Tracking Ability Research Articles

Adaptive neural network control of robotic manipulators with input constraints and without velocity measurements

AbstractThis paper addresses the trajectory tracking problem for a class of uncertain manipulator systems under the effect of external disturbances. The main challenges lie in the input constraints and the lack of measurements of joint velocities. An extend‐state‐observer is utilized to estimate the velocity signals; then, a neural‐network‐based adaptive controller is proposed to solve the problem, where a term based on the nominal model is included to enhance the tracking ability, and the effect of uncertainties and disturbances are compensated by a neural‐network term. Compared with the existing methods, the main distinctive features of the presented approach are: (i) The control law is guaranteed to be bounded by design, instead of directly bounded by a saturation function. (ii) The trade‐off between the performance and robustness of the presented controller can be easily tuned by a parameter that depends on the size of model uncertainties and external disturbances. By virtue of the Lyapunov theorem, the convergence properties of the proposed controller are rigorously proved. The performance of the controller is validated via both simulations and experiments conducted on a two‐degree‐of‐freedom robot manipulator.

A novel suppressing Kalman filter divergence method for the state of charge estimation of lithium-ion batteries under complex conditions

The estimation method for state of charge (SOC) of lithium-ion batteries based on the Extended Kalman Filter (EKF) is prone to losing the ability to track sudden changes under complex driving conditions. Thus, this method cannot guarantee estimation accuracy and robustness. To overcome this deficiency, fading factors are introduced into the error covariance to enhance the effect of newly generated measurement data on the current filtering estimation. A Suboptimal Multiple Fading EKF (SMFEKF) SOC estimation method is proposed herein. The fading factors are solved by forcing the new error sequence of the predicted and actual terminal voltage values to remain orthogonal. The new error sequence that remains orthogonal undergoes transitions from strong autocorrelation in the original stable state to weak autocorrelation. Through real-time adjustment of the Kalman gain matrix, the effective tracking ability of the system in the event of sudden changes gets enhanced. To verify the effectiveness of the proposed method, first, a battery dynamic condition test library covering multiple driving scenarios (such as cities, suburbs, and highways) is established through ADVISOR advanced vehicle simulator driving condition tests. Second, a second-order equivalent circuit model is established and the recursive least squares method is used for parameter identification. Finally, a detailed comparison is made between SMFEKF and EKF in terms of SOC estimation accuracy and robustness under driving conditions with different levels of complexity. It is found that four operating conditions are the main reasons for filtering divergence in EKF. The results show that the root mean square errors of SMFEKF are 63.72 %, 54.20 %, and 21.30 % those of EKF under the three complex conditions, respectively. The new method effectively reduces SOC estimation error and solves the problem of EKF filtering divergence under complex conditions.

Design of Fractional-Order Non-Singular Terminal Sliding Mode Observer Sensorless System for Surface-Mounted Permanent Magnet Synchronous Motor

A new sensorless speed control system for a fractional-order terminal non-singular sliding mode surface-mounted permanent magnet synchronous motor is proposed. The fractional terminal non-singular sliding mode surface, which can converge in finite time, is designed by combining the fractional-order control theory with the terminal attractor concept. Then, a new control rate is proposed to reduce system buffeting. Secondly, an adaptive back electromotive force filter is designed to reduce the harmonics in the sliding mode function estimation and improve the observation accuracy. In addition, the theoretical analysis of the designed system proves that the system can converge in a finite time. Then, a fraction-order phase-locked loop with variable factors is designed to make the system more capable of tracking the rotor. Finally, a simulation and experiment platform is built, and a comparison experiment is carried out, which proves that the designed algorithm has a stronger rotor position tracking ability and a better dynamic performance of the system.

