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Journal articles on the topic 'Horizon tracking'

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Author: Grafiati

Published: 4 June 2021

Last updated: 20 February 2023

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1

Gogia, Rahul, Raman Singh, Paul de Groot, Harshit Gupta, Seshan Srirangarajan, Jyoti Phirani, and Sayan Ranu. "Tracking 3D seismic horizons with a new hybrid tracking algorithm." Interpretation 8, no. 4 (September 14, 2020): SQ39—SQ45. http://dx.doi.org/10.1190/int-2019-0296.1.

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We have developed a new algorithm for tracking 3D seismic horizons. The algorithm combines an inversion-based, seismic-dip flattening technique with conventional, similarity-based autotracking. The inversion part of the algorithm aims to minimize the error between horizon dips and computed seismic dips. After each cycle in the inversion loop, more seeds are added to the horizon by the similarity-based autotracker. In the example data set, the algorithm is first used to quickly track a set of framework horizons, each guided by a small set of user-picked seed positions. Next, the intervals bounded by the framework horizons are infilled to generate a dense set of horizons, also known as HorizonCube. This is done under the supervision of a human interpreter in a similar manner. The results show that the algorithm behaves better than unconstrained flattening techniques in intervals with trackable events. Inversion-based algorithms generate continuous horizons with no holes to be filled posttracking with a gridding algorithm and no loop skips (jumping to the wrong event) that need to be edited as is standard practice with autotrackers. Because editing is a time-consuming process, creating horizons with inversion-based algorithms tends to be faster than conventional autotracking. Horizons created with the adopted algorithm follow seismic events more closely than horizons generated with the inversion-only algorithm, and the fault crossings are sharper.

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2

Wang, Huiran, Qidong Wang, Wuwei Chen, Linfeng Zhao, and Dongkui Tan. "Path tracking based on model predictive control with variable predictive horizon." Transactions of the Institute of Measurement and Control 43, no. 12 (April 6, 2021): 2676–88. http://dx.doi.org/10.1177/01423312211003809.

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Model predictive control is one of the main methods used in path tracking for autonomous vehicles. To improve the path tracking performance of the vehicle, a path tracking method based on model predictive control with variable predictive horizon is proposed in this paper. Based on the designed model predictive controller for path tracking, the response analysis of path tracking control system under the different predictive horizons is carried out to clarify the influence of predictive horizon on path tracking accuracy, driving comfort and real-time of the control algorithm. Then, taking the lateral offset, the steering frequency and the real-time of the control algorithm as comprehensive performance indexes, the particle swarm optimization algorithm is designed to realize the adaptive optimization for the predictive horizon. The effectiveness of the proposed method is evaluated via numerical simulation based on Simulink/CarSim and hardware-in-the-loop experiment on an autonomous driving simulator. The obtained results show that the optimized predictive horizon can adapt to the different driving environment, and the proposed path tracking method has good comprehensive performance in terms of path tracking accuracy of the vehicle, driving comfort and real-time.

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3

Herron, Donald A. "Pitfalls in horizon autopicking." Interpretation 3, no. 1 (February 1, 2015): SB1—SB4. http://dx.doi.org/10.1190/int-2014-0062.1.

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Interpreters use horizon autopicking in many seismic interpretations in the modern workstation environment. When properly used and with data quality permitting this technique enables efficient and accurate tracking of horizons but is not without its pitfalls. Four common pitfalls are improper selection of the input control or seed grid, not accounting for the “directional” behavior of tracking algorithms, attempting autopicking in areas with poor reflection continuity and/or low signal-to-noise ratio, and failing to recognize elements of geology that are not suitable for autopicking.

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4

Lou, Yihuai, Bo Zhang, Tengfei Lin, and Danping Cao. "Seismic horizon picking by integrating reflector dip and instantaneous phase attributes." GEOPHYSICS 85, no. 2 (January 30, 2020): O37—O45. http://dx.doi.org/10.1190/geo2018-0303.1.

