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Journal articles on the topic 'Control path'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Koyachi, Noriho, and Shigeru Sarata. "Path Planning and Path Following Control of an Autonomous Wheel Loader." Abstracts of the international conference on advanced mechatronics : toward evolutionary fusion of IT and mechatronics : ICAM 2010.5 (2010): 699–704. http://dx.doi.org/10.1299/jsmeicam.2010.5.699.

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2

Pankov, Sergey. "Configuration Path Control." International Journal of Control, Automation and Systems 21, no. 1 (2023): 306–17. http://dx.doi.org/10.1007/s12555-021-0466-5.

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3

Gammell, Jonathan D., and Marlin P. Strub. "Asymptotically Optimal Sampling-Based Motion Planning Methods." Annual Review of Control, Robotics, and Autonomous Systems 4, no. 1 (2021): 295–318. http://dx.doi.org/10.1146/annurev-control-061920-093753.

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Motion planning is a fundamental problem in autonomous robotics that requires finding a path to a specified goal that avoids obstacles and takes into account a robot's limitations and constraints. It is often desirable for this path to also optimize a cost function, such as path length. Formal path-quality guarantees for continuously valued search spaces are an active area of research interest. Recent results have proven that some sampling-based planning methods probabilistically converge toward the optimal solution as computational effort approaches infinity. This article summarizes the assum

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4

Astolfi, A. "The path of control." European Journal of Control 19, no. 5 (2013): 340. http://dx.doi.org/10.1016/j.ejcon.2013.05.021.

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5

Egami, Tadashi, Tomoyasu Yoshikawa, and Makoto Kurihara. "Path Control with Vector Decomposition." IEEJ Transactions on Industry Applications 120, no. 2 (2000): 262–68. http://dx.doi.org/10.1541/ieejias.120.262.

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6

Höskuldsson, Agnar, Oxana Rodionova, and Alexey Pomerantsev. "Path modeling and process control." Chemometrics and Intelligent Laboratory Systems 88, no. 1 (2007): 84–99. http://dx.doi.org/10.1016/j.chemolab.2006.09.010.

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7

SZABOLCSI, Róbert. "MODEL PREDICTIVE CONTROL APPLIED IN UAV FLIGHT PATH TRACKING MISSIONS." Review of the Air Force Academy 17, no. 1 (2019): 49–62. http://dx.doi.org/10.19062/1842-9238.2019.17.1.7.

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8

Cong, GENG, ZHANG Jian, and LIU Jinxiong. "Path Tracking Control Based on Adaptive Control Period." IFAC-PapersOnLine 53, no. 5 (2020): 592–97. http://dx.doi.org/10.1016/j.ifacol.2021.04.148.

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9

Joo, Young-Hoon, and Jong-Seon Kim. "Path Control Algorithm for AGV Using Right of Path Occupation." Journal of Korean institute of intelligent systems 18, no. 5 (2008): 592–98. http://dx.doi.org/10.5391/jkiis.2008.18.5.592.

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10

Borst, C., M. Mulder, M. M. van Paassen, and J. A. Mulder. "Path-Oriented Control/Display Augmentation for Perspective Flight-Path Displays." Journal of Guidance, Control, and Dynamics 29, no. 4 (2006): 780–91. http://dx.doi.org/10.2514/1.16469.

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11

SAKAI, Yasuhiro, Yuji ISHINO, Masaya TAKASAKI, and Takeshi MIZUNO. "2A16 Development of a Flux-Path Control Magnetic Suspension System with Swing-Type Control Plates." Proceedings of the Symposium on the Motion and Vibration Control 2010 (2010): _2A16–1_—_2A16–9_. http://dx.doi.org/10.1299/jsmemovic.2010._2a16-1_.

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12

Hu, Chaofang, Lingxue Zhao, Lei Cao, Patrick Tjan, and Na Wang. "Steering control based on model predictive control for obstacle avoidance of unmanned ground vehicle." Measurement and Control 53, no. 3-4 (2020): 501–18. http://dx.doi.org/10.1177/0020294019878871.

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In this paper, a strategy based on model predictive control consisting of path planning and path tracking is designed for obstacle avoidance steering control problem of the unmanned ground vehicle. The path planning controller can reconfigure a new obstacle avoidance reference path, where the constraint of the front-wheel-steering angle is transformed to formulate lateral acceleration constraint. The path tracking controller is designed to realize the accurate and fast following of the reconfigured path, and the control variable of tracking controller is steering angle. In this work, obstacles

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13

Yang, Zeyu, and Liang Hong. "Intelligent Vehicle Path Tracking Control Based on Model Predictive Control." Journal of Research in Science and Engineering 7, no. 3 (2025): 34–41. https://doi.org/10.53469/jrse.2024.07(03).8.

