Reference Signal Research Articles

Observer-based proportional-retarded controller for payload swing attenuation of 2D-crane systems including load hoisting-lowering

Crane systems are essential systems utilized in industry and for research. Nevertheless, they are always affected by endogenous and exogenous disturbances, which may generate undesirable payload oscillations, compromising people’s security and the system itself. Thus, to deal with these issues and control these mechatronic systems efficiently, this manuscript develops a novel robust observer-based proportional-retarded controller for perturbed two-dimensional cranes, considering variation in the rope length. This novel scheme makes the trolley follow a desired reference signal while reducing the payload variations. The controller structure allows for compensating disturbances, while a new control approach introduces artificial delays that stabilize the closed-loop system and attain the desired control objective. A formal theoretical analysis demonstrates the validity of the new proposal. Then, experimental results show the outstanding performance of the proposed control scheme and its superior performance against other methodologies from the literature.

Hidden Markov models for predicting cell-level mobile networks performance degradation

The ubiquitous deployment of Fourth-generation (4G) and Fifth-generation (5G) networks, while expanding user connectivity over a larger geographic area, presents significant challenges for mobile network management. The surge in connected devices increase cell congestion, while user mobility, fading, and interference further deteriorate channel quality. The combined effect of these factors contribute to a gradual degradation in cell and network performance, straining the capabilities of Mobile Network Operators (MNOs) to maintain optimal service quality. The degradation progresses through distinct stages, and ultimately leading to cell outages. Existing cell degradation detection methods, such as key performance indicators (KPIs) monitoring and drive tests, rely on indicators such as signal strength (like reference signal received power (RSRP)), network measurements (like traffic volume and handover requests), and user complaints. However, these methods may not provide real-time insights, leading to delays in identifying and addressing performance issues. This paper proposes a Hidden Markov Model (HMM) approach for proactive cell degradation prediction using real-time time-series KPIs data collected from a telecom operator’s network management system. The NetMax system, a network management system specifically designed for ZTE networks, was used for KPIs data collection. RSRP, 3GPP-recommended signal strength metrics for degradation measure, defines hidden states, while traffic volume and handover requests represent the observable states. The HMM achieves an average prediction accuracy of 93.12%, F1 score of 91.81%, and precision of 92.82% with 23 observation lengths, demonstrating the models’s effectiveness in predicting mobile network’s cell degradation severity.

Comparing neural network architectures for non-intrusive speech quality prediction

Non-intrusive speech quality predictors evaluate speech quality without the use of a reference signal, making them useful in many practical applications. Recently, neural networks have shown the best performance for this task. Two such models in the literature are the convolutional neural network based DNSMOS and the bi-directional long short-term memory based Quality-Net, which were originally trained to predict subjective targets and intrusive PESQ scores, respectively. In this paper, these two architectures are trained on a single dataset, and used to predict the intrusive ViSQOL score. The evaluation is done on a number of test sets with a variety of mismatch conditions, including unseen speech and noise corpora, and common voice over IP distortions. The experiments show that the models achieve similar predictive ability on the training distribution, and overall good generalization to new noise and speech corpora. Unseen distortions are identified as an area where both models generalize poorly, especially DNSMOS. Our results also suggest that a pervasiveness of ambient noise in the training set can cause problems when generalizing to certain types of noise. Finally, we detail how the ViSQOL score can have undesirable dependencies on the reference pressure level and the voice activity level.

