Tracking Scheme Research Articles

Path tracking and energy efficiency coordination control strategy for skid-steering unmanned ground vehicle

The skid steering unmanned ground vehicle (SUGV) plays an important role in extremely harsh environments. Improving the autonomous control capability and energy efficiency of SUGV is urgently needed. This article presents a skid steering-based path tracking control strategy. In the upper controller, an improved model-free sliding mode controller (APMS) is used to calculate the yaw moment for tracking control. On the lower controller, the Snow Ablation Optimizer (SAO) is used to distribute the output torque of the drive motors, taking longitudinal force, yaw moment and energy consumption into account. Finally, the designed controller is validated through simulation under different operating conditions. The results show that the proposed coordination controller achieves good control performance, increases energy efficiency and at the same time ensures tracking accuracy.

Tracking modifications to implementation strategies: a case study from SNaP - a hybrid type III randomized controlled trial to scale up integrated systems navigation and psychosocial counseling for PWID with HIV in Vietnam

IntroductionEvaluation of implementation strategies is core to implementation trials, but implementation strategies often deviate from the original plan to adjust to the real-world conditions. The optimal approach to track modifications to implementation strategies is unclear, especially in low-resource settings. Using data from an implementation trial for people who inject drugs (PWID) with HIV in Vietnam, we describe the tracking of implementation strategy modifications and present findings of this process.MethodsSNaP (Systems Navigation and Psychosocial Counseling) is a hybrid type-III effectiveness-implementation randomized controlled trial aiming to scale up the evidence-based intervention, integrated systems navigation and psychosocial counseling, for PWID with HIV in Vietnam. Forty-two HIV testing sites were randomized 1:1 to a standard or tailored arm. While the standard arm (SA) received a uniform package of strategies, implementation strategies for the tailored arm (TA) were tailored to address specific needs of each site. The central research team also met monthly with the TA to document how their tailored strategies were implemented over time. Five components were involved in the tracking process: describing the planned strategies; tracking strategy use; monitoring barriers and solutions; describing modifications; and identifying and describing any additional strategies.ResultsOur approach allowed us to closely track the modifications to implementation strategies in the tailored arms every month. TA sites originally identified 27 implementation strategies prior to implementation. During implementation, five strategies were dropped by four sites and two new strategies were added to twelve sites. Modifications of five strategies occurred at four sites to accommodate their changing needs and resources. Difficulties related to the COVID-19 pandemic, low number of participants recruited, high workload at the clinic, lack of resources for HIV testing and high staff turnover were among barriers of implementing the strategies. A few challenges to tracking modifications were noted, including the considerable amount of time and efforts needed as well as the lack of motivation from site staff to track and keep written documentations of modifications.ConclusionsWe demonstrated the feasibility of a systematic approach to tracking implementation strategies for a large-scale implementation trial in a low-resource setting. This process could be further enhanced and replicated in similar settings to balance the rigor and feasibility of implementation strategy tracking. Our findings can serve as additional guidelines for future researchers planning to report and track modifications to implementation strategies in large, complex trials.Trial registration: clinicaltrials.gov ID: NCT03952520 (first posted 2019-05-16).

A Maximum Power Point Tracking (MPPT) Strategy Based on Harris Hawk Optimization (HHO) Algorithm

To address the multi-peak characteristics of the P-U output characteristic curve when the photovoltaic array is partially shaded, a Maximum Power Point Tracking (MPPT) method combining Harris Hawk optimization, and a variable step conductance increment method is proposed in this article. Although the Harris Hawk Optimization algorithm has advantages in optimizing multi-modal P-U curves, when the lighting conditions suddenly change, the output characteristic curve will undergo drastic changes, causing the algorithm to oscillate repeatedly at the maximum power point. Moreover, due to its inherent limitations, the Harris Hawk algorithm has a low convergence accuracy, slow tracking speed, and is prone to getting trapped in a local optimum. In order to compensate for the shortcomings of the Harris Hawk Optimization algorithm, the variable step conductance increment method is switched for local exploration to improve the algorithm’s ability to escape from the local optimal solution and reduce power oscillation. Finally, the MPPT control of the proposed strategy was simulated and verified on the MATLAB/Simulink simulation (2021) platform. In the MPPT test experiment, the proposed composite algorithm was applied to simulate and verify uneven irradiance conditions.

