Tracking Ability Research Articles

A Novel Joint Estimation Method of State of Charge and State of Health Based on the Strong Tracking-Dual Adaptive Extended Kalman Filter Algorithm for the Electric Vehicle Lithium-Ion Batteries

In order to enhance the efficiency of electric vehicle lithium-ion batteries, accurate estimation of the battery state is essential. To solve the problems of system noise statistical uncertainty and battery model inaccuracy when using the extended Kalman filter (EKF) algorithm to estimate the battery state, a novel joint estimation algorithm of SOC and SOH based on the strong tracking-dual adaptive extended Kalman filter (ST-DAEKF) is proposed. Based on the extended Kalman filtering algorithm, the fading factor is introduced into it to enhance the tracking ability. Meanwhile, the adaptive filter which can statistics the characteristics of time-varying noise is used to adjust the noise parameters of the system. The BBDST condition and the DST condition at 25 °C are used for simulation and verification in MATLAB. The results of the algorithm simulation show that under the BBDST condition, the maximum SOC error and the average error of the proposed algorithm are 3.41% and 0.99%, respectively, with the corresponding convergence time of 15 seconds. And under the DST condition, the corresponding data is 1.56%, 1.29%, and 20 seconds, respectively. At the same time, compared with the extended Kalman algorithm, the SOH estimation results of this algorithm also have a better estimation effect and reference value. Under the BBDST condition, the maximum SOH error and average error under this algorithm are 0.12% and 0.06%, with the corresponding data of 0.66% and 0.23% under the DST condition. The above data proves the superiority of the joint estimation algorithm.

Synchronous Control Characteristics Analysis of Shield Propulsion Hydraulic System Based on Tracking Differentiator and Self-Adaptive Nonlinear PID

Aiming at the low precision problem of multi-cylinder cooperative propulsion control in different regions of shield propulsion hydraulic systems under conditions of large load changes, this paper proposes a tracking differentiator and self-adaptive nonlinear PID (TD-NPID) control method to improve the synchronous control characteristics of shield propulsion hydraulic systems. First, the working principles of shield propulsion hydraulic systems were analyzed, and a mathematical model and TD-NPID controller were developed. Then, a simulation model was developed in AMESim-MATLAB environment, and the synchronous dynamic performances of fuzzy PID control, conventional PID control, and TD-NPID control were compared and analyzed. The results demonstrated that the shield propulsion hydraulic system with TD-NPID control had better servo tracking ability and steady-state performance than the systems with fuzzy or conventional PID control, which verified the feasibility of the application of TD-NPID control for the synchronous control of shield propulsion hydraulic systems.

Decentralized Phase Shedding with Low Power Mode for Multiphase Converter

With a multiphase converter, the phase-shedding function dedicated to maximizing the power efficiency, in a manner that is dependent on the load current, is always provided by a centralized controller that induces a Single Point of Failure (SPOF). The objective of this study is to obtain a decentralized control approach to implement this function by removing any SPOF. The method consists of using identical local controllers, each associated with a converter phase, that communicate with each other in a daisy-chain structure. Instead of measuring the global output current to determine the optimal number of active phases required, each local controller measures its own leg current and takes a local decision based on threshold crossing management and inter-controller communications. Functional simulations are carried out on a 5-leg 12 V/1.2 V 60 W multiphase converter supplying a modern microcontroller. They demonstrate that the number of active phases is well adjusted, in a dynamic manner, depending on the load current level. Specific events such as load current inrush or the start-up sequence are analyzed to guarantee optimal transient responses. A maximum power efficiency tracking ability is also demonstrated. Finally, it is shown that this control strategy allows phase shedding to be implemented using as many phases as desired, in a modular manner, thereby avoiding any centralized processing.