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Seismic horizons are the compulsory inputs for seismic stratigraphy analysis and 3D reservoir modeling. Manually interpreting horizons on thousands of vertical seismic slices of 3D seismic survey is a time-consuming task. Automatic horizon interpreting algorithms are usually based on the seismic reflector dip. However, the estimated seismic reflector dip is usually inaccurate near and across geologic features such as unconformities. We are determined to improve the quality of picked horizons using multiple seismic attributes. We assume that seismic horizons follow the reflector dip and that the same horizons should have similar instantaneous phase values. We first generate horizon patches using a reflector dip attribute, which is similar to current methods. We use seismic coherence attribute as the stop criteria for tracking the horizon within each patch. Considering the inaccuracy of reflector dip estimates at and near the discontinuous structures such as fault and unconformities, we use the seismic instantaneous phase attribute to improve the quality of the generated horizon patches. We generate horizons by merging the residual horizon patches and only outputting the best horizon in each iteration. Our method is capable of generating a horizon for each reflection within the 3D seismic survey, and the generated horizons strictly follow the seismic reflections over the whole seismic survey. Finally, each time sample of seismic traces is assigned a chronostratigraphic relative geologic time value according to the tracked horizons.

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5

Zhang, Bing, Changfu Zong, Guoying Chen, and Guiyuan Li. "An adaptive-prediction-horizon model prediction control for path tracking in a four-wheel independent control electric vehicle." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 233, no. 12 (January 11, 2019): 3246–62. http://dx.doi.org/10.1177/0954407018821527.

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An adaptive-prediction-horizon model prediction control-based path tracking controller for a four-wheel independent control electric vehicle is designed. Unlike traditional model prediction control with fixed prediction horizon, this paper devotes to satisfy the varied path tracking demand by adjusting online the prediction horizon of model prediction control according to its effect on vehicle dynamic characteristics. Vehicle dynamic stability quantized with the vehicle sideslip-feature phase plane is preferentially considered in the prediction horizon adjustment. For stability during switching prediction horizon and for robustness during path tracking, the numerical problem inherent in the adaptive-prediction-horizon model prediction control is analysed and solved by introducing exponentially decreasing weight. Subsequently, the desired motion for path tracking with the four-wheel independent control electric vehicle is realized with a hierarchical control structure. Simulation results finally illustrate the effectiveness of the proposed method.

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6

Labrunye, Emmanuel, and Camille Carn. "Merging chronostratigraphic modeling and global horizon tracking." Interpretation 3, no. 2 (May 1, 2015): SN59—SN67. http://dx.doi.org/10.1190/int-2014-0130.1.

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We have determined to combine the automatic interpretation of horizons in a seismic cube with a space/time framework to construct a chronostratigraphic model that matched seismic events and could be used later, without having to rework it in reservoir modeling and seismic characterization. A large number of single seismic events were automatically extracted from the cube as horizon patches. Each patch was associated to an individual isogeologic time constraint. An optimization process then proposed a geologically coherent model in the volume, filling the gaps in the area where no patch was extracted, and taking into account additional geologic information, such as unconformities, fault displacement, or well information. Furthermore, this process generated a seismic flattened volume used to check the quality of the model and revealed some geologic features such as unpicked faults, channels, lobes, and splays. We evaluated a use case in which this method was successfully tested on a complex faulted data set.

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7

Zou, Rui, and Sourabh Bhattacharya. "Visibility-Based Finite-Horizon Target Tracking Game." IEEE Robotics and Automation Letters 1, no. 1 (January 2016): 399–406. http://dx.doi.org/10.1109/lra.2016.2521429.

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8

Yao and Tian. "A Model Predictive Controller with Longitudinal Speed Compensation for Autonomous Vehicle Path Tracking." Applied Sciences 9, no. 22 (November 6, 2019): 4739. http://dx.doi.org/10.3390/app9224739.

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Autonomous vehicle path tracking accuracy faces challenges in being accomplished due to the assumption that the longitudinal speed is constant in the prediction horizon in a model predictive control (MPC) control frame. A model predictive control path tracking controller with longitudinal speed compensation in the prediction horizon is proposed in this paper, which reduces the lateral deviation, course deviation, and maintains vehicle stability. The vehicle model, tire model, and path tracking model are described and linearized using the small angle approximation method and an equivalent cornering stiffness method. The mechanism of action of longitudinal speed changed with state vector variation, and the stability of the path tracking closed-loop control system in the prediction horizon is analyzed in this paper. Then the longitudinal speed compensation strategy is proposed to reduce tracking error. The controller designed was tested through simulation on the CarSim-Simulink platform, and it showed improved performance in tracking accuracy and satisfied vehicle stability constrains.

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9

Hu, Chaofang, and Lingxue Zhao. "Overtaking control strategy based on model predictive control with varying horizon for unmanned ground vehicle." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 235, no. 1 (August 14, 2020): 78–92. http://dx.doi.org/10.1177/0954407020947515.