Robust fuzzy – backstepping mode control of an induction motor

This paper presents a novel control for induction motors using backstepping control and fuzzy logic. The Backstepping control is suggested as a substitute for the conventional PI controller to attain high-performance motion control systems for the speed, flux, and current control loops. Stability analysis, based on Lyapunov theory, is also conducted to guarantee the convergence of the speed tracking error from all possible initial conditions. The speed regulator was changed to a fuzzy logic regulator. The simulation results confirm that the proposed hybrid control fuzzy-backstepping scheme offers improved performance in terms of trajectory tracking ability and robustness against variation when subjected to time-varying reference input.

PyOKR: A Semi-Automated Method for Quantifying Optokinetic Reflex Tracking Ability.

The study of behavioral responses to visual stimuli is a key component of understanding visual system function. One notable response is the optokinetic reflex (OKR), a highly conserved innate behavior necessary for image stabilization on the retina. The OKR provides a robust readout of image tracking ability and has been extensively studied to understand visual system circuitry and function in animals from different genetic backgrounds. The OKR consists of two phases: a slow tracking phase as the eye follows a stimulus to the edge of the visual plane and a compensatory fast phase saccade that resets the position of the eye in the orbit. Previous methods of tracking gain quantification, although reliable, are labor intensive and can be subjective or arbitrarily derived. To obtain more rapid and reproducible quantification of eye tracking ability, we have developed a novelsemi-automated analysis program, PyOKR, that allows for quantification of two-dimensional eye tracking motion in response to any directional stimulus, in addition to being adaptable to any type of video-oculography equipment. This method provides automated filtering, selection of slow tracking phases, modeling of vertical and horizontal eye vectors, quantification of eye movement gains relative to stimulus speed, and organization of resultant data into a usable spreadsheet for statistical and graphical comparisons. This quantitative and streamlined analysis pipeline, readily accessible via PyPI import, provides a fast and direct measurement of OKR responses, thereby facilitating the study of visual behavioral responses.

The effect of special exercises using the visual stimuli device on the speed of motor response, visual tracking, the skills of cutting and dispersing the ball, and various defensive movements for young handball players

The research is significant because it aims to improve the speed of motor response and visual tracking through targeted exercises using the visual stimuli device that the two researchers created as part of the training program for the sample. This program also supports the individual defensive movements of the players, which are exemplified by the skills of cutting and dispersing the ball and other defensive movements for young handball players. Regarding the research issue, the researchers observed a deficiency in the players' defensive performance, which manifests as slow movement and reaction to the ball's movement. This deficiency could be attributed to a lack of interest in handball player training programs as well as a weakness in visual abilities and motor response speed to the attacker's and the ball's movement. Preparing unique workouts with visual stimuli to enhance young handball players' motor reaction speed, visual tracking abilities, ball-cutting and ball-dispersing capabilities, and defensive maneuvers is one of the research's goals. The research population was purposefully selected and sampled; specifically, the goalkeepers, who numbered two, were omitted, and the youth players of the Diyala Handball Club for the 2022–2023 sports season, numbered eighteen. As a result, the sample size increased to sixteen participants, who were split into eight players, representing the experimental group, and eight players, representing the control group, at random (even and odd numbers). After conducting an exploratory experiment on two handball players, the researchers came to the conclusion that the experimental study sample's development of visual tracking and motor reaction speed was positively impacted by workouts on the visual stimulus device. The researchers suggest that in order to improve handball players' efficiency, it is imperative to engage in visual stimulus workouts that enhance skill performance and physical and visual capacities.

Online Predictive Visual Servo Control for Constrained Target Tracking of Fixed-Wing Unmanned Aerial Vehicles

This paper proposes an online predictive control method for fixed-wing unmanned aerial vehicles (UAVs) with a pan-tilt camera in target tracking. It aims to achieve long-term tracking while concurrently maintaining the target near the image center. Particularly, this work takes the UAV and pan-tilt camera as an overall system and deals with the target tracking problem via joint optimization, so that the tracking ability of the UAV can be improved. The image captured by the pan-tilt camera is the unique input associated with the target, and model predictive control (MPC) is used to solve the optimization problem with constraints that cannot be performed by the classic image-based visual servoing (IBVS). In addition to the dynamic constraint of the UAV, the perception constraint of the camera is also taken into consideration, which is described by the maximum distance between the target and the camera. The accurate detection of the target depends on the amount of its feature information contained in the image, which is highly related to the relative distance between the target and the camera. Moreover, considering the real-time requirements of practical applications, an MPC strategy based on soft constraints and a warm start is presented. Furthermore, a switching-based approach is proposed to return the target back to the perception range quickly once it exceeds the range, and the exponential asymptotic stability of the switched controller is proven as well. Both numerical and hardware-in-the-loop (HITL) simulations are conducted to verify the effectiveness and superiority of the proposed method compared with the existing method.

