenuated by the static output controller, while the matched disturbance is observed and compensated by the disturbance observer. Then, a composite anti-disturbance static output controller, including a linear part and a nonlinear part, is constructed to satisfy the stochastic $H_{\infty }$ stability. By using the Finsler lemma, sufficient conditions formed as symmetric linear matrix inequalities are derived to design the gains of disturbance observer and the output feedback controller. Finally, some simulations are given to illustrate the feasibility of the developed strategy. Full article

This paper reviews various design approaches for sensing schemes that utilize silicon nanowire (SiNW) ion-sensitive field-effect transistors (ISFETs) for pH-sensing applications. SiNW ISFETs offer advantageous characteristics, including a high surface-to-volume ratio, fast response time, and suitability for integration with complementary metal oxide semiconductor (CMOS) technology. This review focuses on SiNW ISFET-based biosensors in three key aspects: (1) major fabrication processes and device structures; (2) theoretical analysis of key performance parameters in readout circuits such as sensitivity, linearity, noise immunity, and output range in different system configurations; and (3) an overview of existing readout circuits with quantitative evaluations of N-type and P-type current-mirror-based circuits, highlighting their strengths and limitations. Finally, this paper proposes a modified N-type readout scheme integrating an operational amplifier with a negative feedback network to overcome the low sensitivity of conventional N-type circuits. This design enhances gain control, linearity, and noise immunity while maintaining stability. These advancements are expected to contribute to the advancement of the current state-of-the-art SiNW ISFET-based readout circuits. Full article

Video encoding technology is a foundational component in the advancement of modern technological applications. The latest standard in universal video coding, H.266/VVC, features a quad-tree with nested multi-type tree (QTMT) partitioning structure, which represents an improvement over its predecessor, High-Efficiency Video Coding (H.265/HEVC). This configuration facilitates adaptable block segmentation, albeit at the cost of heightened encoding complexity. In view of the aforementioned considerations, this paper puts forth a deep learning-based approach to facilitate CU partitioning, with the aim of supplanting the intricate CU partitioning process observed in the Versatile Video Coding Test Model (VTM). We begin by presenting the Gradient Feedback Hierarchical CNN (GFH-CNN) model, an advanced convolutional neural network derived from the ResNet architecture, enabling the extraction of features from 64 × 64 coding unit (CU) blocks. Following this, a hierarchical network diagram (HND) is crafted to depict the delineation of partition boundaries corresponding to the various levels of the CU block’s layered structure. This diagram maps the features extracted by the GFH-CNN model to the partitioning at each level and boundary. In conclusion, a LightGBM-based decision tree classification model (L-DT) is constructed to predict the corresponding partition structure based on the prediction vector output from the GFH-CNN model. Subsequently, any errors in the partitioning results are corrected in accordance with the encoding constraints specified by the VTM, which ultimately determines the final CU block partitioning. The experimental results demonstrate that, in comparison with VTM-10.0, the proposed algorithm achieves a 48.14% reduction in complexity with only a 0.83% increase in bitrate under the top-three configuration, which is negligible. In comparison, the top-two configuration resulted in a higher complexity reduction of 63.78%, although this was accompanied by a 2.08% increase in bitrate. These results demonstrate that, in comparison to existing solutions, our approach provides an optimal balance between encoding efficiency and computational complexity. Full article

