Tracking Performance Research Articles

Overcoming Weak Grid Challenges: A Combined Approach to VSI Stability with Impedance Adjustment, Control Optimization, and Microgrid Integration

This paper addresses the challenges in Voltage Source Inverter (VSI) systems connected to weak grids, where frequent impedance changes lead to instability and power quality issues. This research studies how changing grid impedance affects current distortion and the stability of a VSI. It proposes the stability analysis of a single loop controller and optimize its settings using various techniques (ZN-method, PSO, GA) to ensure VSI stability and meet current distortion limits (THD compliance), when grid impedance varies. The primary focus revolves around addressing two key challenges: managing impedance variations at the PCC and enhancing the tracking performance of the PI controller. The VSI-based system connected to the weak grid and in standalone mode is simulated on Typhoon HIL, to validate the effectiveness of obtained optimized controller parameters by changing various conditions like, the output power regulation and sudden load change in a standalone distribution network. The MATLAB/SIMULINK with m-files is utilized for the parameters optimization and controller model simulation purposes. This research is important for developing more reliable and resilient power systems, specifically by investigating the transient behaviour of VSI frequency and voltage under sudden changes, to ensure an uninterruptible power supply to critical loads.

Disturbance Observer–Based Adaptive Dynamic Event‐Triggered Control for a Class of Nonlinear Systems With Input Saturation

ABSTRACTFor the adaptive tracking control of a class of nonlinear systems with asymmetric input saturation and external disturbances, a novel dynamic event‐triggered neural network control scheme on the foundation of full‐state constraints is proposed. Firstly, by adding a compensation term into the traditional dynamic surface control framework, the filtering error ignored generally is eliminated. Secondly, a nonlinear disturbance observer is constructed to observe the compound interference united by external disturbances and approximation errors based on the approximation ability of neural network. Thirdly, an auxiliary variable is combined with a smooth function used to deal with the saturated input function to offset the influence of input saturation constraints. In addition, for ensuring that all states of the system are constrained by preset time‐varying ranges, the adaptive control method and dynamic event‐triggered condition are posed with the help of barrier Lyapunov function. Then, the tracking performance and the boundness of all signals in the closed‐loop system can be guaranteed by employing the presented adaptive control approach with dynamic event‐triggered mechanism, as well as the Zeno behavior does not appear. Finally, the validity of the designed control method can be demonstrated in simulation examples.

Detecting and Quantifying Glaucomatous Visual Function Loss With Continuous Visual Stimulus Tracking: A Case-Control Study.

Continuous visual stimulus tracking could be used as an easy alternative to standard automated perimetry (SAP) for visual function screening. With continuous visual stimulus tracking, we simplified the perimetric task to following a moving dot on a screen with the eyes. Here, we determined whether tracking performance (the agreement between gaze and stimulus position) enables the detection and quantification of glaucomatous visual function loss (in terms of SAP), and whether it shows a learning effect. We evaluated the tracking performance of 36 cases with early, moderate, or severe glaucoma (median with interquartile range [IQR] age = 70 [67-74] years) and 36 controls (median = 70, IQR = 67-72 years). All participants monocularly tracked a moving stimulus (Goldmann size III) at 3 Weber contrast levels: 40, 160, and 640%, while their eye movements were recorded. Glaucoma decreased the tracking performance, with the most severe reduction in the severe glaucoma cases. A distinction between groups was possible, but depended on the contrast level: tracking performance of early glaucoma cases was significantly different from controls only at 40% contrast. Within the cases, glaucomatous visual function loss (SAP Mean Sensitivity [MS]) was best correlated with tracking performance when using 160% contrast. There was no significant learning effect. Overall, the data indicate that it is possible to detect and quantify glaucomatous visual function loss with continuous visual stimulus tracking. Continuous visual stimulus tracking is an easy, fast, and intuitive technique that has the potential for diagnostic applications in detection of new glaucoma cases and monitoring of previously diagnosed cases.