Frequency tracking control of the WPT system based on fuzzy RBF neural network

With the application of electrical equipment, magnetically coupled resonant (MCR) wireless power transfer (WPT) technology has become an effective means to improve equipment intelligence. The MCR-WPT system is a loosely coupled system, and the resonant frequency may be split or detuned due to the changes of load or transferring distance, resulting in the system transfer efficiency (TE) greatly reduced. To solve the problems of limited speed and accuracy in the existing frequency tracking methods, this paper analyzes the relation between the detuning rate and the system TE, proposing an adaptive frequency tracking control method based on fuzzy radial basis function neural network control. The neural network outputs proportion–integration–differentiation parameters to adjust the inverter drive circuit, and the frequency of inverter drive circuit is adjusted nonlinearly in real time to ensure the accurate frequency tracking of the MCR-WPT system. The simulation and experimental results show that the proposed method can enhance the tracking ability of the resonant frequency, and effectively improve the system TE.

Self-adjusting force/bit blending control based on quantitative factor-scale factor fuzzy-PID bit control

In order to improve the operation performance and environmental adaptability of the manipulator, it is essential to control the position and force of the manipulator simultaneously. This paper proposed a hybrid control method of auto-dynamic bit based on bit control to realize flexible grasping of manipulator in unknown environment. The constraint conditions and control types of the hybrid force/bit control are analyzed firstly, and the dynamic model when contacting with the environment is established. Then, an improved position controller based on conventional fuzzy-PID control strategy is introduced to realize the hybrid force/bit control based on the position control by combining constraint estimation method and impedance force control. Finally, the control performance of the force/bit hybrid controller is verified by simulation experiments. The results show that the designed control strategy has relatively ideal force control precision and higher trajectory tracking ability, and the manipulator model has better compliance.

A new recognition moiety diphenylborinate in the detection of pyruvate via Lewis acid/base sensing pathway and its bioimaging applications

Developing new reaction based recognizing units can enhance the specificity of target analyte molecules in practical applications of real samples and biosystems. Therefore, introducing a recognizing moiety diphenylborinate was encountered for the detection of pyruvate biomolecule through Lewis acid-base reaction based sensing strategy. The construction of the Schiff-base back bone between quinoline and N-(diethylamino)salicylaldehyde-diphenylborinate (QSB) were expressed the red shift from blue emission of quinoline in to green as that of dative bond developed between Schiff base nitrogen and boron atoms. The new sensing approach was involved such a way that fluorophore QSB is a Lewis acid while pyruvate acts as Lewis base, where the elimination of Lewis pair produced a weak green fluorescence including the formation of quinoline, N-(diethylamino)salicylaldehyde (QS). The switching products were witnessed through 1H NMR titration, HR-MS and FT-IR studies. The good selectivity and interference ability were achieved in presence of 1000-fold excess of possible interferences with LOD of 16 nM. Moreover, the tracking ability of the probe was employed towards pyruvate in live HeLa cell imaging for evaluating an exogenous and endogenous signals producing ability and its mitochondria targeting property was investigated successfully. Further, the practical utility of the probe was tested with milk samples and obtained good recovery results.

Asymptotic properties of the OBE parameter estimation algorithm under diffuse initialization

The parameters estimation problem of autoregressive with exogenous inputs models is considered. Our approach is based on the study of the optimal bounding ellipsoid (OBE) recurrent algorithm properties. It is assumed that the shape matrix of the initial ellipsoid in the OBE algorithm is proportional to a large positive parameter. Asymptotic expansions of the relations that describe the OBE algorithm are obtained. The limit recursive algorithm (diffuse) not depending on a large parameter which may lead the OBE algorithm to a divergence is proposed and explored. The robust properties of the proposed algorithm are illustrated by a numerical example. By simulation, it is shown that it may provide better the tracking ability compared to the diffuse least-squares (RLS) algorithm and the diffuse RLS algorithm with the sliding window.