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In this paper, a synthesized novel strategy of varying predictive horizon-based model predictive control is proposed for the overtaking control of unmanned ground vehicle. The whole control strategy includes path planning and path tracking. First, the preferred path in presence of diverse constraints of states, inputs, and collision avoidance can be calculated using Gauss pseudospectral method where expected position, velocity, and attitude are provided. Correspondingly, the continuous optimal control problem is converted to discrete nonlinear programming. Second, model predictive control is developed for tracking the optimized path. Considering the effect of the predictive horizon and the Gauss points’ distribution on tracking performance, the varying predictive horizon is introduced to improve the tracking accuracy in non-smooth path. By the varying predictive horizon-based model predictive control method, less computation burden and better control performance are achieved. For the difference between the mathematical expressions and the real unmanned ground vehicle dynamics, genetic algorithm is utilized to identify the parameters of tire model. Simulations in MATLAB and CarSim are both implemented to illustrate the effectiveness of the proposed method.

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10

Hedjar, R., and P. Boucher. "Nonlinear Receding-Horizon Control of Rigid Link Robot Manipulators." International Journal of Advanced Robotic Systems 2, no. 1 (March 1, 2005): 3. http://dx.doi.org/10.5772/5806.

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The approximate nonlinear receding-horizon control law is used to treat the trajectory tracking control problem of rigid link robot manipulators. The derived nonlinear predictive law uses a quadratic performance index of the predicted tracking error and the predicted control effort. A key feature of this control law is that, for their implementation, there is no need to perform an online optimization, and asymptotic tracking of smooth reference trajectories is guaranteed. It is shown that this controller achieves the positions tracking objectives via link position measurements. The stability convergence of the output tracking error to the origin is proved. To enhance the robustness of the closed loop system with respect to payload uncertainties and viscous friction, an integral action is introduced in the loop. A nonlinear observer is used to estimate velocity. Simulation results for a two-link rigid robot are performed to validate the performance of the proposed controller.

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11

Peters, Bas, Justin Granek, and Eldad Haber. "Multiresolution neural networks for tracking seismic horizons from few training images." Interpretation 7, no. 3 (August 1, 2019): SE201—SE213. http://dx.doi.org/10.1190/int-2018-0225.1.

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Detecting a specific horizon in seismic images is a valuable tool for geologic interpretation. Because hand picking the locations of the horizon is a time-consuming process, automated computational methods were developed starting three decades ago. Until now, most networks have been trained on data that were created by cutting larger seismic images into many small patches. This limits the networks ability to learn from large-scale geologic structures. Moreover, currently available networks and training strategies require label patches that have full and continuous horizon picks (annotations), which are also time-consuming to generate. We have developed a projected loss function that enables training on labels with just a few annotated pixels and has no issue with the other unknown label pixels. We use this loss function for training convolutional networks with a multiresolution structure, including variants of the U-net. Our networks learn from a small number of large seismic images without creating patches. Training uses all seismic data without reserving some for validation. Only the labels are split into training/testing. We validate the accuracy of the trained network using the horizon picks that were never shown to the network. Contrary to other work on horizon tracking, we train the network to perform nonlinear regression, not classification. As such, we generate labels as the convolution of a Gaussian kernel and the known horizon locations that communicate uncertainty in the labels. The network output is the probability of the horizon location. We examine the new method on two different data sets, one for horizon extrapolation and another data set for interpolation. We found that the predictions of our methodology are accurate even in areas far from known horizon locations because our learning strategy exploits all data in large seismic images.

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12

Mejía, Juan S., and Dušan M. Stipanović. "Computational receding horizon approach to safe trajectory tracking." Integrated Computer-Aided Engineering 15, no. 2 (February 18, 2008): 149–61. http://dx.doi.org/10.3233/ica-2008-15205.

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13

Zhang, Zhihua, Thomas Leifeld, and Ping Zhang. "Finite Horizon Tracking Control of Boolean Control Networks." IEEE Transactions on Automatic Control 63, no. 6 (June 2018): 1798–805. http://dx.doi.org/10.1109/tac.2017.2754947.

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14

Kryazhimskiy, A. V., and V. I. Maksimov. "Resource-saving tracking problem with infinite time horizon." Differential Equations 47, no. 7 (July 2011): 1004–13. http://dx.doi.org/10.1134/s001226611107010x.

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15

Dongbing Gu and Huosheng Hu. "Receding horizon tracking control of wheeled mobile robots." IEEE Transactions on Control Systems Technology 14, no. 4 (July 2006): 743–49. http://dx.doi.org/10.1109/tcst.2006.872512.