Long-Range Three-Dimensional Tracking of Nanoparticles Using Interferometric Scattering Microscopy.

Tracking nanoparticle movement is highly desirable in many scientific areas, and various imaging methods have been employed to achieve this goal. Interferometric scattering (iSCAT) microscopy has been particularly successful in combining very high spatial and temporal resolution for tracking small nanoparticles in all three dimensions. However, previous works have been limited to an axial range of only a few hundred nanometers. Here, we present a robust and efficient measurement and analysis strategy for three-dimensional tracking of nanoparticles at high speed and with nanometer precision. After discussing the principle of our approach using synthetic data, we showcase the performance of the method by tracking gold nanoparticles with diameters ranging from 10 to 80 nm in water, demonstrating an axial tracking range from 4 μm for the smallest particles up to over 30 μm for the larger ones. We point out the limitations and robustness of our system across various noise levels and discuss its promise for applications in cell biology and material science, where the three-dimensional motion of nanoparticles in complex media is of interest.

Adaptive Free‐Will Arbitrary Time Tracking of a Class of Uncertain SISO Nonlinear Systems

ABSTRACTThis paper investigates the free‐will arbitrary time (FWAT) tracking problem for a class of single‐input single‐output (SISO) strict‐feedback nonlinear systems without/with parametric uncertainties. First, we design an FWAT backstepping tracking controller for strict‐feedback nonlinear systems without uncertainties in a recursive manner. It is shown that the proposed virtual controllers and their derivatives are continuously differentiable for the backstepping design, and the tracking error converges to zero within the pre‐specified settling time independent of design parameters and initial conditions and remains at zero after the pre‐specified settling time. Second, we address the adaptive FWAT tracking problem of SISO strict‐feedback nonlinear systems with parametric uncertainties in the practical stability sense. The continuously differentiable and nonsingular time‐varying adjustment functions for practical FWAT tracking are developed to transform error variables for the recursive backstepping design. Then, the adaptive FWAT backstepping tracking design and stability analysis strategy are established to ensure practical FWAT convergence within the pre‐specified settling time independent of design parameters and initial conditions. The designed adaptive virtual and actual controllers are continuous for all . Furthermore, a practical adaptive prescribed‐time tracking scheme is provided using the proposed practical adaptive FWAT tracking scheme. Finally, the effectiveness of the theoretical approaches is confirmed through a simulation example and an experimental result.

Adaptive asymptotic tracking control of constrained nonlinear MIMO systems subject to unknown hysteresis input: A novel network-based strategy

This paper presents a referential adaptive asymptotic tracking control scheme for nonlinear multi-input and multi-output (MIMO) systems with time-varying full-state constraints and unknown hysteresis input. In order to achieve good asymptotic tracking effect, a zero-limit positive continuous function is introduced into the adaptive backstepping design process while thoroughly considering the impact of disturbance-like terms on the tracking effect. Additionally, a new variable is also imported to replace the reciprocal of unknown coefficient of the Bouc–Wen hysteresis model. Then, a new adaptive law about the new variable is added by combining the positive function, which can not only lessen the impact of the unknown hysteresis input on the tracking effect, but also avoid the “singularity” problem. Aiming at the time-varying full-state constraints, a appropriate time-varying log-type barrier Lyapunov function (TLBLF) is constructed to ensure that all the states of the system are restricted within the constraint scope. Finally, a significant adaptive asymptotic tracking scheme is designed based on multi-dimensional Taylor network (MTN) approximation technology. Apart from achieving high-precision asymptotic tracking performance, the proposed scheme ensures the boundedness of all signals of the closed-loop system without violating the full-state constraints. And, three simulations are given to verify the effectiveness of the scheme.

Flocking analysis and control of a nonlinear collective migration model

Abstract In this paper, we study a nonlinear collective migration model with the Cucker–Smale type weight, the nonlinear velocity coupling and the distributed network. Finite-time flocking tracking can be achieved by the alignment force gathering agents and the tracking force matching target. A trade-off existing between the two forces is established by the tracking strategy that can be viewed as a control. When the strategy is time-invariant, finite-time flocking tracking would occur for any initial state under the long-range weight, only for partial initial state under the short-range weight. An invariant set of the system is proposed and proved to be an attractive domain of the flocking state. Then two time-varying strategies, the average strategy and the maximum strategy, are designed to overcome the constraint of initial state under the short-range weight. The average strategy has to mobilize all agents simultaneously, but only causes once switch. The maximum strategy only mobilizes the agent with the largest velocity deviation, but produces more switches. Several numerical simulations are provided to observe the effects of the time-invariant and time-varying strategies.

