Robust Tracking Research Articles

Dynamic Modeling and Robust Trajectory Tracking Control of a Hybrid Propulsion-Based Small Underwater Robot

This paper proposes a hybrid propulsion-based small underwater robot for robust trajectory tracking control in a harsh and complex underwater environment. The robot is equipped with a Coanda-effect jet thruster and a pair of propeller-based reconfigurable magnetic-coupling thrusters, allowing it to traverse safely in confined or cluttered spaces as well as cruise efficiently in the open water. To investigate the robot dynamic modeling, we first formulated its simplified mathematical model and estimated the hydrodynamic coefficients by performing the planar motion mechanism using CFD (computational fluid dynamics) simulation. Then, a double-loop trajectory tracking control architecture was designed considering the model uncertainties and environmental disturbances. Based on Lyapunov theory, the outer-loop kinematic control produces the virtual velocity command, while the inner-loop dynamic control adopts the full-state feedback L1-adaptive control to match the command. The asymptotic convergence of the tracking errors and the stability of the whole closed-loop system are guaranteed. Finally, comparative simulations in the presence of unknown disturbances and the variation of model parameters were carried out to verify the robustness of our proposed trajectory tracking control, which is also suitable for the separated son robots.

Robust visual tracking with extreme point graph-guided annotation: Approach and experiment

Recent advancements in the field of visual tracking have been propelled by the amalgamation of Siamese networks and region proposal networks, which have demonstrated excellent competitive accuracy while remaining computationally efficient. However, these approaches often suffer from excessive parametric redundancy and additional computational costs owing to the use of anchor boxes or multiscale pyramids, leaving room for improvement in their performance. In this study, a novel extreme point graph-guided tracking approach called SiamEXTR is presented, which tracks generic target objects by detecting five keypoints, including four target-specific extreme points (i.e., the topmost, bottommost, leftmost, and rightmost points) and a center point, without requiring region classification or bounding box regression. To enhance the robustness of the extreme point detection, a new oriented pooling strategy called extreme-pooling is proposed, which captures more recognizable discriminative global and local information to help a pixel predict its category. In addition, a U-shaped backbone network is designed to preserve fine-grained visual details and stronger semantic information at high-resolution, ensuring that the detection granularity of the extreme point graph is as close to the subpixel-level as possible. Based on the detected extreme point graph, the proposed approach not only generates axis-aligned bounding boxes for object annotation, but also provides more accurate octagonal object segmentation masks through a simple approximation strategy. Without bells and whistles, extensive experiments and comparisons on several authoritative large-scale benchmark datasets demonstrated that the SiamEXTR tracker consistently achieved competitive performance, with running speeds significantly exceeding 140 frames per second. The authors hope that the concept behind this approach will serve as a new baseline and promote further development of the visual tracking community.

High-order implicit shock tracking boundary conditions for flows with parametrized shocks

High-order implicit shock tracking (fitting) is a class of high-order, optimization-based numerical methods to approximate solutions of conservation laws with non-smooth features by aligning elements of the computational mesh with non-smooth features. This ensures the non-smooth features are perfectly represented by inter-element jumps and high-order basis functions approximate smooth regions of the solution without nonlinear stabilization, which leads to accurate approximations on traditionally coarse meshes. In this work, we introduce a robust implicit shock tracking framework specialized for problems with parameter-dependent lead shocks (i.e., shocks separating a farfield condition from the downstream flow), which commonly arise in high-speed aerodynamics and astrophysics applications. After a shock-aligned mesh is produced at one parameter configuration, all elements upstream of the lead shock are removed and the nodes on the lead shock are positioned for new parameter configurations using the implicit shock tracking solver. The proposed framework can be used for most many-query applications involving parametrized lead shocks such as optimization, uncertainty quantification, parameter sweeps, “what-if” scenarios, or parameter-based continuation. We demonstrate the robustness and flexibility of the framework using a one-dimensional space-time Riemann problem, and two- and three-dimensional supersonic and hypersonic benchmark problems.