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With the rapid development of intelligent transportation system, the path tracking control of intelligent vehicles has become one of the key technologies. In this paper, the path tracking method of intelligent vehicles based on model predictive control (MPC) is studied deeply. Firstly, the control law for adaptive adjustment of the optimal time domain is designed based on the two-degree-of-freedom vehicle dynamics model and the model control algorithm. Next, the MPC trajectory tracking controller is built based on the error characteristics of the actual front wheel angle and the predicted fron

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14

Pradhan, Somanath, Xiaojun Qiu, and Jinchen Ji. "A Four-Stage Method for Active Control with Online Feedback Path Modelling Using Control Signal." Applied Sciences 9, no. 15 (2019): 2973. http://dx.doi.org/10.3390/app9152973.

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The presence of control signal feedback to the reference microphone in feedforward active control systems deteriorates the control performance. A four-stage method is proposed in this paper to carry out online feedback path modelling with the control signal. It consists of controller initialization, feedback path modelling using decorrelation filters, active control operation, and feedback path change detection for maintaining the control operation. In contrast to the existing auxiliary noise injection method, the proposed method uses five switches and three thresholds to control and maintain

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15

OKAJIMA, Hiroshi, Toru ASAI, and Shigeyasu KAWAJI. "Optimal Velocity Control Method in Path Following Control Problem." Transactions of the Society of Instrument and Control Engineers 44, no. 7 (2008): 566–74. http://dx.doi.org/10.9746/ve.sicetr1965.44.566.

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16

Okajima, H., T. Asai, and S. Kawaji. "Optimal Velocity Control Method in Path Following Control Problem." IFAC Proceedings Volumes 41, no. 2 (2008): 90–95. http://dx.doi.org/10.3182/20080706-5-kr-1001.00015.

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17

QIU, Shi-yu, and Hiroshi MAKINO. "Corner path control using clothoidal interpolation." Journal of the Japan Society for Precision Engineering 56, no. 7 (1990): 1268–73. http://dx.doi.org/10.2493/jjspe.56.1268.

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18

Wang, Bingbo, Xiujuan Ma, Cunchi Wang, Mingjie Zhang, Qianhua Gong, and Lin Gao. "Conserved Control Path in Multilayer Networks." Entropy 24, no. 7 (2022): 979. http://dx.doi.org/10.3390/e24070979.

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The determination of directed control paths in complex networks is important because control paths indicate the structure of the propagation of control signals through edges. A challenging problem is to identify them in complex networked systems characterized by different types of interactions that form multilayer networks. In this study, we describe a graph pattern called the conserved control path, which allows us to model a common control structure among different types of relations. We present a practical conserved control path detection method (CoPath), which is based on a maximum-weighte

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19

Key, Peter, Laurent Massoulié, and Don Towsley. "Path selection and multipath congestion control." Communications of the ACM 54, no. 1 (2011): 109–16. http://dx.doi.org/10.1145/1866739.1866762.

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20

Krustev, Evgeny, and Ljubomir Lilov. "Kinematic path control of robot arms." Robotica 4, no. 2 (1986): 107–16. http://dx.doi.org/10.1017/s0263574700008572.

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SUMMARYPath planning of the end effector motion is here treated from the viewpoint of the path invariance under the transformations of its parametrical representation. Thus, a new method for path planning of the robot arm motion is being developed. Both the problems of finding the end effector time optimal motion and the end effector motion with a prescribed velocity profile along a preplanned path are being solved by the employment of this method. Simulation results are presented and some aspects of implementation are also discussed.

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21

Bergeat, Astrid, and Christian Naulin. "Magnetic control of a reaction path." Nature Chemistry 10, no. 12 (2018): 1177–79. http://dx.doi.org/10.1038/s41557-018-0177-6.

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22

Martin, C. F., Shan Sun, and M. Egerstedt. "Optimal control, statistics and path planning." Mathematical and Computer Modelling 33, no. 1-3 (2001): 237–53. http://dx.doi.org/10.1016/s0895-7177(00)00241-7.

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23

Dubbeldam, D., E. Beerdsen, S. Calero, and B. Smit. "Molecular path control in zeolite membranes." Proceedings of the National Academy of Sciences 102, no. 35 (2005): 12317–20. http://dx.doi.org/10.1073/pnas.0503908102.

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24

Dahl, Ola. "Path constrained robot control — experimental evaluation." Mechatronics 4, no. 2 (1994): 173–98. http://dx.doi.org/10.1016/0957-4158(94)90042-6.