Photovoltaic tracking technologies for sustainable electrification: A techno-economic analysis on Western Pelee Island, Canada

This article investigates the economic and technical feasibility of employing various photovoltaic (PV) tracking systems to electrify Western Pelee Island. The systems under consideration include horizontal-axis monthly adjustment (HMA), horizontal-axis continuous adjustment (HCA), vertical-axis continuous adjustment (VCA), and dual-axis tracker (DAT). The analysis includes a techno-economic assessment of these trackers, considering solar, bio, and diesel operation and two dispatch strategies: cycle charging (CC) and load following (LF). The results indicate that the optimal solution is a CC-controlled system equipped with a VCA tracker. The LF-controlled system with this tracker has a higher net present cost (NPC), cost of energy (COE), and renewable fraction by ∼$0.02 M, ∼$0.002/kWh, and 7.6%, respectively. NPC of HMA and COE of HVA-based systems with CC strategies are the most sensitive cases to SOCmin. In load variation, the largest and lowest decrease in COE, respectively, is observed in HVA and DA trackers controlled by CC dispatch strategy. In order for DA trackers to match the performance of VCA trackers, their costs must decrease by approximately 41% and 43% in CC and LF systems, respectively. The financial sensitivity of DA-based systems is higher due to albedo effects. This study provides valuable insights into optimizing PV tracking for the electrification of Western Pelee Island and enhances our understanding of the economic implications associated with dispatch strategies and tracking technologies in sustainable energy planning.

A Novel Adaptive Non-Singular Fast Terminal Sliding Mode Control for Direct Yaw Moment Control in 4WID Electric Vehicles

This study proposes an adaptive non-singular fast terminal sliding mode control (NFTSMC)-based direct yaw moment control (DYC) strategy to enhance driving stability in four-wheel independent drive (4WID) electric vehicles. Unlike conventional SMC, the proposed method dynamically adapts to system uncertainties and reduces chattering, a critical issue in control applications. The approach begins with the development of an NFTSMC method, analyzing its performance to identify areas for improvement. To enhance robustness and responsiveness, a novel adaptive NFTSMC method is introduced. This method integrates a non-singular fast terminal sliding mode surface with a novel adaptive fast-reaching control law that combines an adaptive switching mechanism and a fast-reaching law. The designed adaptive switching law adjusts the sliding gain in real time based on system conditions, reducing chattering without needing an upper bound on uncertainties as required by traditional NFTSMC methods. Concurrently, the fast-reaching law ensures rapid convergence from any initial condition and accurate tracking performance. Simulation results across various steering maneuvers, including step, sinusoidal, and fish-hook inputs, demonstrate that the proposed method significantly improves tracking accuracy and driving stability over traditional SMC and NFTSMC methods. Marked reductions in RMS and peak yaw rate errors, and effective chattering mitigation, highlight advancements in vehicle safety and stability.

Tube-MPC based trajectory tracking control for substation inspection robot

Abstract A Tube-MPC based trajectory tracking control method was developed to enhance the capabilities of substation inspection robots. First, the kinematic models of the inspection robots were established, and the general form of the optimization control problem for Tube-MPC was introduced. The nominal control law for the nominal system was derived using a linearized nominal model, which was employed to solve the Tube-MPC cost function, ensuring accurate tracking of the desired trajectory. The algorithm was then simulated and tested. Simulation results demonstrate that the Tube-MPC approach significantly outperforms conventional MPC algorithms in terms of trajectory tracking performance and robustness for substation inspection robots. Experimental results further confirm that the Tube-MPC based method effectively enhances both robustness and tracking accuracy during inspection robot operations.