Model-free aperiodic tracking for discrete-time systems using hierarchical reinforcement learning

In the operation of practical systems, it is often a challenging task to deal with limited knowledge of the system dynamics, therefore, it is needed to obtain a framework for the simultaneous learning and control of dynamic systems, able to cope with uncertain dynamics. This paper proposes a model-free aperiodic tracking control method based on MAXQ hierarchical reinforcement learning for unknown dynamics in discrete-time systems. Firstly, a MAXQ hierarchical framework is developed for aperiodic tracking control that decomposes the task into pre-control and accurate control subtasks. The former achieves sub-optimal tracking which prompts the implementation of accurate control. The latter implements aperiodic tracking based on the pre-control result to reduce resource occupation. The structure of the MAXQ framework simplifies the complexity of the tracking task, and the incorporation of pre-control accelerates the learning process in the formal control stage. Secondly, considering the disparities between the control inputs and the control update instants, pre-control is decomposed to learn the triggering strategy and control policy, respectively. Meanwhile, the control policy learns optimal control inputs by minimizing the cost function. Similarly, the triggering strategy and control policy are also separately learned in accurate control. In contrast to traditional aperiodic triggering mechanisms, in accurate control stage, the triggering strategy is developed based on the cumulative error of the pre-control result, thus avoiding the influence of accidental factors and the cumulative effects of errors, enhancing system robustness. Thirdly, the proposed tracking control is derived by using only the input, output, and reference signal data from the system, without relying on system dynamics. It is applicable to both linear and nonlinear systems, demonstrating strong generalizability. Finally, simulation examples are provided to validate the effectiveness and superiority of the proposed method.

Multi-Sensory Tool Holder for Process Force Monitoring and Chatter Detection in Milling.

Sensor-based monitoring of process and tool condition in milling is a key technology for improving productivity and workpiece quality, as well as enabling automation of machine tools. However, industrial implementation of such monitoring systems remains a difficult task, since they require high sensitivity and minimal impact on CNC machines and cutting conditions. This paper presents a novel multi-sensory tool holder for measurement of process forces and vibrations in direct proximity to the cutting tool. In particular, the sensor system has an integrated temperature sensor, a triaxial accelerometer and strain gauges for measurement of axial force and bending moment. It is equipped with a self-sufficient electric generator and wireless data transmission, allowing for a tool holder design without interfering contours. Milling and drilling experiments with varying cutting parameters are conducted. The measurement data are analyzed, pre-processed and verified with reference signals. Furthermore, the suitability of all integrated sensors for detection of dynamic instabilities (chatter) is investigated, showing that bending moment and tangential acceleration signals are the most sensitive regarding this monitoring task.

Preview Control for Cyber–Physical Systems under Periodic Denial-of-Service Attacks

In this paper, the preview control problem for cyber–physical systems (CPSs) under denial-of-service (DOS) attacks is studied. First, we employ an attack-tolerant strategy to design an augmented error system (AES) for scenarios where both state and reference signal channels are subject to periodic attacks. We then discuss the stochastic stability conditions for the AES and derive the corresponding controller. Subsequently, the preview controller for the original system is developed. Finally, the effectiveness of the obtained results is demonstrated through a numerical simulation using an unmanned ground vehicle (UGV) model, indicating the practical applicability of the proposed control strategy.

A high-fidelity lithium-ion battery emulator for electric vehicle application

Lithium-ion (Li-ion) battery has played a key role for the development of electric vehicle (EV) at present, while the Li-ion batteries in the market come from different manufactures. Verifying the performance of the battery management system (BMS) for various battery chemistries is a complex undertaking. This paper proposes a high-fidelity Li-ion battery emulator for EV applications. The emulator utilizes a battery model parameterized by a series of performance tests and a special-designed hardware platform. A three-order battery equivalent circuit model (ECM) is selected to provide the voltage and current reference signal in the processor. Subsequently, the hardware generates the high voltage and current signal in accordance with the reference. To ensure the high accuracy of the battery ECM, a 37 Ah nickel manganese cobalt (NMC) battery was selected for testing under both charge and discharge conditions, as well as across a temperature range of − 30℃ to 45℃. The battery emulator is verified on charge and discharge mode for both accuracy and dynamic performance validations.