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25

KUMON, Makoto, and Norihiko ADACHI. "Dynamic Parametrization for Path Following Control." Transactions of the Japan Society of Mechanical Engineers Series C 67, no. 656 (2001): 1053–59. http://dx.doi.org/10.1299/kikaic.67.1053.

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26

Aphiratsakun, Narong, and Santi Liwsakphaiboon. "Path Control iRobot Create2 Based Sensors." Proceedings of Engineering and Technology Innovation 16 (August 26, 2020): 45–51. http://dx.doi.org/10.46604/peti.2020.4130.

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Robots become a part of humans’ daily lives. They work faster, more accurate, and tireless compared to humans. As technology developes, robots are more capable of serving people in more tasks than before. The objective of this study is to explain the methodology of controlling iRobot Create2 by using Arduino Uno to command iRobot Create2 to use operation codes. Instead of using a brute-force way to control the robot motion by commands, which may cause the whole path to go wrong if only one command is deflected. However, with the sensors, the robot can adjust the direction path despite the devi

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27

Kumon, Makoto, and Norihiko Adachi. "Dynamic Parameterization for Path Following Control." Asian Journal of Control 3, no. 1 (2008): 27–34. http://dx.doi.org/10.1111/j.1934-6093.2001.tb00039.x.

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28

Maniati, E., P. Potter, NJ Rogers, and BJ Morley. "Control of apoptosis in autoimmunity." Journal of Pathology 214, no. 2 (2008): 190–98. http://dx.doi.org/10.1002/path.2270.

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29

Miao, Baorui, and Chao Han. "Intelligent vehicle obstacle avoidance path-tracking control based on adaptive model predictive control." Mechanical Sciences 14, no. 1 (2023): 247–58. http://dx.doi.org/10.5194/ms-14-247-2023.

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Abstract. In order to solve the problems of low path-tracking accuracy, poor safety, and stability of intelligent vehicles with variable speeds and obstacles on the road, a double-layer adaptive model predictive controller (MPC) is designed. A vehicle point mass model is used in an obstacle avoidance planning controller, and the safety collision distance model is established according to the distance relationship between the vehicle and the obstacle to improve the driving safety of the vehicle. The design of the path-tracking controller is based on the three-degrees-of-freedom dynamics model.

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30

Cui, Peiling, Jingxian He, Jian Cui, and Haitao Li. "Improved Path Planning and Attitude Control Method for Agile Maneuver Satellite with Double-Gimbal Control Moment Gyros." Mathematical Problems in Engineering 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/878724.

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Double-gimbal control moment gyros can implement the satellite attitude maneuver efficiently. In order to reduce the energy consumption of double-gimbal control moment gyros and avoid the singularity state, an attitude maneuver path planning method is proposed by using the improved Fourier basis algorithm. Considering that the choice of the Fourier coefficients is important for the Fourier basis algorithm to converge quickly, a choosing method of the initial Fourier coefficients which can reduce the computational time of the path planning algorithm notably is proposed. Moreover, an attitude-tr

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31

Kong, Qi, Liangliang Zhang, and Xin Xu. "Control algorithm of path planning and path tracking for automated valet parking." Journal of Physics: Conference Series 2232, no. 1 (2022): 012018. http://dx.doi.org/10.1088/1742-6596/2232/1/012018.

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Abstract Automated Valet Parking (AVP) can relieve drivers from having to park. It enables vehicles to drive, park and charge fully automatically. This paper focuses on the first working step of the motion planning module, the global path planner. Therefore, a global path planner will be developed and evaluated based on actual research methods. A route will be computed by using an A^* algorithm based on the occupancy grid map. Subsequently, a smoothing operation will follow to obtain a drivable path. By implementing a single polar polynomial function each vertex is smoothened. The resulting gl

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32

HASHIMOTO, Sho, Takayuki MITSUMOTO, and Masao NAGAI. "21401 Study on Autonomous Path Tracking Control Based on Clothoidal Path Generation." Proceedings of Conference of Kanto Branch 2014.20 (2014): _21401–1_—_21401–2_. http://dx.doi.org/10.1299/jsmekanto.2014.20._21401-1_.

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33

SAMPEI, Mitsuji, and Takeshi ITOH. "Path Planning and Path Tracking Control of Wheeled Vehicles Using NonlinearSystem Theory." Transactions of the Institute of Systems, Control and Information Engineers 6, no. 1 (1993): 37–47. http://dx.doi.org/10.5687/iscie.6.37.

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34

Liu, Lixing, Xu Wang, Xiaosa Wang, et al. "Path Planning and Tracking Control of Tracked Agricultural Machinery Based on Improved A* and Fuzzy Control." Electronics 13, no. 1 (2024): 188. http://dx.doi.org/10.3390/electronics13010188.