Parallel-filter observer control with CCD measurements for space optical communication equipment on a moving platform

This paper reports a parallel-filter observer methodology to stabilize the line-of-sight (LOS) for space optical communication missions, which breaks the bandwidth limitation for achieving trajectory tracking on a moving platform with charge-coupled device (CCD) measurements. An equivalent high-order parallel-filter compensator is introduced to reduce the output of the controller, resulting in a stabilized high-bandwidth observer. The parallel-filter observer effectively mitigates the stability degradation associated with a single high-bandwidth filter. Furthermore, a high-bandwidth rate loop is conducive to ensuring the stability of the tracking gimbal in inertial space. The proposed approach directly uses and processes tracking errors to achieve LOS stabilization rather than relying on detailed knowledge of target dynamics to evaluate and compensate for target trajectory. Therefore, it is simple and applicable to provide excellent tracking performance for engineering applications with limited measurement bandwidth. Simulation and experimental results demonstrate the effectiveness of the proposed method for improving LOS stabilization in a tracking system with carrier disturbance and target motion.

The effects of water flow velocity and odor release frequency on the odor-tracking behavior of swimming crabs (Portunus trituberculatus)

Chemical-guided tracking is vital for critical activities such as foraging and mating in swimming crabs (Portunus trituberculatus), as it facilitates the detection and localization of odor sources. While water flow and odor release patterns are known to influence odor-tracking behavior, the specific mechanisms of their impact remain poorly understood. This study quantified the tracking behaviors of juvenile swimming crabs in response to varying water flow velocities (0.7 cm/s, 1.6 cm/s, and 2.5 cm/s) and odor release frequencies (0.5 Hz, 1.0 Hz, and 1.5 Hz). Results indicate that (1) at a flow velocity of 1.6 cm/s, crabs exhibited a higher proportion of tracking and success. Successful trackers at this flow velocity also demonstrated better tracking performance, including higher net-to-gross displacement ratios (NGDR), reduced gross displacements, shorter exit and track durations, and simpler tracking trajectories. (2) While odor release frequency did not significantly affect the proportion of tracking or success, the successful trackers at a higher frequency (1.5 Hz) had lower NGDR and higher gross displacements compared to those exposed to a lower frequency (0.5 Hz). (3) The successful trackers were not stastistically affected by the interaction between flow velocities and release frequencies. (4) In contrast to successful trackers, unsuccessful ones showed highly variable responses to flow velocity and odor release frequency, with generally inferior tracking performance. These findings provide valuable insights into the olfactory ecology of swimming crabs and can inform the optimized of aquaculture management and formulated feeds production.

A multivariable adaptive control method for aeroengine with H∞ performance considering engine output limitation protection based on fully adjustable Neural Network

Aeroengines, being highly nonlinear systems subject to external disturbances, present a challenge for conventional control methods in achieving satisfactory performance. The radial basis function neural networks possess the capability to effectively model highly approximate nonlinear functions, making them suitable for implementation in aeroengine control. However, the radial basis function (RBF) neural network is a local approximation model that inadequately incorporates historical information. Additionally, the tracking error of neural networks can also impact control effectiveness. To address this issue, this paper proposes a multivariable control method for aeroengines with H∞ performance based on fully adjustable Long Short-term Memory-Radial Basis Function Neural Network (LSTM-RBFNN). Firstly, a factual mathematical model of an aeroengine is established based on the principles of aerodynamics and thermodynamics. Secondly, the paper proposed an LSTM-RBF neural network architecture to enhance prediction performance and proposed using a finite time perturbation estimator to gauge the approximate error of neural networks, thereby acquiring immeasurable approximative error information. Then, in order to mitigate the influence of neural network tracking error on the loop, the estimated errors are incorporated into the primary loop controller, and a static state disturbance full feedback H∞ controller is proposed. Then, leveraging the LSTM-RBF architecture proposed in this study, we further employ Taylor expansion of nodes to derive the adaptive law of the neural network and propose a novel neural network adaptive law that incorporates tracking error estimated by the disturbance observer into the adjustment mechanism to enhance tracking performance. The stability of this adaptive law has been proven based on Lyapunov function analysis. And considering the output limitation protection problem of aeroengine, a virtual command value based limitation protection method is proposed for the above control method, and it is proved that this limitation method does not damage the stability and design process of the original control method. Finally, numerical simulation and hardware in loop simulation experiments were conducted, and the results showed that under the same adaptive gain of the neural network, the performance indicators (IAE, IATE, σ) could be reduced by more than 50% compared to other methods, which demonstrates superior tracking and disturbance rejection performance achieved by our proposed method.