E-TLD: Event-Based Framework for Dynamic Object Tracking

This paper presents a long-term object tracking framework with a moving event camera under general tracking conditions. A first of its kind for these revolutionary cameras, the tracking framework uses a discriminative representation for the object with online learning, and detects and re-tracks the object when it comes back into the field-of-view. One of the key novelties is the use of an event-based <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">local sliding window</i> technique that tracks reliably in scenes with cluttered and textured background. In addition, Bayesian bootstrapping is used to assist real-time processing and boost the discriminative power of the object representation. On the other hand, when the object re-enters the field-of-view of the camera, a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">data-driven, global sliding window</i> detector locates the object for subsequent tracking. Extensive experiments demonstrate the ability of the proposed framework to track and detect arbitrary objects of various shapes and sizes, including dynamic objects such as a human. This is a significant improvement compared to earlier works that simply track objects as long as they are visible under simpler background settings. Using the ground truth locations for five different objects under three motion settings, namely translation, rotation and 6-DOF, quantitative measurement is reported for the event-based tracking framework with critical insights on various performance issues. Finally, real-time implementation in C++ highlights tracking ability under scale, rotation, view-point and occlusion scenarios in a lab setting.

Likelihood-Based Selection Radius Directed Equalizer With Time-Multiplexed Pilot Symbols for Probabilistically Shaped QAM

Probabilistically shaped (PS) quadrature amplitude modulation (QAM) recently established itself as the solution to adopt for state-of-the-art coherent transponders. This in turn has drawn attention to the rework of conventional digital signal processing (DSP) algorithms, which are not optimal for QAM-PS. In this context, we propose a novel pilot-aided equalization technique based on the ubiquitous radius directed equalizer (RDE). Our solution employs both payload and time-multiplexed pilot symbols for the adaptive filters update. The payload symbols are treated on the basis of the likelihood of their correct blind assignment. We show that this approach is at the same time able to reduce the global pilot overhead (POH) required by the DSP chain and to provide greatly improved performance in the tracking ability of the equalizer in the event of fast state of polarization rotations. We describe a simulation environment in which we model accurately static and dynamic polarization-related impairments. We test our algorithm over different shaped modulation formats and in the presence of transceiver impairments and demonstrate its superior performance with respect to standard feed-forward implementations in all the scenarios considered.

Modeling and Design of an Aircraft-Mode Controller for a Fixed-Wing VTOL UAV

Vertical takeoff and landing (VTOL) is an essential feature of unmanned aerial vehicles (UAVs). On the one hand, VTOL can expand and enhance the applications of UAVs; yet, on the other hand, it makes the design of control systems for UAVs more complicated. The most challenging demand in designing the control system is to achieve satisfactory response sharpness of fixed-wing UAVs to control commands and ensure that the aircraft mode channels are effectively decoupled. In this work, a six-degree-of-freedom (6-DoF) model with forces and moments is established based on the aerodynamic analysis, which is carried out through computational fluid dynamics (CFD) numerical simulation. The improved proportional derivative (PD) controller based on the extended state observer (ESO) is proposed to design the inner-loop attitude control, which increases the anti-interference ability for internal and external uncertainty of the UAV system. The motion equations of the UAV are established and divided into independent components of longitudinal and lateral motion to design the outer loop control law under minor disturbance conditions. A total energy control system (TECS) for the longitudinal height channel is proposed, which separates speed control and track control. L1 nonlinear path tracking guidance algorithm is used for lateral trajectory tracking so as to improve curve tracking ability and wind resistance. Effectiveness of this approach is proved by actual flight experiment data. Finally, a controller based on angular velocity control is designed to prevent the attitude and head reference system (AHRS) from malfunctioning. Its effectiveness is verified by the response test of the control system.