A Keyphasor-Free Traveling Waves Analysis Method Based on Blade Tip Timing and Experimental Validation

Abstract Blade Tip Timing (BTT) emerges as a non-intrusive, promising method for assessing blade vibration. Despite its potential, BTT's inherent measurement limitations often result in the under-sampling of individual blade data. Assuming that the bladed-disk assembly vibrates in nodal diameter modes, traveling wave analysis (TWA) can overcome the limitations of under-sampling and identify the nodal diameter modes. Traditional TWA methods rely on a keyphasor or once-per-revolution (OPR) sensor to provide shaft reference signals. However, such a sensor could be damaged during operation or challenging to install due to limited space. In this paper, a new keyphasor-free TWA (KF-TWA) method is introduced, which is capable of measuring the order and nodal diameter of nodal diameter vibrations with as few as two sensors. Theoretical derivations reveal the method's resilience to the steady-state displacement of blades. The feasibility and effectiveness of the KF-TWA method are validated through numerical experiments and compressor tests. In the numerical experiments, blade detuning and nodal diameter vibration responses are simulated, leading to the development of a criterion for identifying nodal diameter vibrations. A compressor test rig, equipped with a 64-keyphasor disk and a sufficient number of circumferentially distributed sensors, is designed to provide blade vibration benchmark. In the experiments, the KF-TWA method successfully identified nodal diameter vibrations with amplitudes as low as 0.014 mm, consistently yielding identical results across multiple sensor pairs, which demonstrates a higher robustness compared to the traditional TWA method.

Research on dynamic blood oxygen saturation measurement based on motion noise reconstruction combined with convex combination least mean square adaptive filter

The performance of a pulse oximeter based on photoelectric detection is greatly affected by motion noise (MA) in the photoplethysmographic (PPG) signal. This paper presents an algorithm for detecting motion oxygen saturation, which reconstructs a motion noise reference signal using ensemble of complete adaptive noise and empirical mode decomposition combined with multi-scale permutation entropy, and eliminates MA in the PPG signal using a convex combination least mean square adaptive filters to calculate dynamic oxygen saturation. The test results show that, under simulated walking and jogging conditions, the mean absolute error (MAE) of oxygen saturation estimated by the proposed algorithm and the reference oxygen saturation are 0.05 and 0.07, respectively, with means absolute percentage error (MAPE) of 0.05% and 0.07%, respectively. The overall Pearson correlation coefficient reaches 0.971 2. The proposed scheme effectively reduces motion artifacts in the corrupted PPG signal and is expected to be applied in portable photoelectric pulse oximeters to improve the accuracy of dynamic oxygen saturation measurement.

A mediator and spatial-resolution induced dual-ratiometric self-powered photoelectrochemical aptasensor for accurate detection of antibiotics

This work reports a dual-ratiometric self-powered photoelectrochemical sensor (SPPS) by the combination of the photocathode with potassium persulfate (K2S2O8) as mediator and the photoanode with spatial-resolution technique. The key point is to utilize the activation and conversion of K2S2O8 as a mediator to generate two power peaks (PMax1 and PMax2) simultaneously. Spatial-resolution technique offers reference signals (PMax3 and PMax4) for two power peaks, respectively. With chloramphenicol (CAP) as model analytes, a dual-ratiometric SPPS was developed with aptamer as recognition unit. Briefly, the specific capture of CAP increased the power peaks gradually to produce sensing signals which is capable of quantifying CAP. The sensor can provide two sets of sensing signals related to the CAP concentration, i.e., PMax1/PMax3 (R = 0.997) and PMax2/PMax4 (R = 0.999) with a detection limit (S/N = 3) of 0.29 pM. Compared with single-signal or single-ratiometric SPPS, the dual-ratiometric SPPS exhibits improved analytical performances, proposing a feasible way to realize self-checking and anti-interference.