Research on maximum power point tracking of photovoltaic power generation based on improved hybrid optimization algorithm

Due to environmental factors’ influence, the power–voltage (P–V) curve of a photovoltaic array typically presents multiple peaks. The traditional gravitational search algorithm is inclined to fall into local optimal solutions and demonstrates poor performance in maximum power point tracking. Consequently, this paper proposes applying the PSO algorithm for optimizing the GSA parameters. Meanwhile, introducing the gravitational constant into the GSA algorithm accomplishes the dynamic adjustment of the three key parameters in PSO. Furthermore, the Levy flight step is incorporated to enhance the global search capability. The improved algorithm can improve the search speed and accuracy by adding memory and group interaction to the particle update formula to alleviate oscillation. Simulink modeling and simulation analysis reveals that, compared with traditional algorithms, the improved algorithm can identify the maximum power point of the photovoltaic array more rapidly and stably under static and dynamic shading conditions.

Trajectory tracking of QUAV based on cascade DRL with feedforward control

The adoption of advanced control strategies has become increasingly critical for the quadrotor unmanned aerial vehicle (QUAV). Deep reinforcement learning (DRL) stands at the forefront of these developments, showcasing significant utility, particularly in the realm of static target tracking for QUAVs. However, existing DRL methodologies encounter substantial challenges with latency issues when applied to trajectory tracking scenarios. This paper tackles this challenge by incorporating a feedforward technique, which empowers agents to analyze high order trajectory information. Initially, a cascade DRL controller for fixed point tracking is trained, where multiple agents are separately responsible for translation and rotation movement along each axis. Then, the controller is implemented for trajectory tracking by incorporating the feedforward information from the trajectory, which significantly solves the latency issue. Through extensive tracking demonstrations and quantitative analysis, the proposed integrated scheme demonstrates a significant enhancement in the trajectory tracking performance of QUAVs.

A novel global maximum power point tracking based on flamingo search algorithm for photovoltaic systems

Due to the high dependency of photovoltaic (PV) solar cell’s output on solar irradiance and, the ambient temperature. Maximum power point tracking (MPPT) algorithms are used extensively to operate the system at its full potentials. Moreover, being installed in outdoor spaces, PV modules are inevitably subjected to partial shading conditions, where different parts of the system are receiving different amounts of solar irradiance. In case of occurrence of partial shading conditions on a PV module that is equipped with bypass diodes, the power-voltage (P-V) curve will have multiple peaks. This multi-peak curve requires using an advanced algorithm which track the global maximum power point (GMPP) instead of being deceived and trapped in a local maximum power point. In this paper, the flamingo search algorithm (FSA) is adapted for GMPP tracking for a PV system under partial shading conditions. The FSA algorithm fetch for the GMPP by reading the PV panel power and setting accordingly the duty cycle of the buck converter. To investigate model validity, simulation is performed using the MATLAB/Simulink platform and results demonstrate good tracking performance and fast response that prove the robustness of the system against rapid variations in solar irradiance levels.