Introduction: Fertile soil has a balanced pH and nutrient profile (potassium, phosphorus, and nitrogen), water retention capability, and organic substances. Fertile soil allows for better plant growth, leading to better production. The soil fertility requirements vary from crop to crop. So, it is essential to identify the soil fertility level according to the crop type. Objective: The objective of this paper is to develop a robust model that is capable of predicting the soil fertility. The model is integrated with IoT-generated data and federated learning-based feature selection techniques to improve the accuracy of the dataset. Materials/Methods: Different feature selection techniques were applied to the dataset. Then, we applied machine learning algorithms such as logistic regression, decision tree, and naïve Bayes, as well as their combinations to analyze and improve the performance. The federated learning approach was implemented to train the local models using the individual partitioned datasets. Each local model of the client shared the cryptic output weight and bias without sharing the raw data. There was a centralized model at the server end that collected these weights and biases, preserving data privacy. These collected data were aggregated and applied to find the least square error (LSE). Then, a gradient descent curve (GDC) was applied to identify the optimized weight and bias, which were fed back again to improve the accuracy of the predictions. Result: From our experimental observations, we analyzed the performance metrics of different ML classifiers, and it was revealed that the ensemble of logistic regression and decision tree had a better performance than the other models. One of our client models generates weight and bias with a precision of 87%, an accuracy of 87%, a recall of 87%, and an F1-score of 86%. Further, we collected two of our client system model outcomes from a server model and applied the LSE to identify the optimal W and B. In future work, we wll improve the performance of our model with a recursive approach by verifying the W and B at the client model in a feedback process. Full article

This paper investigates the leader-following $H_{\infty }$ consensus of fractional-order multi-agent systems (FOMASs) under input saturation via the output feedback. Based on the bounded real lemma for FOSs, the sufficient conditions of $H_{\infty }$ consensus for FOMASs are provided in $\alpha \in \left(0,1\right)$ and $\left[1,2\right)$, respectively. Furthermore, the iterative linear matrix inequalities (ILMIs) approaches are applied for solving quadratic matrix inequalities (QMIs). The ILMI algorithms show a method to derive initial values and transform QMIs into LMIs. Mathematical tools are employed to transform the input saturation issue into optimal solutions of LMIs for estimating stable regions. The ILMI algorithms avoid the conditional constraints on matrix variables during the LMIs’ construction and reduce conservatism. The approach does not disassemble the entire MASs by transformations to the Laplacian matrix, instead adopting a holistic analytical perspective to obtain gain matrices. Finally, numerical examples are conducted to validate the efficiency of the approach. Full article

This article investigates the issue of $H_{\infty }$ security output feedback control for a nonlinear networked cascade control system with deception attacks. First, to further reduce the amount of communication data, reasonably schedule network resources, and alleviate the impact of multi-channel deception attacks, an attack-dependent adaptive event-triggered mechanism is introduced into the primary network channel, and its adaptive triggered threshold can be adjusted according to the random attack probability. Secondly, the output dynamic quantization of the secondary network channel is considered. Then, a novel security cascade output feedback controller design framework based on the Takagi–Sugeno (T-S) fuzzy networked cascade control system under deception attacks is established. In addition, by introducing the Lyapunov–Krasovskii stability theory, the design conditions of the controller are given. Finally, the effectiveness and superiority of the proposed design strategies are verified by two simulation examples of power plant boiler–turbine system and power plant boiler power generation control system. Full article

Magnetorheological fluids (MRFs) are functional fluids that exhibit rapid and reproducible rheological responses to external magnetic fields. An MRF has been utilized to develop a haptic device with precise haptic feedback for teleoperative surgical systems. To achieve this, we developed several types of compact MRF clutches for haptics (H-MRCs) and integrated them into a twin-driven MRF actuator (TD-MRA). The first TD-MRA prototype was successfully used to generate fine haptic feedback for operators. However, undesirable torque ripples were observed due to shaft misalignment and the low rigidity of the structure. Additionally, the detailed torque control performance was not evaluated from both static and dynamic current inputs. The objective of this study is to develop a second prototype to reduce torque ripple by improving the structure and evaluating its static and dynamic torque performance. Torque performance was measured using both constant and stepwise current inputs. The coefficient of variance of the torque was successfully reduced by half due to the structural redesign. Although the time constants of the H-MRC were less than 10 ms, those of the TD-MRA were less than 20 ms under all conditions. To address the slower downward output response, we implemented an improved input method, which successfully halved the response time. Full article