A ratiometric fluorescence sensor with different responsive modes based on carbon dots-embedded Tb-MOFs for the determination of norfloxacin and levofloxacin

Norfloxacin (NOR) and levofloxacin (LEV) are the two most frequently used fluoroquinolones (FQs) in clinic. Their residues seriously endanger the ecosystem and human health. Due to their similarity in structure and properties, it is urgent to develop an efficient and sensitive strategy for detection and differentiation. Herein, we synthesized a novel ratiometric fluorescent sensor for the first time by combining N, S co-doped carbon dots (CDs) and the precursors of Tb-MOFs through a facile one-pot method. The introduction of CDs effectively facilitated the energy transfer between Tb3+ and FQs, overcoming the limitation that single Tb-MOFs could not identify similar antibiotics. Specifically, the presence of NOR resulted in reverse signal response through the inner filter effect and antenna effect. The synergistic effect of these two mechanisms contributed to achieving signal amplification accompanied by a distinguishable color transition. The limit of detection (LOD) was 0.036 μM. Different from NOR, the addition of LEV reduced the electron density of the system, weakened the coordination ability of Tb3+ with LEV, and induced a single signal response with Tb3+ fluorescence intensity as a reference signal (LOD = 0.383 μM). Furthermore, the method proved to be rapid and visual, allowing for the straightforward analysis of FQs residues in water, food matrices, and biological samples with satisfactory precision. By integrating N, S-CDs@Tb-MOFs with flexible substrates, the paper-based sensor facilitated the visual quantitative determination of FQs by reading RGB values. The developed sensor presents a promising strategy for the identification and real-time monitoring of antibiotics.

Comparison of Advanced Multivariable Control Techniques for Axial-Piston Pump

This article is devoted to a comparison of two advanced control techniques applied to the same plant. The plant is a certain type of axial-piston pump. A linear-quadratic (LQR) controller and an H-infinity (H∞) controller were synthesized to regulate the displacement volume of the pump. The classical solution to such a problem is to use a hydro-mechanical controller (by pressure, flow rate, or power) but, in the available sources, there are solutions that implement proportional-integral-derivative (PID), LQR, model predictive control (MPC), etc. Unlike a classical solution, in our case, the hydro-mechanical controller is replaced by an electro-hydraulic proportional valve, which receives a reference signal from an industrial microcontroller. It is used as the actuator of the pump swash plate. The pump swash plate swivel angle determines the displacement volume, respectively, and the flow rate of the pump. The microcontroller is capable of embedding various control algorithms with different structures and complexities. The developed LQR and H∞ controllers are compared in the simulation and real experiment conditions. For this purpose, the authors have developed a laboratory experimental test bench, enabling a real-time function of the control system via USB/CAN communication. Both controllers are compared under different pump loading modes. Also, this paper contributes an uncertain model of an axial-piston pump with proportional valve control that is obtained from experimental data. Based on this model, the robust stability of the closed-loop system is investigated by comparing the structure of a singular value (μ). The investigations show that both control systems achieved robust stability. Moreover, they can tolerate up to four times larger uncertainties than modeled ones. The system with the H∞ controller attenuates approximately at least 30 times the disturbances with frequency up to 1 rad/s while the system with the LQR controller attenuates at least 10 times the same disturbances.

Cupric ion coordination-mediated molecularly imprinted electrochemical sensor for the recognition and ratiometric detection of lidocaine

Molecularly imprinted polymers (MIPs) have been widely used as artificial recognition elements in sensing applications. However, their electrochemical sensing performance is generally hampered by limited affinity and uncontrolled condition change. In this work, a novel MIP electrochemical sensor based on metal coordination interaction was prepared and used for the recognition and ratiometric detection of lidocaine (LC). The sensor was constructed by electrodepositing Cu-coordinated MIP on biomass carbon modified glassy carbon electrode. Herein, Cu2+ ions acted as anchor for the immobilization of LC during the synthesis process, enabling the orderly formation of molecular recognition sites. Reversely, the metal coordination between Cu2+ ions and LC molecules facilitated the recognition of LC. Moreover, the doped cupric ions in the polymer film could provide a reference signal for subsequent ratiometric strategy. Thus the resulting sensor exhibited high selectivity, sensitivity, satisfactory reproducibility, and anti-interference ability. Under the selected conditions, the peak current ratio of LC and cupric ion was linear to LC concentration in the range of 0.008–2.5 μmol L−1 (R2 = 0.9951), and the limit of detection was 1.9 nmol L−1 (S/N = 3). The practical feasibility of the sensor was evaluated by detecting human serum and pharmaceutical samples, and satisfactory outcomes were obtained.