Butyrylcholinesterase-Activated Near-Infrared Fluorogenic Probe for In Vivo Theranostics of Alzheimer's Disease.

Butyrylcholinesterase (BChE) is a promising biomarker and effective therapeutic target for Alzheimer's disease (AD). Herein, we designed a BChE-activated near-infrared (NIR) probe, DTNP, which could be activated by BChE and inhibit its enzymatic activity. DTNP is composed of a cyclopropane moiety as the recognition unit, a NIR fluorophore hemicyanine as the NIR reporter, and a BChE inhibitor as the therapeutic unit. DTNP specifically binds BChE with high sensitivity and exhibits strong "turn-on" NIR fluorescence as well as nerve cell protection. In vivo imaging shows DTNP has favorable blood-brain barrier permeability and long-term tracking ability with preliminary competence in AD diagnosis. DTNP can significantly inhibit BChE activity, promote the release of ACh, and rescue learning deficits and cognitive impairment. Therefore, DTNP, the first reported and partially validated theranostic probe for the detection of BChE in AD, may provide a foundation and inspiration for imaging and therapy in AD.

MobileInst: Video Instance Segmentation on the Mobile

Video instance segmentation on mobile devices is an important yet very challenging edge AI problem. It mainly suffers from (1) heavy computation and memory costs for frame-by-frame pixel-level instance perception and (2) complicated heuristics for tracking objects. To address these issues, we present MobileInst, a lightweight and mobile-friendly framework for video instance segmentation on mobile devices. Firstly, MobileInst adopts a mobile vision transformer to extract multi-level semantic features and presents an efficient query-based dual-transformer instance decoder for mask kernels and a semantic-enhanced mask decoder to generate instance segmentation per frame. Secondly, MobileInst exploits simple yet effective kernel reuse and kernel association to track objects for video instance segmentation. Further, we propose temporal query passing to enhance the tracking ability for kernels. We conduct experiments on COCO and YouTube-VIS datasets to demonstrate the superiority of MobileInst and evaluate the inference latency on one single CPU core of the Snapdragon 778G Mobile Platform, without other methods of acceleration. On the COCO dataset, MobileInst achieves 31.2 mask AP and 433 ms on the mobile CPU, which reduces the latency by 50% compared to the previous SOTA. For video instance segmentation, MobileInst achieves 35.0 AP and 30.1 AP on YouTube-VIS 2019 & 2021.

Effects of logistics performance on Vietnam’s exports: a quantitative analysis using the PPML method

This study aims to analyze the effects of logistics performance on Vietnam’s exports at both the overall level and in specific aspects. We investigate the linkage between logistics performance and Vietnam’s exports based on a panel of bilateral trade from Vietnam to 158 trading partners from 2007 to 2018. Using the Poisson pseudo-maximum likelihood method, the positive and significant effects of Vietnam’s and importing countries’ logistics performance on exports are identified. Our estimates become highly consistent after controlling for both time-specific effects and time-invariant country-fixed effects and remain robust to changes in the model specification, contributing reliable empirical evidence to the literature. Considering the specific aspects of logistics performance, we find the significant roles of the ability to track and trace consignments, the quality of logistics services, the customs clearance efficiency, and the timeliness of shipments in trade. From Vietnam’s perspective, improvements in tracking and tracing ability and timeliness can enhance exports to other countries, with larger effects than those of trading partners. Based on these findings, this study provides evidence for policymakers to effectively enhance trade with other countries by developing the national logistics system alongside further logistics cooperation with trading partners and with a greater focus on tracking and tracing ability and timeliness.