An adaptive approach to machine learning for compact particle accelerators

Machine learning (ML) tools are able to learn relationships between the inputs and outputs of large complex systems directly from data. However, for time-varying systems, the predictive capabilities of ML tools degrade if the systems are no longer accurately represented by the data with which the ML models were trained. For complex systems, re-training is only possible if the changes are slow relative to the rate at which large numbers of new input-output training data can be non-invasively recorded. In this work, we present an approach to deep learning for time-varying systems that does not require re-training, but uses instead an adaptive feedback in the architecture of deep convolutional neural networks (CNN). The feedback is based only on available system output measurements and is applied in the encoded low-dimensional dense layers of the encoder-decoder CNNs. First, we develop an inverse model of a complex accelerator system to map output beam measurements to input beam distributions, while both the accelerator components and the unknown input beam distribution vary rapidly with time. We then demonstrate our method on experimental measurements of the input and output beam distributions of the HiRES ultra-fast electron diffraction (UED) beam line at Lawrence Berkeley National Laboratory, and showcase its ability for automatic tracking of the time varying photocathode quantum efficiency map. Our method can be successfully used to aid both physics and ML-based surrogate online models to provide non-invasive beam diagnostics.

Estimating Tukey depth using incremental quantile estimators

Measures of distance or how data points are positioned relative to each other are fundamental in pattern recognition. The concept of depth measures how deep an arbitrary point is positioned in a dataset, and is an interesting concept in this regard. However, while this concept has received a lot of attention in the statistical literature, its application within pattern recognition is still limited.To increase the applicability of the depth concept in pattern recognition, we address the well-known computational challenges associated with the depth concept, by suggesting to estimate depth using incremental quantile estimators. The suggested algorithm can not only estimate depth when the dataset is known in advance, but can also track depth for dynamically varying data streams by using recursive updates. The tracking ability of the algorithm was demonstrated based on a real-life application associated with detecting changes in human activity from real-time accelerometer observations. Given the flexibility of the suggested approach, it can detect virtually any kind of changes in the distributional patterns of the observations, and thus outperforms detection approaches based on the Mahalanobis distance.

Nitrogen Functionalities of Amino-Functionalized Nitrogen-Doped Graphene Quantum Dots for Highly Efficient Enhancement of Antimicrobial Therapy to Eliminate Methicillin-Resistant Staphylococcus aureus and Utilization as a Contrast Agent.

There is an urgent need for materials that can efficiently generate reactive oxygen species (ROS) and be used in photodynamic therapy (PDT) as two-photon imaging contrast probes. In this study, graphene quantum dots (GQDs) were subjected to amino group functionalization and nitrogen doping (amino-N-GQDs) via annealing and hydrothermal ammonia autoclave treatments. The synthesized dots could serve as a photosensitizer in PDT and generate more ROS than conventional GQDs under 60-s low-energy (fixed output power: 0.07 W·cm−2) excitation exerted by a 670-nm continuous-wave laser. The generated ROS were used to completely eliminate a multidrug-resistant strain of methicillin-resistant Staphylococcus aureus (MRSA), a Gram-positive bacterium. Compared with conventional GQDs, the amino-N-GQDs had superior optical properties, including stronger absorption, higher quantum yield (0.34), stronger luminescence, and high stability under exposure. The high photostability and intrinsic luminescence of amino-N-GQDs contribute to their suitability as contrast probes for use in biomedical imaging, in addition to their bacteria tracking and localization abilities. Herein, the dual-modality amino-N-GQDs in PDT easily eliminated multidrug-resistant bacteria, ultimately revealing their potential for use in future clinical applications.

A Hybrid Maximum Power Point Tracking Method without Oscillations in Steady-State for Photovoltaic Energy Systems