Self-organizing hypercomplex-valued adaptive network

A novel, unsupervised, artificial intelligence system is presented, whose input signals and trainable weights consist of complex or hypercomplex values. The system uses the effect given by the complex multiplication that the multiplicand is not only scaled but also rotated. The more similar an input signal and the reference signal are, the more likely the input signal belongs to the corresponding class. The data assigned to a class during training is stored on a generic layer as well as on a layer extracting special features of the signal. As a result, the same cluster can hold a general description and the details of the signal. This property is vital for assigning a signal to an existing or a new class. To ensure that only valid new classes are opened, the system determines the variances by comparing each input signal component with the weights and adaptively adjusts its activation and threshold functions for an optimal classification decision. The presented system knows at any time all boundaries of its clusters. Experimentally, it is demonstrated that the system is able to cluster the data of multiple classes autonomously, fast, and with high accuracy.

Observer-based reinforcement learning for optimal fault-tolerant consensus control of nonlinear multi-agent systems via a dynamic event-triggered mechanism

In this article, an adaptive optimized consensus tracking control problem is studied for nonlinear strict-feedback dynamic multi-agent systems (MASs), considering both unmeasurable system states and time-varying bias faults. By utilizing the backstepping technique, we develop an adaptive reinforcement learning (RL) algorithm within the observer-critic-actor architecture, specially designed to compensate for the lack of state information and derive control inputs, thereby achieving approximate optimal control. Moreover, an event-triggered mechanism is introduced in the sensor-to-controller channel, which dynamically adjusts the triggering threshold online and employs event-sampled states to initiate control actions. To address discontinuities caused by state triggering, we construct virtual controllers that continuously sample state signals and reconfigure the actual controller based on previously triggered states. The outputs of the MASs are shown to accurately track the desired reference signals while ensuring the boundedness of all closed-loop signals. Additionally, the proposed controller is verified to be devoid of Zeno behavior. Finally, the effectiveness of our control methodology is demonstrated through numerical simulation.

K-filter-based distributed adaptive bipartite tracking for nonlinear multi-agent systems with unknown control direction

This paper investigates K-filter-based distributed adaptive output-feedback bipartite tracking problem for multi-agent systems with unknown control direction. The agents are partitioned into two groups, where agents belonging to the same group are cooperative while agents belonging to the distinct group are competitive. Firstly, the unmeasurable state variables of the system under unknown control direction are estimated through K-filters. Then, a neighbourhood error is introduced to transform a structurally balanced network into a cooperative network. Based on K-filters, neighbourhood error, Nussbaum-type functions and dynamic surface control, a distributed adaptive output-feedback control protocol is designed to achieve the bipartite tracking. It is shown that all signals in the resulting closed-loop systems are semi-global bounded and the output of each agent can track the reference signal provided that the network is structurally balanced and contains a directed spanning tree. Finally, a numerical example is given to verify the effectiveness of the proposed method.

Optimization of electrolyte‐insulator‐semiconductor capacitor sensor by differential design

AbstractAn electrolyte‐insulator‐semiconductor capacitor (EISCAP) sensor with the unique differential design is proposed where a liquid‐free reference (LR) device is used as the reference signal source. After the output matching based on AC excitation adjustment, LR device can provide up to 80 % elimination efficiency of background signal for differential EISCAP sensor. Experiment results of pH sensing demonstrate that the differential EISCAP sensor has the better accuracy and time stability compared with conventional EISCAP sensor. The differential design provides a feasible optimization direction for EISCAP sensor in monitoring applications.