Using control theory for preventing induced seismicity due to fluid injections in a reservoir

Deep Geothermal Energy, Hydrogen Underground Storage and Carbon Capture Utilization and Storage are promising techniques to satisfy the large-scale needs of the energy sector. However, they all depend on the injection of fluids into the earth’s crust, which, in turn, can cause earthquakes [1, 2]. Earthquakes nucleate when large amounts of elastic energy, stored in the earth's crust, are suddenly released due to abrupt sliding over faults. Fluid injections can create new, or reactivate existing, seismogenic faults and, therefore, cause earthquakes [2, 3].
 More recently, new results for controlling the earthquake instability of a single, natural, mature seismic fault were obtained [4, 5, 6, 7, 8]. These works were based on the mathematical control theory to stabilize and control the underlying physical system, which is underactuated, strongly non-linear and uncertain. The above works could inspire new approaches for preventing induced seismicity too [4, 9].
 In this work, a 3D diffusion equation is considered to model induced seismicity due to fluid injections in a geological reservoir. A robust tracking control is then designed to force the seismicity rate to follow a desired reference, minimize induced seismicity and assure fluid circulation. The designed regulator ensures the control task despite system uncertainties and external perturbations. Simulations of the process are presented to show the reliability and performance of the control approach.

A Robust Adaptive Control Strategy for Electro-hydraulic Servo Based on Friction Compensation

Background: The electro-hydraulic servo system has the advantages of high stiffness, fast response speed, high precision and power-to-weight ratio. Objective: In this patent, an adaptive robust control strategy based on friction compensation is proposed for insufficient control accuracy due to a large number of nonlinearities and uncertainties in the system. Methods: In this patent, the mathematical relationship between the physical quantities of the valve-controlled cylinder is analyzed. Then the mathematical model of valve controlled cylinder considering the main nonlinearity and uncertainty of the system is established. The fuzzy proportion-integral-derivative, deterministic robust, and adaptive robust single-cylinder trajectory tracking controllers are designed and the tracking performance of different reference trajectories is analyzed by MATLAB/Simulink software. Results: In this patent, all the three controllers can track the reference trajectories smoothly. Adaptive robust control strategy can make the uncertain parameters of system converge and approach truth value. Conclusion: In this patent, the adaptive robust control strategy has the function of online parameter adjustment. When tracking accuracy is taken as the index, its tracking effect is better than fuzzy proportion-integral-derivative and deterministic robust.

Optimal oxygen excess ratio tracking of proton exchange membrane fuel cell with uncertainty based on robust model predictive control strategy

AbstractTo address the challenge of tracking the optimal oxygen excess ratio (OER) in the cell stack cathode of the proton exchange membrane fuel cell (PEMFC), which is subject to norm‐bounded parameter uncertainty and frequent external perturbations, this manuscript provides a novel robust model predictive control strategy with a state feedback control structure and hard constraints. We aim to minimize the tracking error of OER while maximizing the net power under varying operational conditions of the PEMFC. Initially, a control‐oriented model for the air supply system is built and its accuracy is validated by comparing it with experimental results. Next, we construct the discrete state‐space equations for optimal control and employ the linear matrix inequality (LMI) method to transform the problem of difficult min‐max optimization solutions into a convex optimization problem with LMI constraints. Finally, the effectiveness of the proposed control algorithm is validated through simulation. The results demonstrate the strong robustness and superiority of the proposed controller against uncertainties in plant parameters for robust OER trajectory tracking. It improves the transient performance through robust precision tracking applications, which will eventually accelerate PEMFC commercialization.

Robust Accurate Lane Detection and Tracking for Automated Rubber-Tired Gantries in a Container Terminal

Lane detection and tracking technique is the autonomous driving basis for Rubber-Tired Gantries (RTGs), vital to the automation and intelligence updating of man-driven container terminals. However, the existing lane detection methods developed for common road scenarios cannot meet the high-precision and robust all-weather requirements of RTG autonomous driving. In this article, we propose an Adaptive Edge-based Lane Detection and Tracking method considering RTG lanes’ characteristics in this paper. First, the candidate edges of lane lines are detected and paired based on the enhanced gradient features. Next, inverse perspective mapping is employed to search the right edges, followed by an adaptive sliding-window method. Ultimately, we develop an adaptive Kalman filter to track lane lines robustly, detecting confidence weighting by relaxing the constraint of lane line width. The proposed method is tested in an actual container yard, the lane centerline’s average position error is 2.051 pixels, and the detection success rate is close to 100%.