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16

Kryazhimskiy, Arkady, and Vyacheslav Maksimov. "Resource-saving infinite-horizon tracking under uncertain input." Applied Mathematics and Computation 217, no. 3 (October 2010): 1135–40. http://dx.doi.org/10.1016/j.amc.2010.01.014.

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17

McNab, Robert J., and Tsu-Chin Tsao. "Receding Time Horizon Linear Quadratic Optimal Control for Multi-Axis Contour Tracking Motion Control1." Journal of Dynamic Systems, Measurement, and Control 122, no. 2 (December 15, 1998): 375–81. http://dx.doi.org/10.1115/1.482476.

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A receding time horizon linear quadratic optimal control approach is formulated for multi-axis contour tracking problem. The approach employs a performance index with fixed weights on quadratic contouring error, tracking error, and control input over a future finite horizon. The problem is then cast into a standard receding horizon LQ problem with time varying weighting matrices, which are functions of the future contour trajectory within the horizon. The formulation thus leads to a solution of time varying state feedback and finite preview gains. Stability is proven for the linear trajectory case. Experimental and simulated results for an X-Y motion control problem are presented, which demonstrate the effectiveness of the control scheme and the effects of the key controller design parameters. [S0022-0434(00)01202-8]

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18

Hong, Zhong, Mingjun Su, Feng Qian, Guangmin Hu, and Qingyun Han. "Global seismic horizon interpretation based on data mining — A new tool for seismic geomorphologic study." Interpretation 8, no. 1 (February 1, 2020): T131—T140. http://dx.doi.org/10.1190/int-2018-0210.1.

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Accurate and efficient seismic horizon interpretation is important for seismic geomorphology study. By integrating the improved density-based clustering method to generate horizon patches and a heuristic combining strategy to merge horizon patches, we have developed a novel data mining approach to automatically extract globally optimal horizons for detailed geomorphologic interpretation. First, the application of improved density-based clustering method has distinct merits in calculation speed and avoiding the phenomenon of mis-ties. We design a heuristic combining strategy to effectively combine the horizon patches. It is also able to ameliorate the problem of mis-ties that frequently occurs in horizon picking. Second, the proposed algorithm can identify abnormal unit in terms of independent horizon fragments. Furthermore, the introduced method is capable of detecting small-scale seismic geomorphologic features. The applications indicate good real-time performance of our new global interpretation algorithm in automated-tracking speed and quality. Our method can resolve the problem of mis-ties in cases of complex seismic reflection to a certain extent. Besides, not only are a series of channels separately recognized, but also small-scale meandering rivers are clearly mapped. Our algorithm is capable of adding more geologic information and realizing a better showcase of geomorphologic features.

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19

Inel, Fouad, Ali Medjbouri, and Giuseppe Carbone. "A Non-Linear Continuous-Time Generalized Predictive Control for a Planar Cable-Driven Parallel Robot." Actuators 10, no. 5 (May 4, 2021): 97. http://dx.doi.org/10.3390/act10050097.

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This paper addresses a novel nonlinear algorithm for the trajectory tracking of a planar cable-driven parallel robot. In particular, we outline a nonlinear continuous-time generalized predictive control (NCGPC). The proposed controller design is based on the finite horizon continuous-time minimization of a quadratic predicted cost function. The tracking error in the receding horizon is approximated using a Taylor-series expansion. The main advantage of the proposed NCGPC is based on using an analytic solution, which can be truncated to a desired degree of order of the Taylor-series. This allows us to achieve a prediction horizon of NCGPC tracking performance. The description of the proposed NCGPC method is followed by a comparison between NCGPC and a conventional computed torque control (CTC) method. Robustness tests are performed by considering payload and parameter uncertainties for both controllers. Simulation results of NCGPC compared to the commonly used CTC prove the effectiveness and advantages of the proposed approach.

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20

Zhang, Qingle, Jun-e. Feng, and Ticao Jiao. "Finite horizon tracking control of probabilistic Boolean control networks." Journal of the Franklin Institute 358, no. 18 (December 2021): 9909–28. http://dx.doi.org/10.1016/j.jfranklin.2021.10.003.

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21

Camilli, Richard, Christopher M. Reddy, Dana R. Yoerger, Benjamin A. S. Van Mooy, Michael V. Jakuba, James C. Kinsey, Cameron P. McIntyre, Sean P. Sylva, and James V. Maloney. "Tracking Hydrocarbon Plume Transport and Biodegradation at Deepwater Horizon." Science 330, no. 6001 (August 19, 2010): 201–4. http://dx.doi.org/10.1126/science.1195223.