Soft exosuits have emerged as potent assistive tools for walking support and rehabilitation training. However, most existing soft exosuit systems rely on preset assistance modes, which may not accurately align with individual physiological states and movement requirements, leading to variable user experiences and efficacy. While existing human-in-the-loop (HIL) research predominantly focuses on optimizing metabolic cost and torque difference parameters, there is a notable absence of real-time monitoring methods that closely reflect the human body’s physiological state and strategies that dynamically indicate walking efficiency. Motivated by this, we developed a novel personalized power-assist system. This system optimizes the power-assist output of the hip joint by monitoring the user’s physiological and motion signals in real time, including heart rate (HR), blood oxygen saturation (SpO₂), and inertial measurement unit (IMU) data, to assist hip flexion based on feedback. The findings from a metabolic expenditure trial demonstrate that the innovative soft exosuit, which is based on a Physiological State Monitoring Control (PSMC) system, achieves a reduction of 7.81% in metabolic expenditure during treadmill walking at a speed of 3.5 km/h compared to walking without the assistance of the exosuit. Additionally, during continuous exercise with varying intensities, the metabolic consumption level is reduced by 5.1%, 5.8%, and 8.2% at speeds of 2, 4, and 6 km per hour, respectively. These results support the design of a novel hip flexion-assisting soft exosuit, demonstrating that applying different assistance forces in consideration of different physiological states is a reasonable approach to reducing metabolic consumption. Full article

This paper studies the output feedback $H\infty$ control problem of event-triggered Markov-type networked control systems. Firstly, a new Lyapunov–Krasovskii functional is constructed, which contains an event-triggered scheme, Markovian jump system, and quantified information. Secondly, the upper bound of the weak infinitesimal generation operator of the Lyapunov–Krasovskii function is estimated by combining Wirtinger’s-based integral inequality and reciprocally convex inequality. Finally, based on the Lyapunov stability theory, the closed-loop stability criterion of event-triggered Markov-type networked control systems and the design method of the output feedback $H\infty$ controller for the disturbance attenuation level $\gamma$ are given in the terms of linear matrix inequalities. The effectiveness and superiority of the proposed method are verified using three numerical examples. Full article

We experimentally investigate the dynamics characteristics of a two-state quantum dot laser (TSQDL) subject to optical feedback. Firstly, we inspect the impact of the temperature on the power-current characteristics of the ground state (GS) lasing and the excited state (ES) lasing in the TSQDL operating at free-running. The results demonstrate that with the decrease in the temperature, the threshold current for GS lasing ($I_{th}^{GS}$) and the threshold current for ES lasing ($I_{th}^{ES}$) decrease very slowly. There exists a current for GS quenching ($I_Q^{GS}$), which is gradually increased with the decrease in the temperature. After introducing optical feedback, the overall trend of change is similar to those obtained under free-running. Next, through inspecting the time series and power spectrum of the output from the TSQDL under optical feedback, the dynamical characteristics of the TSQDL are investigated under different feedback ratios, and diverse dynamical states including quasi-chaos pulse package, chaos state, regular pulse package, quasi-period two, quasi-regular pulsing, and chaos regular pulse package have been observed. Finally, for the TSQDL biased at three different cases: lower than $I_{th}^{ES}$, slightly higher than $I_{th}^{ES}$, and higher than $I_{th}^{ES}$, nonlinear dynamic state evolutions with the increase in feedback ratio are inspected, respectively. The results show that, for the TSQDL biased at lower than $I_{th}^{ES}$, it presents an evolution route of stable state—quasi-chaos pulse package—chaos state—regular pulse package. For the TSQDL biased at slightly larger than $I_{th}^{ES}$, it presents an evolution route of stable state—quasi-regular pulsing—quasi-period two—chaos regular pulse package. For the TSQDL biased at higher than $I_{th}^{ES}$, the TSQDL always behaves stable state within the range of feedback ratio that the experiment can achieve. However, with the increase in optical feedback ratio, the number of longitudinal modes for GS lasing and ES lasing are changed. Full article