Investigation of laser phase noise tolerance in simplified coherent PON with subcarrier multiplexing

In this paper, we experimentally investigate the laser phase noise tolerance for a 100 Gb/s/λ coherent passive optical network (PON) with simplified optical network units (ONUs) transceivers when distributed feedback (DFB) laser is used as the local oscillator (LO). In the proposed scheme, the digitally subcarrier-multiplexed (DSCM) signals are generated in the coherent transmitter at the optical line terminal (OLT) side based on Alamouti encoding in two polarizations and received by a single-polarization heterodyne receiver at the ONU side. The experimental results show that the power budget can achieve 34.2 dB and 33.9 dB after a 25 km standard single-mode fiber (SSMF) link when external cavity laser (ECL) and DFB lasers are used as the LOs at the ONU side. Polarization and carrier phase tracking schemes for the DSCM systems based on frequency domain pilot tones were used in digital signal process (DSP). At the same time, the frequency offset estimation and phase noise compensation algorithms are conducted using residual carriers. By choosing an appropriate width for the low-pass filter, the optimal performance for the phase noise compensation can be achieved. The pre-equalization operation at the OLT side is also verified for the subcarrier-multiplexed signals to provide better and flatter performances.

A Decoupled Modulation Strategy for Constant Voltage/Current Wireless Charging System Under High Coils Misalignment

ABSTRACTOutput fluctuation and lower transmission efficiency under load and mutual inductance variation are hot concerned in the wireless charging system for power batteries. In this paper, a decoupled tracking strategy is proposed to realize constant voltage (CV) or current (CC) output and high transmission efficiency. The circuit model of series–series compensation network is built, and corresponding transmission characteristics can be derived. First, the load and mutual inductance value can be estimated by measuring the primary electrical information, including the inverter output voltage, current, and the phase error between them. Then load‐independent voltage or current output characteristics under coils misalignment can be maintained by tracking the optimal switching frequency of inverter part. In addition, the value of output voltage or current can be adjusted flexibly for different operation occasions by the employment of pulse‐width modulation. As a result, the load‐independent output characteristics realization and output adjustment can be decoupled. Based on only the primary measuring information, the CV/CC output can be maintained via the proposed hybrid control strategy when the coils misalignment and load variation happen, increasing the robustness of whole wireless power transfer (WPT) system. Furthermore, the soft switching of all power switches in the inverter part can be realized to reduce the switching loss. Compared with previous research, the proposed wireless charging system has a higher power density and a simplified control strategy. Finally, an experimental platform with 60‐V charging voltage and 3.6‐A charging current is built to verify the feasibility of the proposed sys‐tem and control method.

Event-triggered finite-time adaptive neural network control for quadrotor UAV with input saturation and tracking error constraints

In this article, an event-triggered finite-time adaptive neural network control tracking strategy is proposed for quadrotor unmanned aerial vehicle (UAV) with input saturation and error constraints. Firstly, the radial basis function neural networks (RBFNNs) are adopted to identify the unknown uncertainty of quadrotor UAV model from the installation errors, gyroscope errors and so on. An auxiliary equation is constructed to deal with input physical saturation from the actuator motors. Additionally, by combining the performance function and error transformation, the issue of error constraint is solved. Based on the Lyapunov stability theory and event-triggered mechanisms, a finite-time adaptive neural network scheme is developed to ensure that the closed-loop quadrotor UAV system is semi-globally practically finite-time stable, and save the computation, resources, and transmission load. Finally, the simulation results illustrate the good tracking performance of quadrotor UAV by using the proposed control strategy.