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22

Yang, Liuxin, and Sam Zandong Sun. "Seismic horizon tracking using a deep convolutional neural network." Journal of Petroleum Science and Engineering 187 (April 2020): 106709. http://dx.doi.org/10.1016/j.petrol.2019.106709.

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23

Liu, Yonggang, Robert H. Weisberg, Chuanmin Hu, and Lianyuan Zheng. "Tracking the Deepwater Horizon Oil Spill: A Modeling Perspective." Eos, Transactions American Geophysical Union 92, no. 6 (February 8, 2011): 45–46. http://dx.doi.org/10.1029/2011eo060001.

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24

Greer, William B., and Cornel Sultan. "Infinite horizon model predictive control tracking application to helicopters." Aerospace Science and Technology 98 (March 2020): 105675. http://dx.doi.org/10.1016/j.ast.2019.105675.

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25

Schwendeman, Michael, and Jim Thomson. "A Horizon-Tracking Method for Shipboard Video Stabilization and Rectification." Journal of Atmospheric and Oceanic Technology 32, no. 1 (January 2015): 164–76. http://dx.doi.org/10.1175/jtech-d-14-00047.1.

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AbstractAn algorithm is presented for the stabilization and rectification of digital video from floating platforms. The method relies on a horizon-tracking technique that was tested under a variety of lighting and sea-state conditions for 48 h of video data over 12 days during a research cruise in the North Pacific Ocean. In this dataset, the horizon was correctly labeled in 92% of the frames in which it was present. The idealized camera model assumes pure pitch-and-roll motion, a flat sea surface, and an unobstructed horizon line. Pitch and roll are defined along the camera look direction rather than in traditional ship coordinates, such that the method can be used for any heading relative to the ship. The uncertainty in pitch and roll is estimated from the uncertainties of the horizon-finding method. These errors are found to be of the order 0.6° in roll and 0.3° in pitch. Errors in rectification are shown to be dominated by the uncertainty in camera height, which may change with the heave motion of a floating platform. The propagation of these errors is demonstrated for the breaking-wave distribution Λ(c). A toolbox for implementation of this method in MATLAB is shared via the MATLAB File Exchange.

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26

Elbanhawi, M., M. Simic, and R. Jazar. "Receding horizon lateral vehicle control for pure pursuit path tracking." Journal of Vibration and Control 24, no. 3 (May 22, 2016): 619–42. http://dx.doi.org/10.1177/1077546316646906.

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The assimilation of path planning and motion control is a crucial capability for autonomous vehicles. Pure pursuit controllers are a prevalent class of path tracking algorithms for front wheel steering cars. Nonetheless, their performance is rather limited to relatively low speeds. In this paper, we propose a model predictive active yaw control implementation of pure pursuit path tracking that accommodates the vehicle’s steady state lateral dynamics to improve tracking performance at high speeds. A comparative numerical analysis was under taken between the proposed strategy and the traditional pure pursuit controller scheme. Tests were conducted for three different paths at iteratively increasing speeds from 1 m/s up to 20 m/s. The traditional pure pursuit controller was incapable of maintaining the vehicle stable at speeds upwards of 5m/s. The results show that implementing receding horizon strategy for pure pursuit tracking improves their performance. The contribution is apparent by preserving a relatively constant controller effort and consequently maintaining vehicle stability for speeds up to 100Km/h in different scenarios. A Matlab implementation of the proposed controller and datasets of the experimental paths are provided to supplement this work.

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27

Sun, Luping, Ling Ding, and Xiangchun Wang. "Research on Initial Model Construction of Seismic Inversion Based on Velocity Spectrum and Siamese Network." Applied Sciences 12, no. 20 (October 20, 2022): 10593. http://dx.doi.org/10.3390/app122010593.

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The initial model plays an important role in seismic inversion. Generally, the initial model is constructed by lateral extrapolation of parameters under horizons constraints. However, without horizon data, initial modeling becomes a challenging task. Velocity spectrum is a 2D image that can reflect the characteristics of the formations. We regard the problem of establishing the initial model as the problem of similarity analysis of seismic lateral characteristics and propose a method of establishing the initial inversion model based on velocity spectrum and Siamese network. Firstly, the lateral variation of formation characteristics is tracked on velocity spectra generated by common depth point (CDP) gathers. Then, the target tracking results at different CDP positions are obtained with the triple Siamese network. Finally, the discrete inversion parameters are extrapolated along the tracking paths to obtain the initial inversion model. The Siamese network can quickly obtain the similarity of 2D images and does not need manual labels. The theoretical and practical results show that our method can efficiently generate the initial model that conforms to the seismic structure and stratigraphic characteristics without the constraint of interpreted horizon data.