This paper proposes a hybrid maximum power point tracking (MPPT) method with zero oscillation in steady-state by combining genetic algorithm (GA) and perturbation and observation (P&amp;O) method. The proposed MPPT can track the global maximum power point (GMPP) fast for a photovoltaic (PV) system even under partial shaded conditions (PSC). The oscillations around the GMPP are eliminated and the power loss can be reduced significantly. In addition, the proposed MPPT can make the PV system operate at the highest efficiencies under various atmospheric conditions. During the MPP tracking, the system will oscillate around the MPPs, resulting in unnecessary power loss. To solve the problem, the artificial intelligence (AI) algorithms, such as PSO, Bee Colony optimization, GA, etc., were developed to deal with this issue. However, the problem with the AI algorithm is that the time for convergence may be too long if the range of the MPP search space is large. In addition, if the atmospheric conditions change fast, the PV system may operate at or close to the local maximum power points (LMPPs) for a long time. In this paper, a method combining the P&amp;O’s fast tracking and GA’s GMPP tracking ability is proposed. The proposed system can stop the oscillations as soon as the GMPP is found, thus minimizing the power loss due to oscillations. The proposed MPPT can achieve superior performance while maintaining the simplicity of implementation. Finally, the simulation and experimental results are presented to demonstrate the feasibility of the proposed system.

Tracking and Estimation of Frequency, Amplitude, and Form Factor of a Harmonic Time Series [Lecture Notes

Formulas for estimating and tracking the (time-dependent) frequency, form factor, and amplitude of harmonic time series are presented in this lecture note; in particular, sine-dominant signals, where the harmonics follow roughly the dominant first harmonic, such as photoplethysmography (PPG) and breathing signals. Special attention is paid to the convergence behavior of the algorithm for stationary signals and the dynamic behavior in case of a transition to another stationary state. The latter issue is considered to be important for assessing the tracking abilities for nonstationary signals. We will discuss special cases of sine-dominant signals that will converge to an approximate or the same value as for a single sinusoid. The presented formulas are recursive in nature and use only the instantaneous values of the signal, in a low-cost and low-complexity manner. In particular, there is no need to use square roots or trigonometric or matrix operations; therefore, these formulas are suitable for low-power ambulatory measurements and computationally demanding machine learning algorithms. Furthermore, there appears to be an interesting connection between the form factor and Geary's test of normality.

Research on online parameter identification and SOC estimation methods of lithium‐ion battery model based on a robustness analysis

Under complex working conditions in variable temperatures, the accuracy of SOC is reduced due to the low robustness of the lithium-ion battery model online parameter identification method as well as the SOC estimation approach. Given this problem, a parameter identification method called FF-AGLS (alternative generalized least squares with forgetting factor) is proposed. The proposed method was combined with the robust H∞-CKF (cubature Kalman filter) based on singular value decomposition (SVD) in order to achieve an accurate estimation of lithium-ion battery SOC. FF-AGLS, which adopts unbiased estimation, has strong parameter tracking ability in low temperatures and low SOC regions, as well as high model parameter identification accuracy. As a result, combining the H∞ filter with SVD-CKF can maintain the robustness of the algorithm when the model parameters are uncertain, which may solve issues related to the decrease in SOC estimation accuracy caused by temperature changes. Finally, a series of experiments were conducted on the proposed method at different temperatures, while its performance was verified with the current under different working conditions. Accordingly, the joint algorithm based on FF-AGLS and H∞-SVD-CKF was able to accurately track model parameters and SOC with a strong degree of robustness.

Integral sliding mode control for DFIG based WECS with MPPT based on artificial neural network under a real wind profile

Handling the internal parametric variations and the nonlinearities of the high rated wind energy conversion system (WECS) is remained among the main challenges to maximize the produced energy, ameliorate its quality and ensure its efficient integration on the grid. In this context, a robust integral sliding mode control (ISMC) with Lyapunov function is proposed to control the active and reactive powers of a doubly fed induction generator (DFIG) based wind turbine, and to assure high dynamic performances according to the wind speed variation. To operate around an optimal rotational speed, a robust MPPT algorithm with mechanical speed control based on artificial Neural Network Controller (ANNC) is presented in order to extract the maximum power. Thereafter, the robust integral SMC are replaced by Field Oriented Control (FOC_PI) for comparative purposes. The objective is to prove the best performances of the system obtained by the proposed control method in terms of the dynamic response, total harmonic distortion THD (%) of the injected current into the grid, the reference tracking ability, Overshoot (%), precision and robustness. The effectiveness and robustness of each control techniques has been implemented and tested under MATLAB/Simulink environment by using a 1.5 MW wind system model.