Active Disturbance Rejection Control Design with Sensitivity Constraint for Drum Water Level

The drum water level plays a crucial role in the safety and economy of heat recovery boilers. However, the control of the drum water level faces many challenges, such as external disturbances and system uncertainties. To enhance the control performance of the drum water level, a modified active disturbance rejection control (MADRC) optimized with sensitivity constraint is proposed in this paper. Firstly, the control structure of the three-element control system for the drum water level is introduced and analyzed. Based on the regular active disturbance rejection control (ADRC) structure, the structure of the MADRC is introduced and the convergence of the proposed MADRC is proven. Then a modified whale optimization algorithm (MWOA) with sensitivity constraint is applied to optimize the parameters of the MADRC. With different sensitivity constraints, the parameters of the MADRC and comparative controllers are obtained, and their control performance for tracking and disturbance rejection abilities is compared. Moreover, the ability to handle system uncertainties is analyzed. Simulation results and performance indexes show that the proposed MADRC can obtain the best tracking and disturbance rejection abilities with satisfactory robustness. The satisfactory control performance shows that the proposed MADRC has wide application potential for heat recovery boilers and other industrial processes.

Predictive controllers for synchronous reluctance motor drive systems

This paper proposes the design and implementation of predictive controllers for synchronous reluctance motor drive systems to enhance their dynamic responses. The predictive speed and current controllers in this paper are designed in systematic procedures. The predictive speed controller is implemented by using Laguerre function procedure. The Laguerre function is used to simplify the algorithm and to minimize the execution time of the digital signal processor. For predictive current controller, a finite control set method improves the current tracking ability. The measured currents are used to predict the future phase-current based on the motor model. The optimal control inputs of both predictive controllers are determined by using a cost function minimization method. Experimental results show the proposed drive system provides a wide adjustable speed range, from 2 r/min to 1800 r/min. It has better performance than a proportional-integral (PI) controller including fast rise time, which is 0.9 second, small steady state error, which is 0.32 r/min, and small current ripples. A 32-bit floating-point digital signal processor, TMS-320-F-28335 DSP, is employed to implement the control algorithms.

Fuzzy adaptive PID control for path tracking of field intelligent weeding machine

In order to reduce the influence of external interference on the intelligent weeder, improve the stability of its path tracking system, enhance its weeding effect, and reduce the rate of seedling injury, the motion steering control of the four-wheeled weeder is studied. The control strategy of fuzzy adaptive proportional integral differential (PID) algorithm is determined by establishing the path tracking mathematical model of the field intelligent weeder; the fuzzy adaptive PID controller is designed with the input average steering angle deviation of the front wheels and the rate of change of the deviation so as to realize the automatic adjustment and optimization of the parameters. We use Simulink to build the control system model and to compare and analyze with the PID controller. The results show that under the action of the step signal, the rise time of the fuzzy adaptive PID and PID control system responses is 2.596 and 4.209 s, respectively; under the action of the impulse, the fuzzy adaptive PID has a significant advantage over the traditional PID control system in terms of the rise time and response time. In addition, this control system has a fast response speed, high adaptability, high anti-interference ability, and superior path tracking ability, which are necessary for improving the accuracy of the field intelligent weeder and reducing the rate of seedling injury.

Dynamic phasor measurement algorithm based on high-precision time synchronization

Ensuring the swift and precise tracking of power system signal parameters, especially the frequency, is imperative for the secure and stable operation of power grids. In instances of faults within the distribution network, abrupt changes in frequency may occur, presenting a challenge for existing algorithms that struggle to effectively track such signal variations. Addressing the need for enhanced performance in the face of frequency mutations, this paper introduces an innovative approach—the Covariance Reconstruction Extended Kalman Filter (CREKF) algorithm. Initially, the dynamic signal model of electric power is meticulously analyzed, establishing a dynamic signal relationship based on high-precision time source sampling tailored to the signal model’s characteristics. Subsequently, the filter gain, covariance matrix, and variance iteration equation are determined based on the signal relationship among three sampling points. In a final step, recognizing the impact of the covariance matrix on algorithmic tracking ability, the paper proposes a covariance matrix reset mechanism utilizing hysteresis induced by output errors. Through extensive verification with simulated signals, the results conclusively demonstrate that the CREKF algorithm exhibits superior measurement accuracy and accelerated tracking speed when confronted with mutating signals.