Distributed robust tracking control for multiple unmanned aerial vehicles: theory and experimental verification

The distributed trajectory tracking problem for multiple UAVs under unknown external disturbance is investigated. A new nonlinear robust trajectory tracking strategy for multiple UAVs is proposed. The dynamic model for the UAVs' formation is illustrated in the Tangent frame. For the trajectory tracking problem, a Robust formation tracking control strategy based on robust integral of the signum of error (RISE) is designed to compensate for the effects of unknown external disturbances, which improves the robustness of the UAVs' formation system. Based on the Lyapunov stability analysis, it is proved that the global asymptotic convergence of the coordination errors and the semi-global asymptotic convergence of the UAVs' position tracking errors are achieved. The proposed formation control strategy is validated via real-time experiments that are performed on the self-build UAVs' formation flight control testbed. Flights without wind disturbance and flights with disturbance are all performed. The performance comparison experiment with the conventional sliding mode control algorithm is performed. The experimental results show that the designed control strategy can achieve good coordinated trajectory tracking of multiple UAVs, and has better control performance compared with normal sliding mode control law.

Coordinated strategy for path tracking and dynamic stability considering extreme obstacle avoidance in distributed drive electric vehicles

PurposeThis research aims to investigate the trajectory tracking problem for a four-wheel independent drive autonomous vehicle (4WID) and propose an integrated, coordinated control strategy to address the mutual interference between trajectory tracking and stability control in extreme cases.Design/methodology/approachThe authors establish an adaptive preview model that modifies the preview distance based on vehicle speed. They utilize a three-degrees-of-freedom vehicle model and employ model predictive control to calculate the necessary front wheel angle for trajectory tracking. In terms of longitudinal control, a longitudinal coordinated control mechanism is established to achieve the two conflicting objectives of trajectory tracking accuracy and dynamic stability through early deceleration. A stability controller based on sliding mode control (SMC) is designed, considering tire constraints and tracking the optimal yaw angle and sideslip angle. Furthermore, a lateral coordinated control strategy is developed, considering the weight coefficient of stability control, and the yaw moment is calculated and distributed based on the vehicle torque requirements.FindingsThe proposed integrated, coordinated control strategy successfully addresses the mutual interference between trajectory tracking and stability control in extreme cases for the 4WID vehicle. The strategy achieves trajectory tracking accuracy, dynamic stability and reduced energy consumption while taking into account tire constraints.Originality/valueWe have proposed a cooperative control strategy for the trajectory tracking problem of autonomous driving vehicles. This strategy is different from previous methods in that we have taken into account the integrated dynamic control in both longitudinal and lateral directions, balancing the conflicting control requirements and reducing energy consumption, improving trajectory tracking accuracy and vehicle dynamic stability. We have verified the feasibility of this strategy through joint simulation under different driving conditions.

Optimized Energy Management System for Wind Lens-Enhanced PMSG Utilizing Zeta Converter and Advanced MPPT Control Strategies

This paper presents the design and analysis of an efficient energy management system for a wind lens integrated with a permanent magnet synchronous generator (PMSG) and a zeta converter. The wind lens, a ring-shaped structure encircling the rotor, enhances the turbine’s capability to capture wind energy by increasing the wind influx through the turbine. In the contemporary wind energy sector, PMSGs are extensively employed due to their superior performance characteristics. This study integrates a 1 kW PMSG system with a wind lens to optimize power extraction from the wind energy conversion system (WECS) under varying wind speeds. A comparative analysis of different control strategies for maximum power point tracking (MPPT) is conducted, including the incremental conductance (INC) method and the perturb and observe (P&O) method. The performance of the MPPT controller integrated with the wind lens-based PMSG system is assessed based on output DC voltage and power delivered to the load. To evaluate the overall effectiveness of these control strategies, both steady-state voltage and dynamic response under diverse wind conditions are examined. The system is modeled and simulated using the MATLAB R2023a/Simulink 9.1 software, and the simulation results are validated to demonstrate the efficacy of the proposed energy management system.

Data-Driven Model Predictive Control for Redundant Manipulators With Unknown Model.