A Two-Degree-of-Freedom PID Integral Super-Twisting Controller Based on Atom Search Optimizer Applied to DC-DC Interleaved Converters for Fuel Cell Applications

This paper focuses on the real-time implementation of an optimal high-performance control applied to an interleaved nonisolated DC/DC converter designed for fuel cell applications. Three-phase interleaved boost converters are utilized to minimize input current undulation, increase efficiency, and provide a high output voltage in order to ensure the performance of the FC stack. The proposed control strategy contains an outer loop that generates the reference current based on a two-degree-of-freedom PID controller. This controller provides a robust setpoint tracking and disturbance rejection, which improves the system’s response and efficiency. A fast inner regulation loop based on a super-twisting integral sliding mode (STISM) algorithm is developed to achieve a fixed converter output voltage, equitable phase current sharing, and fast regulation against load disturbances in failure operation. The STISM algorithm exhibits a rapid convergence property of the sliding mode and effectively avoids the chattering phenomena frequently observed in conventional sliding modes. The proposed controller’s gains are determined using the atom search optimization algorithm, which ensures exceptional reliability and a high degree of robustness and stability of the controllers under a variety of operational conditions. This method is inspired from the behavior of atoms and their electrons during the excitation process leading to a one-of-a-kind optimization technique which contributes to the controller’s reliability. Using Matlab-Simulink simulation tools, the efficacy and performance of the designed control have first been evaluated and assessed and compared with other optimization algorithms, and then with a dual loop based on a PID controller. Then, they have been verified by real-time hardware implementation on a 1.2 KW prototype FC converter driven by the dSPACE-1104 card under a variety of tests. The suggested approach offers impressive experimental results in dynamic and steady states.

Nonlinear disturbance observer-based robust predefined time tracking and vibration suppression control for the rigid-flexible coupled robotic mechanisms with large beam-deformations

Nonlinear disturbance observer-based robust predefined time tracking and vibration suppression control for the rigid-flexible coupled robotic mechanisms with large beam-deformations

Robust Tracking as Constrained Optimization by Uncertain Dynamic Plant: Mirror Descent Method and ASG—Version of Integral Sliding Mode Control

A class of controlled objects is considered, the dynamics of which are determined by a vector system of ordinary differential equations with a partially known right-hand side. It is presumed that the state variables and their velocities can be measured. Designing a robust tracking controller under some constraints to admissible state variables is the research goal. This construction, which extends the results for the average subgradient technique (ASG), and is an update of the subgradient descent technique (SDM) and integral sliding mode (ISM) approach, is realized by using the Legendre–Fenchel transform. A two-link robot manipulator with three revolute joints, powered by individual PMDC motors, is presented as an illustrative example of the suggested approach implementation.

Graph Attention Networks and Track Management for Multiple Object Tracking

Multiple object tracking (MOT) constitutes a critical research area within the field of computer vision. The creation of robust and efficient systems, which can approximate the mechanisms of human vision, is essential to enhance the efficacy of multiple object-tracking techniques. However, obstacles such as repetitive target appearances and frequent occlusions cause considerable inaccuracies or omissions in detection. Following the updating of these inaccurate observations into the tracklet, the effectiveness of the tracking model, employing appearance features, declines significantly. This paper introduces a novel method of multiple object tracking, employing graph attention networks and track management (GATM). Utilizing a graph attention network, an attention mechanism is employed to capture the relationships of nodes within the graph as well as node-to-node correlations across graphs. This mechanism allows selective focus on the features of advantageous nodes and enhances discriminability between node features, subsequently improving the performance and robustness of multiple object tracking. Simultaneously, we categorize distinct tracklet states and introduce an efficient track management method, which employs varying processing techniques for tracklets in diverse states. This method can manage occluded tracks in crowded scenes and improves tracking accuracy. Experiments conducted on three challenging public datasets (MOT16, MOT17, and MOT20) demonstrate that our method could deliver competitive performance.

Distributed Robust Formation Tracking Control for Quadrotor UAVs with Unknown Parameters and Uncertain Disturbances

In this paper, the distributed formation tracking control problem of quadrotor unmanned aerial vehicles is considered. Adaptive backstepping inherently accommodates model uncertainties and external disturbances, making it a robust choice for the dynamic and unpredictable environments in which unmanned aerial vehicles operate. This paper designs a formation flight control scheme for quadrotor unmanned aerial vehicles based on adaptive backstepping technology. The proposed control scheme is divided into two parts. For the position subsystem, a distributed robust formation tracking control scheme is developed to achieve formation flight of quadrotor unmanned aerial vehicles and track the desired flight trajectory. For the attitude subsystem, an adaptive disturbance rejection control scheme is proposed to achieve attitude stabilization during unmanned aerial vehicle flight under uncertain disturbances. Compared to existing results, the novelty of this paper lies in presenting a disturbance rejection flight control scheme for actual quadrotor unmanned aerial vehicle formations, without the need to know the model parameters of each unmanned aerial vehicle. Finally, a quadrotor unmanned aerial vehicle swarm system is used to verify the effectiveness of the proposed control scheme.