The nonlinear model reference adaptive impedance control for underwater manipulator operation objects in bilateral teleoperation system

The bilateral teleoperation system is susceptible to model parameter uncertainty and unknown disturbances in both the master and slave manipulators, resulting in instability and inaccuracies in the force and position tracking performance. To address these issues, a novel nonlinear model reference adaptive impedance controller has been designed to achieve coordinated force and position synchronization of dual manipulators. The adaptive control laws, based on sliding mode functions, have been designed to compensate for the uncertainty of the manipulator model. Furthermore, an adaptive estimation law has been employed to appraise the unknown upper bound of external disturbances. This ensures that the closed-loop model parameters of the dual manipulator converge to the reference impedance model respectively. Simultaneously, it enables the position error between the reference model response and the end-effector task space position to asymptotically converge to zero. To verify the effectiveness of the proposed controller, simulations have been conducted on the MATLAB platform and experiments on a single degree of freedom teleoperation system have been performed. The results demonstrate that the controller exhibits strong robustness and has the capability of force-position tracking ability.

Bayesian approximation for parameterized KALMAN filter for investigation and simulation of unknown noise variance trajectory following in state space models with different noise distributions

Bayesian approach can be used for parameter identification and extraction in state space models and its ability for analyzing sequence of data in dynamical system is proved in different literatures. In this paper, Bayesian approach for approximation of variances in measurement noise with KALMAN filter is applied for estimation of the dynamical state and measurement data in discrete dynamical system. Detection of uncertainty and estimation of those can be done simultaneously with adaptive KALMAN filter. This algorithm at each step time estimates noise variance and state of system with KALMAN filter. Then, approximation is formed at each step separately and at each step sufficient statistics of the state and noise variances are computed with a fixed-point iteration of a KALMAN filter. For showing influence of variance in measurement data on algorithm different simulations is applied. First, effect of variance and its distribution on detection performance is simulated in KALMAN filter without Bayesian formulation. Then simulation is applied to KALMAN filter with ability of variance tracking of measurement data.in these simulations, influence of distribution of measurement data in each step is estimated and true variance of data is obtained by algorithm and is compared in different scenarios. Afterwards, one typical modeling of nonlinear state space model with inducing noise measurement is simulated by this approach. Finally, the performance and the important limitations of this algorithm in these simulations are explained.

Design of a Novel Chaotic Horse Herd Optimizer and Application to MPPT for Optimal Performance of Stand-Alone Solar PV Water Pumping Systems

A significant part of agricultural farms in the Kingdom of Saudi Arabia (KSA) are in off-grid sites where there is a lack of sufficient water supply despite its availability from groundwater resources in several regions of the country. Since abundant agricultural production is mainly dependent on water, farmers are forced to pump water using diesel generators. This investigation deals with the increase in the effectiveness of a solar photovoltaic water pumping system (SPVWPS). It investigated, from a distinct perspective, the nonlinear behavior of photovoltaic modules that affects the induction motor-pump because of the repeated transitions between the current and the voltage. A new chaotic Horse Herd Optimization (CHHO)-based Maximum Power Point Tracking technique (MPPT) is proposed. This algorithm integrates the capabilities of chaotic search methods to solve the model with a boost converter to maximize power harvest while managing the nonlinear and unpredictable dynamical loads. The analytical modeling for the proposed SPVWPS components and the implemented control strategies of the optimal duty cycle of the DC–DC chopper duty cycle and the Direct Torque Control (DTC) of the Induction Motor (IM) has been conducted. Otherwise, the discussions and evaluations of the proposed model performance in guaranteeing the maximum water flow rate and the operation at MPP of the SPVWPS under partial shading conditions (PSC) and changing weather conditions have been carried out. A comparative study with competitive algorithms was conducted, and the proposed control system’s accuracy and its significant appropriateness to improve the tracking ability for SPVWPS application have been proven in steady and dynamic operating climates and PSC conditions.