Research on localization control of magneto-controlled capsule endoscopy based on sensor array

Purpose To improve the localization accuracy of the magnetically controlled capsule endoscope (MCCE), a new localization method, based on the magnetic dipole model, is proposed, where the anti-disturbance permanent magnet (APM) is used as the source of stable magnetic field, thus reducing the interference of the geomagnetic field or the electric conductor magnetic field in the system. Design/methodology/approach The coupling magnetic force model between the APM and the capsule endoscope is established to obtain the magnetic force relationship and magnetic induction intensity. Along the three axes, magnetic induction intensity data are collected by a 3 × 3 sensor array composed of nine magnetic field intensity sensors, while the data are uploaded to the main computer by the STM32F103C8T6 control board over a ESP8266 WIFI module connection. Next, the axial magnetic induction intensity data are decoupled to obtain the measurement trajectory, whereas the error function is established based on the calculated trajectory parameters. Finally, the Levenberg–Marquardt (L-M) algorithm is used to solve the position information of the MCCE. Findings Experiments show that the average localization error of an MCCE in a straight and circular bend tube is 4.76 mm, whereas in a U-bend tube, it is 6.82 mm. Originality/value The optimized simulation value in the linear and bending environment is in good agreement with the experimental value, which verifies the accuracy of the MCCE localization system based on magnetic field sensor array, exhibiting good performance in localization and position tracking while providing a theoretical basis for the subsequent research.

Trajectory tracking control strategy for biaxial systems based on disturbance rejection and axis-coupled interpolation command modifier

This paper proposes a new control strategy for biaxial systems. It improves the trajectory tracking capabilities of biaxial system by enhancing the tracking performance of feed axes, disturbance rejection capabilities of feed axes, and consistency of dynamics between axes. First, the axis position controller is designed, it uses the feedforward controller and the state feedback controller to realize the active control of the low-order resonant modes of the mechanical system. In addition, a reduced-order extended state observer is designed to estimate the unknown state variables and disturbances in real time. The estimated disturbances will be compensated to the control loop to enhance the disturbance rejection capability of the axis. Second, the interpolation command modifier is designed. It is axis-coupled, which can not only compensate for the error caused by the axes themselves, but also compensate for the error caused by the coupling dynamics between axes. Finally, the effectiveness of the proposed integrated control strategy is experimentally verified. The experimental results show that the trajectory tracking accuracy of the biaxial systems and its robustness to load variations are both improved by using the proposed control strategy.

Adaptive Finite‐Time RL Control for Stochastic Non‐Linear Systems With Full State Constraints and Dead Zone Output

ABSTRACTIn this article, the finite‐time control problem of adaptive neural network (NN) reinforcement learning (RL) is investigated for the continuous time stochastic non‐linear systems with full state constraints and dead zone output. Firstly, the adaptive estimation and smooth approximation technique are introduced to solve the difficulty arising from the dead zone non‐linearity. Moreover, to overcome the problem of calculating the explosion caused by the repeated differentiation of the virtual control signals, a finite‐time command filter is constructed. Combining the backstepping technique and the identifier‐actor‐critic RL strategy, an adaptive neural finite‐time RL control scheme is proposed for the considered system by constructing the tangent‐type time‐varying barrier Lyapunov functions (BLFs), which optimizes the tracking performance while ensuring all states do not violate the constraints. Under the proposed control strategy, it is guaranteed that all signals are bounded in probability, and the output of the system can track the reference signal within a finite‐time. Finally, the simulation results verify the effectiveness of the proposed scheme.

Node Selection and Path Optimization for Passive Target Localization via UAVs

The performance of passive target localization is affected by the positions of unmanned aerial vehicles (UAVs) at a large scale. In this paper, to improve resource utilization efficiency and localization accuracy, the node selection problem and the path optimization problem are jointly investigated. Firstly, the target passive localization model is established and the Chan-based time difference of arrival (TDOA) localization method is introduced. Then, the Cramer–Rao lower bound (CRLB) for Chan-TDOA localization is derived, and the problems of node selection and path optimization are formulated. Secondly, a CRLB-based node selection method is proposed to properly divide the UAVs into several groups, localizing different targets, and a CRLB-based path optimization method is proposed to search for the optimal UAV position configuration at each time step. The proposed path optimization method also effectively handles no-fly-zone (NFZ) constraints, ensuring operational safety while maintaining optimal target tracking performance. Also, to improve the efficiency of path optimization, particle swarm algorithm (PSO) is applied to accelerate the searching process. Finally, numerical simulations are performed to verify the validity and effectiveness of the proposed methods in this paper.