The tracking control of redundant manipulators plays a crucial role in robotics research and generally requires accurate knowledge of models of redundant manipulators. When the model information of a redundant manipulator is unknown, the trajectory-tracking control with model-based methods may fail to complete a given task. To this end, this article proposes a data-driven neural dynamics-based model predictive control (NDMPC) algorithm, which consists of a model predictive control (MPC) scheme, a neural dynamics (ND) solver, and a discrete-time Jacobian matrix (DTJM) updating law. With the help of the DTJM updating law, the future output of the model-unknown redundant manipulator is predicted, and the MPC scheme for trajectory tracking is constructed. The ND solver is designed to solve the MPC scheme to generate control input driving the redundant manipulator. The convergence of the proposed data-driven NDMPC algorithm is proven via theoretical analyses, and its feasibility and superiority are demonstrated via simulations and experiments on a redundant manipulator. Under the drive of the proposed algorithm, the redundant manipulator successfully carries out the trajectory-tracking task without the need for its kinematics model.

Modelling of Control Strategy for Sun Tracking of Concentrated Photovoltaic (CPV) Systems and Performance Analysis of Optical Losses Due to Tracking Errors.

Abstract Among the wide range of solar energy technologies, CPV remains a promising technology worldwide. The photovoltaic concentrator maximizes its efficiency through the use of optical concentration systems. But since optical concentration depends on direct solar radiation, maximizing the quantity of useful solar radiation received requires using a solar tracker that tracks the direction of the sun. To maximize collector efficiency, it’s crucial to choose tracking systems that align with the optical concentration factor. Inadequate focusing of direct solar radiation on the concentrator’s receiver can result in high optical losses, regardless of quality. This paper focuses on improving the accuracy and lowering the cost of the tracker trajectory generation strategy. A sun tracker simulator is first developed, allowing for the investigation of the impact of sun tracking performance a traditional control strategy is initially implemented that uses an astronomical calculation to follow the path of the sun. This strategy uses a sun position algorithm from the National Renewable Energy Laboratory to generate the tracker trajectory and track the sun’s path. When tested on the simulator, as a result, this strategy demonstrated a level of accuracy that ensured a good balance between intricacy and accuracy. Then, An investigation study was conducted to determine how this solar tracker’s precision might affect its optical efficiency. the optical losses resulting from the tracking were calculated using the provided location, The optical loss calculated for this collector’s maximum tracking error is approximately 9%. The research methodology showed that the tracker precision satisfied the requirements of the optical system under study.

UAT:Unsupervised object tracking based on graph attention information embedding

An excellent unsupervised tracker includes a powerful base tracker and an effective unsupervised tracking strategy. However, most base trackers lack internal feature representations for information embedding processes. Most unsupervised trackers are not robust enough in complex environments and lack an effective template update strategy. We propose an unsupervised object tracking based on graph attention information embedding (UAT) to solve the above problems. UAT combines graph attention mechanism with multi-scale features to construct a multi-scale graph attention module (MGA). MGA module dynamically and efficiently completes the information embedding between the template branch and the search area branch. The response map obtained by fusing the feature maps of the two branches is more informative about the location of the target. An attention based information reinforcement update module (RUM) improves the robustness of the tracker. RUM enhances the representation of the feature map in both the spatial dimension and the channel dimension. Template features are also updated indirectly through information transfer between the two branches. RUM suppresses background interference and improves network perception during tracking. Experiments on challenging benchmarks such as VOT2018, VOT2019, TrackingNet, OTB100, LaSOT and UAV123 demonstrate that the proposed UAT achieves state-of-the-art performance in unsupervised trackers.

Modifying Sequential Monte Carlo Optimisation for Index Tracking to Allow for Transaction Costs

Managing a portfolio whose value closely tracks an index by trading only in a subset of the index constituents involves an NP-hard optimisation problem. In the prior literature, it has been suggested that this problem be solved using sequential Monte Carlo (SMC, also known as particle filter) methods. However, this literature does not take transaction costs into account, although transaction costs are the primary motivation for attempting to replicate the index by trading in a subset, rather than the full set of index constituents. This paper modifies the SMC approach to index tracking to allow for proportional transaction costs and implements this extended method on empirical data for a variety stock indices. In addition to providing a more practically useful tracking strategy by allowing for transaction costs, we find that including a penalty for transaction costs in the optimisation objective can actually lead to better tracking performance.