The usability and reliability of a smartphone application for monitoring future dementia risk in ageing UK adults.

The rising number of dementia diagnoses and imminent adoption of disease-modifying treatments necessitate innovative approaches to identify individuals at risk, monitor disease course and intervene non-pharmacologically earlier in the disease course. Digital assessments of dementia risk and cognitive function have the potential to outperform traditional in-person assessments in terms of their affordability, accuracy and longitudinal tracking abilities. However, their accessibility and reliability in older adults is unclear. To evaluate the usability and reliability of a smartphone assessment of lifestyle and cognitive factors relevant to dementia risk in a group of UK-based older adults. Cognitively healthy adults (n = 756) recruited through the Dementias Platform UK Great Minds volunteer register completed three assessments of cognitive function and dementia risk over a 3-month period and provided usability feedback on the Five Lives smartphone application (app). We evaluated cognitive test scores for age, gender and higher education effects, normality distributions, test-retest reliability and their relationship with participants' lifestyle dementia risk factors. Participants found the app 'easy to use', 'quick to complete' and 'enjoyable'. The cognitive tests showed normal or near-to-normal distributions, variable test-retest reliabilities and age-related effects. Only tests of verbal ability showed gender and education effects. The cognitive tests did not correlate with lifestyle dementia risk scores. The Five Lives assessment demonstrates high usability and reliability among older adults. These findings highlight the potential of digital assessments in dementia research and clinical practice, enabling improved accessibility and better monitoring of cognitive health on a larger scale than traditional in-person assessments.

Iterative learning control for differential inclusion systems with random fading channels by varying average technique.

The aim of this paper is to study iterative learning control for differential inclusion systems with random fading channels between the plant and the controller. In reality, the phenomenon of fading will inevitably occur in network transmission, which will greatly affect the tracking ability of output trajectory. This study discusses the impact of fading channel on tracking performance at the input and output sides, respectively. First, a set-valued mapping in a differential inclusion system with uncertainty is converted into a single-valued mapping by means of a Steiner-type selector. Then, to offset the effect of the fading channel and improve the tracking ability, a variable local average operator is constructed. The convergence of the learning control algorithm designed by the average operator is proved. The results show that the parameters in the varying local average operator can be adjusted to trade-off between the learning rate and the fading offset rate. Finally, the theoretical results are verified by numerical simulation of the switched reluctance motors.

A Novel Hybrid Whale Optimization Algorithm Differential Evolution Algorithm-Based Maximum Power Point Tracking Employed Wind Energy Conversion Systems for Water Pumping Applications: Practical Realization

This article describes the implementation of a proposed hybrid whale optimization algorithm differential evolution (WOADE) as a maximum power point tracking (MPPT) for wind energy conversion systems (WECS) employed for water pumping applications. The proposed hybrid MPPT algorithm provides rapid and zero oscillation power tracking to achieve the global maximum power point (GMPP) within a limited number of iterations. In this work, the exploitation capability of the whale optimization algorithm (WOA) is integrated with the exploration behavior of differential evolution (DE) to improve the tracking ability over a steady and dynamic operating climate. The performance of a permanent magnet synchronous generator (PMSG) driven centrifugal pump for water pumping applications with wind energy conversion systems has also been investigated. A modified Ćuk converter provides a higher output voltage with an enhanced voltage conversion ratio and is employed for optimal wind power tracking. The practical realization is done to substantiate the productiveness of the introduced hybrid WOADE based MPPT algorithm for WECS applied for water pumping under varying and sudden changes in wind velocity.