A model-free decoupled and robust repetitive controller for trajectory tracking performance of a three-axis parallel mechanism

A model-free decoupled and robust repetitive controller for trajectory tracking performance of a three-axis parallel mechanism

Feedback linearization-based model reference adaptive control for multirotor UAVs under uncertainties from fuel and payload

This paper proposes a feedback linearization-based model reference adaptive control (FL-MRAC) scheme to handle a multirotor unmanned aerial vehicle (UAV) equipped with a gasoline-battery-type hybrid system under uncertainties. The gasoline-battery-type hybrid propulsion systems are increasingly used to increase operational time. This multirotor system suffers from uncertainties that may originate from offcentered payloads, or fuel consumption during flight. These uncertainties can change model parameters such as mass, moment of inertia, and center of gravity. Therefore, a new control strategy is applied using feedback linearization as a baseline design augmented with a model reference adaptive control strategy on a multirotor UAV. To formulate the structure of uncertainties, the mathematical model of the multirotor UAV with fuel and payload is constructed considering the effect of the mass change rate and the off-diagonal inertia tensor. FL-MRAC improves the performance of attitude and altitude tracking using the closed-loop reference model under model parameter uncertainties and time-varying nature. To guarantee stability, the uniform ultimate boundedness of the closed-loop system is proved considering the time-varying parameters. Numerical simulations are performed using the root-mean-square error and the fast Fourier transform. In Case 1, FL-MRAC with a closed reference model exhibits a more accurate tracking performance and transient response than FL-MRAC with an open reference model under the uncertainties of time-varying parameter uncertainties. In Case 2, the proposed method of tracking performance demonstrates an improved attitude and altitude tracking performance compared to sliding mode control and nonlinear disturbance observer under time-varying parameter uncertainties and external disturbances due to fuel sloshing effect.

Related Keyframe Optimization Gaussian–Simultaneous Localization and Mapping: A 3D Gaussian Splatting-Based Simultaneous Localization and Mapping with Related Keyframe Optimization

Simultaneous localization and mapping (SLAM) is the basis for intelligent robots to explore the world. As a promising method for 3D reconstruction, 3D Gaussian splatting (3DGS) integrated with SLAM systems has shown significant potential. However, due to environmental uncertainties, errors in the tracking process with 3D Gaussians can negatively impact SLAM systems. This paper introduces a novel dense RGB-D SLAM system based on 3DGS that refines Gaussians through sub-Gaussians in the camera coordinate system. Additionally, we propose an algorithm to select keyframes closely related to the current frame, optimizing the scene map and pose of the current keyframe. This approach effectively enhances both the tracking and mapping performance. Experiments on high-quality synthetic scenes (Replica dataset) and low-quality real-world scenes (TUM-RGBD and ScanNet datasets) demonstrate that our system achieves competitive performance in tracking and mapping.

A contribution to 3D tracking of deformable bubbles in swarms using temporal information

Reliable Lagrangian 3D tracking of individual bubble swarm members allows a deeper understanding of hydrodynamic bubble–bubble interactions and their collective rise. For multi-view measurements, we have recently developed such a tracking method (Hessenkemper in Int J Multiph Flow 179:104932, 2024), which is able to track deformable bubbles with low to moderate view obstruction through the bubbles to each other. In the present work, we aim to further enhance the 3D tracking performance by additionally incorporating 2D temporal information in the form of previously established 2D tracks in each camera view. The new 3D tracking method is able to disambiguate cross-view object associations at each time step by using the 2D track information accumulated over time. In addition, the temporal information from multiple 2D domains is used in two post-processing steps to improve the completeness of established 3D trajectories. Compared to the previous 3D tracking method, the extended 3D tracking framework shows noticeable improvements in tracking ability, accuracy, and completeness of trajectories.Graphical abstract