Analysis of UTM Tracking Performance for Conformance Monitoring via Hybrid SITL Monte Carlo Methods

Conformance monitoring supports UTM safety by observing if unmanned aircraft (UA) are adhering to declared operational intent. As a supporting system, robust cooperative tracking is critical. Nevertheless, tracking systems for UAS traffic management (UTM) are in an early stage and under-standardized, and existing literature hardly addresses the problem. To bridge this gap, this study aims to probabilistically evaluate the impact of the change in tracking performances on the effectiveness of conformance monitoring. We propose a Monte Carlo simulation-based method. To ensure a realistic simulation environment, we use a hybrid software-in-the-loop (SITL) scheme. The major uncertainties contributing to the stochastic evaluation are measured separately and are integrated into the final Monte Carlo simulation. Latency tests were conducted to assess the performance of different communication technologies for cooperative tracking. Flight technical error generation via SITL simulations and navigational system error generation based on flight experiments were employed to model UA trajectory uncertainty. Based on these tests, further Monte Carlo simulations were used to study the overall impacts of various tracking key performance indicators in UTM conformance monitoring. Results suggest that the extrapolation of UA position enables quicker non-conformance detection, but introduces greater variability in detection delay, and exacerbates the incidence of nuisance alerts and missed detections, particularly when latencies are high and velocity errors are severe. Recommendations for UA position update rates of ≥1 Hz remain consistent with previous studies, as investments in increasing the update rate do not lead to corresponding improvements in conformance monitoring performance according to simulation results.

Robust DOA estimation and tracking for integrated sensing and communication massive MIMO OFDM systems

Robust DOA estimation and tracking for integrated sensing and communication massive MIMO OFDM systems

Extended state observer based dynamic iterative learning for trajectory tracking control of a six-degrees-of-freedom manipulator

With the development of industrial automation comes an ever, broadening number of application scenarios for manipulators along with increasing demands for their precise control. However, manipulator trajectory tracking control schemes often exhibit problems such as those related to high levels of coupling, complex calculations, and in various difficulties in application for industrial environments. For the problems of low accuracy in control and poor robustness of multiple-jointed robotic trajectory tracking, iterative learning control (ILC) with model compensation (MC) based on extended state observer (ESO) has been proposed for the trajectory tracking control of six-degrees-of-freedom (six-DOF) manipulators. The scheme has excellent features to overcome uncertainties in repetitive tasks, including unknown bounded perturbations that are external to the model or dynamic perturbations that are internal to the model. The proposed control strategy combines ESO, iterative learning, and MC, for precise control of trajectory tracking. Here, ESO is used to estimate disturbances, iterative learning allows fast and accurate control in repeated tasks, and the model-compensated control algorithm alleviates the necessary for many inverse operations. The convergence of our proposed control scheme is proved through Lyapunov function and time-varying approximation theory. Simulation and experimental results verify the validity of the proposed scheme.

ADVANCEMENTS IN OBJECT DETECTION AND TRACKING ALGORITHMS: AN OVERVIEW OF RECENT PROGRESS

In the realm of computer vision, object detection and tracking constitute fundamental tasks, and recent years have borne witness to astounding advancements attributed to the integration of deep learning techniques. This paper aims to provide a comprehensive overview of the remarkable progress achieved in the domain of object detection and tracking algorithms, shedding light on their profound implications for accuracy, speed, and practical applications in the real world. Advances in object detection have been primarily driven by the adoption of Convolutional Neural Networks (CNNs). Prominent models such as Faster R-CNN, YOLO (You Only Look Once), and SSD (Single Shot Multi-Box Detector) have emerged, significantly enhancing the precision of object identification. Additionally, efficient detectors like Efficient Det and Mobile Net have emerged, striking a balance between accuracy and computational efficiency, thereby enabling real-time applications, even on resource-constrained devices. For tracking, Multi-Object Tracking (MOT) algorithms have undergone improvements, incorporating graph-based approaches such as the Hungarian algorithm and Joint Probabilistic Data Association Filter (JPDAF). These advancements have enabled robust object tracking across video frames. This paper also delves into the synergy between deep learning and real-world applications, emphasizing the impact of these algorithms in domains like autonomous vehicles, surveillance systems, robotics, and augmented reality. KEYWORDS: Object detection, Tracking algorithms, Computer vision, Deep learning, Advancements, Real-world applications, Convolutional Neural Networks, Multi-Object Tracking, Autonomous vehicles, Surveillance, Robotics, Augmented reality.