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28

Kikuchi, Tomoya, Kenichiro Nonaka, and Kazuma Sekiguchi. "Moving Horizon Estimation with Probabilistic Data Association for Object Tracking Considering System Noise Constraint." Journal of Robotics and Mechatronics 32, no. 3 (June 20, 2020): 537–47. http://dx.doi.org/10.20965/jrm.2020.p0537.

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Object tracking is widely utilized and becomes indispensable in automation technology. In environments containing many objects, however, occlusion and false recognition frequently occur. To alleviate these issues, in this paper, we propose a novel object tracking method based on moving horizon estimation incorporating probabilistic data association (MHE-PDA) through a probabilistic data association filter (PDAF). Since moving horizon estimation (MHE) is accomplished through numerical optimization, we can ensure that the estimation is consistent with physical constraints and robust to outliers. The robustness of the proposed method against occlusion and false recognition is verified by comparison with PDAF through simulations of a cluttered environment.

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29

Wan, Shizheng, Xiaofei Chang, Quancheng Li, and Jie Yan. "Finite-Horizon Optimal Tracking Guidance for Aircraft Based on Approximate Dynamic Programming." Mathematical Problems in Engineering 2019 (March 21, 2019): 1–12. http://dx.doi.org/10.1155/2019/8649781.

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Referring to the optimal tracking guidance of aircraft, the conventional time based kinematics model is transformed into a downrange based model by independent variable replacement. The deviations of in-flight altitude and flight path angle are penalized and corrected to achieve high precision tracking of reference trajectory. The tracking problem is solved as a linear quadratic regulator applying small perturbation theory, and the approximate dynamic programming method is used to cope with the solving of finite-horizon optimization. An actor-critic structure is established to approximate the optimal tracking controller and minimum cost function. The least squares method and Adam optimization algorithm are adopted to learn the parameters of critic network and actor network, respectively. A boosting trajectory with maximum final velocity is generated by Gauss pseudospectral method for the validation of guidance strategy. The results show that the trained feedback control parameters can effectively resist random wind disturbance, correct the initial altitude and flight path angle deviations, and achieve the goal of following a given trajectory.

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30

CHEN, Jian, Dong SUN, and Jie YANG. "A RECEDING-HORIZON FORMATION TRACKING CONTROLLER WITH LEADER-FOLLOWER STRATEGIES." IFAC Proceedings Volumes 41, no. 2 (2008): 4400–4405. http://dx.doi.org/10.3182/20080706-5-kr-1001.00741.

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31

Hammer, Jacob. "Nonlinear Tracking on the Infinite Horizon: Optimal Robust State Feedback." IFAC-PapersOnLine 54, no. 9 (2021): 602–9. http://dx.doi.org/10.1016/j.ifacol.2021.06.122.

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32

Mudge, S. M., M. T. BenKinney, D. Beckmann, and J. S. Brown. "Tracking the Dispersant Applied during the MC252 Deepwater Horizon Incident." International Oil Spill Conference Proceedings 2011, no. 1 (March 2011): abs351. http://dx.doi.org/10.7901/2169-3358-2011-1-351.

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33

Koohifar, Farshad, Abhaykumar Kumbhar, and Ismail Guvenc. "Receding Horizon Multi-UAV Cooperative Tracking of Moving RF Source." IEEE Communications Letters 21, no. 6 (June 2017): 1433–36. http://dx.doi.org/10.1109/lcomm.2016.2603977.

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34

Leizarowitz, Arie. "Infinite horizon stochastic regulation and tracking with the overtaking criterion∗." Stochastics 22, no. 2 (October 1987): 117–50. http://dx.doi.org/10.1080/17442508708833470.

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35

Primbs, James A., and Chang Hwan Sung. "A Stochastic Receding Horizon Control Approach to Constrained Index Tracking." Asia-Pacific Financial Markets 15, no. 1 (March 2008): 3–24. http://dx.doi.org/10.1007/s10690-008-9073-1.

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36

Hansen, Andreas, Yutong Li, and J. Karl Hedrick. "Invariant sliding domains for constrained linear receding horizon tracking control." IFAC Journal of Systems and Control 2 (December 2017): 12–17. http://dx.doi.org/10.1016/j.ifacsc.2017.11.001.