Distributed adaptive formation control for underactuated quadrotors with guaranteed performances

This paper investigates a distributed adaptive formation control problem for underactuated quadrotors with guaranteed performances. To ensure a robust and stable formation pattern with predefined behavior bounds, by transforming the original constrained formation synchronization error dynamics into an equivalent unconstrained one, a prescribed performance mechanism is introduced in the translational loop to render the formation regulation as a prior. An adaptive consensus strategy is developed according to undirected graph theory and Lyapunov stability rules for follower quadrotors to achieve a distributed cooperative formation with prescribed tracking abilities via exchanging local information with neighbors. The presented control scheme has the following salient merits: (1) the formation synchronization errors can be guaranteed within pre-assigned bounds with desired transient behaviors despite of uncertain disturbances; (2) by using a state estimation error to update neural network (NN) parameters, rather than the tracking error that widely applied in traditional NN approximators, and with the help of MLP technique, the proposed SE-MLP observer capable of decreasing the computational complexity can achieve a fast identification of lumped disturbances without causing high-frequency oscillations even using a large adaptive gain, and the transient solutions of L2 norm of the differential of neural weights are established to illustrate the mechanism of SE-MLP observer in reducing chattering behaviors. The merits of presented algorithm are confirmed by sufficient simulations.

Combining deep learning with geometric features for image-based localization in the Gastrointestinal tract

Tracking monocular colonoscope in the GastroIntestinal (GI) tract is challenging as the obtained images suffer from deformation, blurred textures, and significant changes in appearance. These drawbacks greatly restrict the tracking ability of conventional geometry-based methods, which are heavily dependent on the performance of corner points extraction from the image. Even though end-to-end Deep Learning (DL) can overcome these issues, limited labeling data is a roadblock to the state-of-the-art DL-based method. To handle these drawbacks, we propose a novel approach to combine the DL-based method with the traditional geometry-based approach to achieve better localization with small training data. In this work, a DL network is trained with the images of the pre-operative endoscopy/colonoscopy. Siamese architecture is introduced to perform the zone labeling of the image based on the anatomical segmentation with expert knowledge. Then, using the image in the therapeutic intervention, our method predicts the 6 degrees of freedom scope pose and recover geometric reference to the images from the pre-operative endoscopy/colonoscopy. The DL network predicts the zone of the testing image, and the pre-generated triangulated map points within the zone in the training set are registered with the bundle adjustment algorithm. The proposed hybrid method is tested on the synthetic data sets and the real-world in-vivo data sets. Further, the results achieved through various experiments validate that the proposed method outperforms traditional geometry-based only or DL-based only localization techniques.

A novel IMC-FOF design for four wheel steering systems of distributed drive electric vehicles

To improve handling and stability for distributed drive electric vehicles (DDEV), the study on four wheel steering (4WS) systems can improve the vehicle driving performance through enhancing the tracking capability to desired vehicle state. Most previous controllers are either a large amount of calculation, or requires a lot of experimental data, these are relatively time-consuming and laborious. According to the front and rear wheel steering angle of DDEV can be distributed independently, a novel controller named internal model controller with fractional-order filter (IMC-FOF) for 4WS systems is proposed and studied in this paper. The IMC-FOF is designed using the internal model control theory and compared with IMC and PID controller. The influence of time constant and fractional-order parameters which is optimized using quantum genetic algorithms (QGA) on tracking ability of vehicle state are also analyzed. Using a production vehicle as an example, the simulation is performed combining Matlab/Simulink and CarSim. The comparison results indicated that the proposed controller presents performance to distribute the front and rear wheel steering angle for ensuring better tracking capability to desired vehicle state, meanwhile it possesses strong robustness.