Active disturbance rejection control: an application to continuous microalgae photobioreactors

AbstractBACKGROUNDMathematical modelling is a widely employed approach for investigating the growth behaviour of microalgae. As a result, the development of model‐based controllers to regulate process variables has garnered increasing attention. However, despite the significant efforts invested in this area, control performance can be adversely affected by unmodelled dynamics and disturbances.RESULTSTwo active disturbance rejection controllers (ADRC) were designed to enable robust tracking of biomass concentration in continuous microalgae photobioreactors, with reduced reliance on the mathematical model of the system. The controllers were tuned to achieve a nonovershoot response and minimize settling time based on the culture's characteristics. Simulations were performed using optimal setpoints specific to each model. The results showcased a maximum output signal deviation of ±2.2%, ±7.8% and ± 7.62% for the Dunaliella tertiolecta, Isochrysis affinis galbana and Chlorella vulgaris models, respectively, regardless of the presence of simulated disturbances.CONCLUSIONSThe findings of this study significantly contribute to the advancement of the field of sustainable microalgae production. By introducing less dependent model‐based controllers, this research enhances the feasibility of implementing robust control strategies. These controllers require only knowledge of the equation system's order and the control gain function, simplifying the design process. This approach effectively addresses control performance degradation arising from unmodelled dynamics and disturbances. The ability to maintain desired process variables through ADRC controllers not only ensures improved control performance, but also supports the cultivation of specific microalgal species, when an accurate model is not available, thus promoting the overall progress and viability of microalgae biomass production. © 2023 Society of Chemical Industry.

Robust tracking strategy for nonlinear connected vehicle cyber-physical systems

This paper investigates the problem of tracking target control of a nonlinear system in the context of cyber-physical systems for connected autonomous vehicles with external unknown disturbances. A new robust tracking strategy via backstepping sliding-mode control is proposed. First, a connected nonlinear vehicle dynamical model with disturbances is constructed. Then, a disturbance observer is presented to approximate the unknown disturbances when the derivative of the disturbance is bounded. This paper has proved that the error of the observation can converge to zero in finite time. Third, a tracking control method is designed which combines the backstepping method with the sliding-mode method. According to the method, the estimated values of interference are used as a priori knowledge. Furthermore, the stability of the designed control strategy is demonstrated through using the Lyapunov theory. Finally, simulation experiments are presented to demonstrate the feasibility of the proposed approaches.

A nonsingular fast terminal sliding mode predictive controller for underwater soft crawling robots with parameter uncertainty

Unknown disturbances and material properties affect the trajectory tracking of underwater soft clawing robots. A nonsingular fast terminal sliding mode predictive controller is proposed to eliminate these effects. To characterize hysteresis and creep in dielectric elastomer materials, a dynamic model incorporating an improved dead-zone operator algorithm is constructed. Meanwhile, a nonsingular fast terminal sliding mode predictive controller that combines the Lie derivative function and sliding mode controller surface is developed to improve the accuracy and robustness of trajectory tracking. Additionally, the dynamic response and convergence speed of the proposed control system are improved by using an adaptive nonlinear disturbance observer to estimate the disturbance and uncertainty terms. Finally, the superiority and effectiveness of the proposed control method are verified by numerical simulations and experiments.

Stochastic-robust planning optimization method based on tracking-economy extreme scenario tradeoff for CCHP multi-energy system

The load tracking performance of combined cooling, heating, and power multi-energy system (CCHP-MES) greatly depends on the equipment capacity configuration. And the frequent fluctuations in the source-load uncertainty puts higher demands on the load tracking ability of CCHP-MES. For this reason, this paper proposes a novel tracking-economy extreme scenario tradeoff model by comparing the differences between tracking ability and economic benefit. The corresponding extreme scenarios based on these two objectives are characterized and the concept of uncertainty adjustment parameter is introduced to weigh the preference of extreme scenarios. On this basis, a multi-objective two-stage stochastic-robust optimization approach is developed, where the first stage optimizes the investment cost and the second stage optimizes the operating cost. Case study using typical load demand and renewable energy data in Nanjing verify the efficacy of the optimization model. The results indicate that the proposed tracking-economy extreme scenario can better characterize the frequent fluctuations of uncertainties compared to the traditional definition of extreme scenario. The equipment capacity configuration obtained under tracking-economy extreme scenario tradeoff model can better cope with the uncertainty fluctuations under extreme weather. Depending on different optimization preference, the economic robustness and tracking robustness can be improved by 13.80% and 36.06%, respectively.