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37

Kamalapurkar, Rushikesh, Lindsey Andrews, Patrick Walters, and Warren E. Dixon. "Model-Based Reinforcement Learning for Infinite-Horizon Approximate Optimal Tracking." IEEE Transactions on Neural Networks and Learning Systems 28, no. 3 (March 2017): 753–58. http://dx.doi.org/10.1109/tnnls.2015.2511658.

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38

Huang, Yuan, Yifang Shi, and Taek Song. "An Efficient Multi-Path Multitarget Tracking Algorithm for Over-The-Horizon Radar." Sensors 19, no. 6 (March 20, 2019): 1384. http://dx.doi.org/10.3390/s19061384.

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In target tracking environments using over-the-horizon radar (OTHR), one target may generate multiple detections through different signal propagation paths. Trackers need to jointly handle the uncertainties stemming from both measurement origin and measurement path. Traditional multitarget tracking algorithms suffer from high computational loads in such environments since they need to enumerate all possible joint measurement-to-track assignments considering the measurements paths unless they employ some approximations regarding the measurements and their corresponding paths. In this paper, we propose a novel algorithm, named multi-path linear multitarget integrated probabilistic data association (MP-LM-IPDA), to efficiently track multitarget in multiple detection environments. Instead of generating all possible joint assignments, MP-LM-IPDA calculates the modulated clutter measurement density for each measurement cell of each track. The modulated clutter measurement density considers the possibility that the measurement cells originate from the clutter as well as from other potential targets. By incorporating the modulated clutter measurement density, the single target tracking structure can be applied for multitarget tracking, which significantly reduces the computational load. The simulation results demonstrate the effectiveness and efficiency of the proposed algorithm.

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Liu, Fen, Wendong Xiao, Shuai Chen, and Chengpeng Jiang. "Adaptive Dynamic Programming-Based Multi-Sensor Scheduling for Collaborative Target Tracking in Energy Harvesting Wireless Sensor Networks." Sensors 18, no. 12 (November 22, 2018): 4090. http://dx.doi.org/10.3390/s18124090.

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Collaborative target tracking is one of the most important applications of wireless sensor networks (WSNs), in which the network must rely on sensor scheduling to balance the tracking accuracy and energy consumption, due to the limited network resources for sensing, communication, and computation. With the recent development of energy acquisition technologies, the building of WSNs based on energy harvesting has become possible to overcome the limitation of battery energy in WSNs, where theoretically the lifetime of the network could be extended to infinite. However, energy-harvesting WSNs pose new technical challenges for collaborative target tracking on how to schedule sensors over the infinite horizon under the restriction on limited sensor energy harvesting capabilities. In this paper, we propose a novel adaptive dynamic programming (ADP)-based multi-sensor scheduling algorithm (ADP-MSS) for collaborative target tracking for energy-harvesting WSNs. ADP-MSS can schedule multiple sensors for each time step over an infinite horizon to achieve high tracking accuracy, based on the extended Kalman filter (EKF) for target state prediction and estimation. Theoretical analysis shows the optimality of ADP-MSS, and simulation results demonstrate its superior tracking accuracy compared with an ADP-based single-sensor scheduling scheme and a simulated-annealing based multi-sensor scheduling scheme.

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Khamis, Ahmed, D. Subbaram Naidu, and Ahmed M. Kamel. "Nonlinear Finite-Horizon Regulation and Tracking for Systems with Incomplete State Information Using Differential State Dependent Riccati Equation." International Journal of Aerospace Engineering 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/178628.

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This paper presents an efficient online technique used for finite-horizon, nonlinear, stochastic, regulator, and tracking problems. This can be accomplished by the integration of the differential SDRE filter algorithm and the finite-horizon state dependent Riccati equation (SDRE) technique. Unlike the previous methods which deal with the linearized system, this technique provides finite-horizon estimation and control of the nonlinear stochastic systems. Further, the proposed technique is effective for a wide range of operating points. Simulation results of a missile guidance system are presented to illustrate the effectiveness of the proposed technique.

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Hedjar, R. "Nonlinear Predictive Control With End Point Constraints." Journal of Engineering Research [TJER] 3, no. 1 (December 1, 2006): 69. http://dx.doi.org/10.24200/tjer.vol3iss1pp69-74.