Dspace implementation of real-time selective harmonics elimination technique using modified carrier on three phase inverter

Introduction. In the contemporary world, alternative electrical energy has become an integral part of our daily existence, with the majority of our electrical materials, electronic devices, and industrial equipment relying on this energy source. Consequently, ensuring the quality of the electrical signal obtained is of paramount importance in the process of converting and distributing electrical energy. The improvement of the output voltage inverter can be achieved through adjustments to the inverter structure or by refining the control strategy. The novelty of the presented research lies in an innovative approach that employs real-time modulation for efficient control over the reduction of harmonics, alongside managing the fundamental component. This approach is applicable to both bipolar and unipolar configurations, featuring quarter-wave and half-wave symmetries. Purpose. Employing this modulation strategy aims to enhance the durability of the switching components and enhance the voltage output of the inverter. Methods. The methodology is founded on a sine-sine modulation as its foundational model. It constitutes an inventive pulse width modulation technique in which a reference sinusoidal waveform, operating at the desired signal frequency, is compared to a modified carrier signal with an identical time period as the reference signal. Results. This paper introduces a broader and more comprehensive approach, alongside specific solutions, for the control and mitigation of harmonics in three phase voltage source inverters. The proposed method offers precise control over the reduction of harmonics and the fundamental component, and it can be implemented in extensive power electronic converters. Practical value. To assess the effectiveness of the given control approach, we conducted simulations as well as real-time implementation employing Dspace DS1104 controller board. The outcomes were highly favorable, confirming the effectiveness and validity of the suggested control algorithm. References 15, tables 4, figures 7.

Design of solar and energy storage systems fed reduced switch multilevel converter with flower pollination optimization

In the present situation, the growing use of power electronic devices and non-linear loads has led to power quality (PQ) issues, including harmonics and poor power factor, which adversely affect the distribution network. This study contributes a design of shunt active power filter, powered by solar energy and energy storage systems, to address these PQ issues. To minimize losses, a five-level reduced-switch voltage source converter has been considered. Additionally, a neural network-based reference signal generation method is used, eliminating the need for conventional synchronous reference frame and active-reactive power theories, along with their complex abc and αβ0 transformations. This work also includes the optimal selection of the shunt filter and the gain parameters for the proportional-integral-derivative (PID) controller used in the shunt and battery control system. These parameters, along with the weights and biases of the neural network, are optimally determined using a nature-inspired flower pollination optimization algorithm.The proposed system has three primary objectives: (1) stabilizing the voltage across the DC bus capacitor, (2) reducing total harmonic distortion (THD) and improving the power factor (PF), and (3) ensuring the power management under the varying irradiation and load conditions. The effectiveness of the proposed system is evaluated through three testing scenarios, with results compared to conventional SRF and pq methods using a proportional-integral controller (PIC). The analysis reveals that the THD for the case studies is 3.32 %, 2.93 %, and 3.98 %, significantly lower than the other techniques compared. Additionally, the PF is nearly at unity, with a lower settling time of 0.05 s for the DC bus voltage.

Learning to use landmarks for navigation amplifies their representation in retrosplenial cortex.

Visual landmarks provide powerful reference signals for efficient navigation by altering the activity of spatially tuned neurons, such as place cells, head direction cells, and grid cells. To understand the neural mechanism by which landmarks exert such strong influence, it is necessary to identify how these visual features gain spatial meaning. In this study, we characterized visual landmark representations in mouse retrosplenial cortex (RSC) using chronic two-photon imaging of the same neuronal ensembles over the course of spatial learning. We found a pronounced increase in landmark-referenced activity in RSC neurons that, once established, remained stable across days. Changing behavioral context by uncoupling treadmill motion from visual feedback systematically altered neuronal responses associated with the coherence between visual scene flow speed and self-motion. To explore potential underlying mechanisms, we modeled how burst firing, mediated by supralinear somatodendritic interactions, could efficiently mediate context- and coherence-dependent integration of landmark information. Our results show that visual encoding shifts to landmark-referenced and context-dependent codes as these cues take on spatial meaning during learning.