Comparative analysis of tracking and behavioral patterns between wild-type and genetically modified fruit flies using computer vision and statistical methods

Collective animal behavior occurs in groups and swarms at almost every biological scale, from single-celled organisms to the largest animals on Earth. The intriguing mysteries behind these group behaviors have attracted many scholars, and while it is known that models can reproduce qualitative features of such complex behaviors, this requires data from real animals to demonstrate, and obtaining data on the exact features of these groups is tricky. In this paper, we propose the Hidden Markov Unscented Tracker (HMUT), which combines the state prediction capability of HMM and the high-precision nonlinear processing capability of UKF. This prediction-driven tracking mechanism enables HMUT to quickly adjust tracking strategies when facing sudden changes in target motion direction or rapid changes in speed, reducing the risk of tracking loss. Videos of fruit fly swarm movement in an enclosed environment are captured using stereo cameras. For the captured fruit fly images, the thresholded AKAZE algorithm is first used to detect the positions of individual fruit flies in the images, and the motion of the fruit flies is modeled using a multidimensional hidden Markov model (HMM). Tracking is then performed using the Unscented Kalman Filter algorithm to obtain the flight trajectories of the fruit flies in two camera views. Finally, 3D reconstruction of the trajectories in both views is achieved through polar coordinate constraints, resulting in 3D motion data of the fruit flies. Additionally, the efficiency and accuracy of the proposed algorithm are evaluated by simulating fruit fly swarm movement using the Boids algorithm. Finally, based on the tracked fruit fly flight data, behavioral characteristics of the fruit flies are analyzed from two perspectives. The first is a statistical analysis of the differences between the two behaviors. The second dimension involves clustering trajectory similarity using the DTW method based on fruit fly flight trajectories, further analyzing the similarity within clusters and differences between clusters.

Continuation of nonlinear normal modes using reduced-order models based on generalized characteristic value decomposition

AbstractOver the past two decades, data-driven reduced-order modeling (ROM) strategies have gained significant traction in the nonlinear dynamics community. Currently, several challenges in physical interpretation and data availability remain overlooked in current methodologies. This work proposes a novel ROM methodology based on a newly proposed generalized characteristic value decomposition (GCVD) to address these obstacles. The GCVD-ROM approach proposes a new perspective toward data-driven ROMs via characterization of the dynamics before any ROM considerations are made. In doing so, a significant degree of versatility is inherited in the GCVD-ROM strategy, allowing our models to reproduce the full-scale dynamics in different regions of the parameter space at the cost of a single training data set. Our approach utilizes computationally efficient free-decay data sets alongside a windowed-decomposition scheme, allowing us to extract energy-dependent modal structures for use in model-order reduction. This is accomplished using the physically insightful characteristic values provided by the GCVD, which are shown to be directly related to the system poles at a particular response amplitude. This natural metric, paired with a resonance tracking scheme, allows us to address the difficulties associated with physical interpretation and data availability without sacrificing the convenient aspects of linear projection-based model order reduction. A computational framework for the continuation and bifurcation analysis using linear projection-based ROMs is also presented, permitting us to deploy rigorous analysis and bifurcation studies to verify that our ROMs reproduce the intrinsic complexity of full-scale systems. A detailed walk-through of the GCVD-ROM approach is demonstrated on a simple system where important practical considerations and implementation details are discussed using a concrete example. The discretized von Kármán beam and shallow arch partial differential equations are also used to explore complicated scenarios involving modal coupling across disparate time scales and internal resonances.

A Deep Reinforcement Learning Approach to Injection Speed Control in Injection Molding Machines with Servomotor-Driven Constant Pump Hydraulic System

The control of the injection speed in hydraulic injection molding machines is critical to product quality and production efficiency. This paper analyzes servomotor-driven constant pump hydraulic systems in injection molding machines to achieve optimal tracking control of the injection speed. We propose an efficient reinforcement learning (RL)-based approach to achieve fast tracking control of the injection speed within predefined time constraints. First, we construct a precise Markov decision process model that defines the state space, action space, and reward function. Then, we establish a tracking strategy using the Deep Deterministic Policy Gradient RL method, which allows the controller to learn optimal policies by interacting with the environment. Careful attention is also paid to the network architecture and the definition of states/actions to ensure the effectiveness of the proposed method. Extensive numerical results validate the proposed approach and demonstrate accurate and efficient tracking of the injection velocity. The controller’s ability to learn and adapt in real time provides a significant advantage over the traditional Proportion Integration Differentiation controller. The proposed method provides a practical solution to the challenge of maintaining accurate control of the injection speed in the manufacturing process.