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The optimal nonlinear predictive control structure with end point constraints is presented, which provides asymptotic tracking of smooth reference trajectories. The controller is based on a finite horizon continuous time minimization of nonlinear predicted tracking errors. A key feature of the control law is that its implementation does not need to perform an online optimization, and asymptotic tracking of smooth reference signal is guaranteed. The proposed control scheme is applied to planning motions problem of a mobile robot. Simulations results are performed to validate the tracking performance of the proposed controller.

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Davey, Samuel J., Giuseppe A. Fabrizio, and Mark G. Rutten. "Detection and Tracking of Multipath Targets in Over-the-Horizon Radar." IEEE Transactions on Aerospace and Electronic Systems 55, no. 5 (October 2019): 2277–95. http://dx.doi.org/10.1109/taes.2018.2884185.

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Lee, Jae-Won, Wook Hyun Kwon, and Joon Hwa Lee. "Receding horizon H tracking control for time-varying discrete linear systems." International Journal of Control 68, no. 2 (January 1997): 385–400. http://dx.doi.org/10.1080/002071797223686.

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Li, Huiping, Pan Xie, and Weisheng Yan. "Receding Horizon Formation Tracking Control of Constrained Underactuated Autonomous Underwater Vehicles." IEEE Transactions on Industrial Electronics 64, no. 6 (June 2017): 5004–13. http://dx.doi.org/10.1109/tie.2016.2589921.

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Zhang, Yueqiang, Haibo Liu, Ang Su, Yang Gui, and Yang Shang. "Real-time estimation of ship's horizontal attitude based on horizon tracking." Optik 126, no. 23 (December 2015): 4475–83. http://dx.doi.org/10.1016/j.ijleo.2015.08.156.

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Prodan, Ionela, Sorin Olaru, Ricardo Bencatel, João Borges de Sousa, Cristina Stoica, and Silviu-Iulian Niculescu. "Receding horizon flight control for trajectory tracking of autonomous aerial vehicles." Control Engineering Practice 21, no. 10 (October 2013): 1334–49. http://dx.doi.org/10.1016/j.conengprac.2013.05.010.

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Kim, K. B. "Receding horizon tracking control for constrained linear continuous time-varying systems." IEE Proceedings - Control Theory and Applications 150, no. 5 (September 1, 2003): 534–38. http://dx.doi.org/10.1049/ip-cta:20030711.

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Najafi Birgani, Soleiman, Bijan Moaveni, and Ali Khaki-Sedigh. "Infinite horizon linear quadratic tracking problem: A discounted cost function approach." Optimal Control Applications and Methods 39, no. 4 (April 23, 2018): 1549–72. http://dx.doi.org/10.1002/oca.2425.

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Yang, Can, and Jie Liu. "Trajectory Tracking Control of Intelligent Driving Vehicles Based on MPC and Fuzzy PID." Mathematical Problems in Engineering 2023 (February 3, 2023): 1–24. http://dx.doi.org/10.1155/2023/2464254.

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To improve the stability and accuracy of quintic polynomial trajectory tracking, an MPC (model predictive control) and fuzzy PID (proportional-integral-difference)- based control method are proposed. A lateral tracking controller is designed by using MPC with rule-based horizon parameters. The lateral tracking controller controls the steering angle to reduce the lateral tracking errors. A longitudinal tracking controller is designed by using a fuzzy PID. The longitudinal controller controls the motor torque and brake pressure referring to a throttle/brake calibration table to reduce the longitudinal tracking errors. By combining the two controllers, we achieve satisfactory trajectory tracking control. Relative vehicle trajectory tracking simulation is carried out under common scenarios of quintic polynomial trajectory in the Simulink/Carsim platform. The result shows that the strategy can avoid excessive trajectory tracking errors which ensures a better performance for trajectory tracking with high safety, stability, and adaptability.

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Hu, Ying, Xiaomin Shi, and Zuo Quan Xu. "Constrained stochastic LQ control on infinite time horizon with regime switching." ESAIM: Control, Optimisation and Calculus of Variations 28 (2022): 5. http://dx.doi.org/10.1051/cocv/2021110.

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This paper is concerned with a stochastic linear-quadratic (LQ) optimal control problem on infinite time horizon, with regime switching, random coefficients, and cone control constraint. To tackle the problem, two new extended stochastic Riccati equations (ESREs) on infinite time horizon are introduced. The existence of the nonnegative solutions, in both standard and singular cases, is proved through a sequence of ESREs on finite time horizon. Based on this result and some approximation techniques, we obtain the optimal state feedback control and optimal value for the stochastic LQ problem explicitly. Finally, we apply these results to solve a lifetime portfolio selection problem of tracking a given wealth level with regime switching and portfolio constraint.

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