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In order to improve the efficiency of agricultural machinery operations and reduce production costs, this article proposes a path planning algorithm based on the improved A* algorithm (IA*) and a tracking controller based on fuzzy sliding mode variable structure control (F-SMC) to meet the operation requirements of tracked agricultural machinery. Firstly, we introduce a heuristic function with variable weights, a penalty, and a fifth-order Bezier curve to make the generated path smoother. On this basis, the ant colony algorithm is introduced to further optimize the obtained path. Subsequently,

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35

Miková, Ľubica, Alexander Gmiterko, Michal Kelemen, et al. "Motion control of nonholonomic robots at low speed." International Journal of Advanced Robotic Systems 17, no. 1 (2020): 172988142090255. http://dx.doi.org/10.1177/1729881420902554.

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Many applications in robotics require precise tracking of the prescribed path. The aim of this article is to develop and verify by computer simulation a control design method which ensures that the “output” of the robot will move along a prescribed path. A virtual vehicle approach algorithm was used to track a predefined vehicle path. The idea behind this algorithm is that the movement of a virtual vehicle on a predefined path is controlled by a differential equation whose input is a control deviation representing the distance between a real and a virtual vehicle. The main advantage of the pat

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36

Tsuchiya, T., and T. Egami. "Robot Path Control with Variable Speed by Preview Control and Adaptive Control." IFAC Proceedings Volumes 23, no. 8 (1990): 121–26. http://dx.doi.org/10.1016/s1474-6670(17)51723-1.

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37

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 (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 d

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38

Liu, Cheng, Jing Sun, and Zaojian Zou. "Integrated Line of Sight and Model Predictive Control for Path Following and Roll Motion Control Using Rudder." Journal of Ship Research 59, no. 02 (2015): 99–112. http://dx.doi.org/10.5957/jsr.2015.59.2.99.

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Roll motion control and path following are two representative marine control problems that have been traditionally treated separately. However, these two problems are closely coupled, as roll motion could cause negative effects on marine surface vessels during path following in seaways and path following actions could cause undesirable roll motion. In this article, an optimal controller is proposed for the integrated path following and roll motion control problem. The rudder, whose actuation amplitude and rate are both limited, is the only control input, while the cross-track error, heading an

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39

Tan, Y. G., D. K. Liu, F. Liu, and Z. D. Zhou. "Digital robust preview control of path tracking." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 219, no. 1 (2005): 111–16. http://dx.doi.org/10.1243/095440705x9434.

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A robust optimal preview control method is presented in this paper for path tracking control problems to improve robustness and tracking precision of path tracking control systems. The known path information is used as reference input signals. Simulation results show that this method is valid not only for improving the performance of highly accurate trajectory control but also for improving system stabilization.

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40

Chun Kiat, Tan, Hungsun Son, and Paw Yew Chai. "UAV Flight Path Control Using Contraction-Based Back-stepping Control." Open Journal of Applied Sciences 03, no. 02 (2013): 65–70. http://dx.doi.org/10.4236/ojapps.2013.32b013.

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41

Codesseira, Victor C., and Eduardo A. Tannuri. "Path Following Control for Autonomous Ship using Model Predictive Control." IFAC-PapersOnLine 54, no. 16 (2021): 57–62. http://dx.doi.org/10.1016/j.ifacol.2021.10.073.

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42

OAKI, Junji, and Tsutomu MITA. "Path control of robot arm using linearlized constraint control method." Journal of the Robotics Society of Japan 3, no. 4 (1985): 285–98. http://dx.doi.org/10.7210/jrsj.3.285.

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43

Lee, Junho, and Hyuk-Jun Chang. "Analysis of explicit model predictive control for path-following control." PLOS ONE 13, no. 3 (2018): e0194110. http://dx.doi.org/10.1371/journal.pone.0194110.

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44

Duarte, Fernando, and Luis Miguel Pires. "SCORBOT arm control using pseudoinverse jacobian control for Bézier path." Brazilian Journal of Technology 8, no. 1 (2025): e76721. https://doi.org/10.38152/bjtv8n1-011.

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The kinematics of manipulators represents a fundamental challenge in the field of robotic automatic control. This challenge involves transitioning between Cartesian space and combinatorial space. The kinematic equations governing motion are formulated using the Denavit-Hartenberg (D-H) notation. This paper introduces a method for the inversion of a SCORBOT ER-V plus manipulator, aimed at meticulously analyzing the arm's movement from one spatial point to another. To implement this solution, it is necessary to identify only the starting and ending points in space, along with the geometric const

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45

Xing, Fei. "The Optical Path Closed-Loop Control System." Advanced Materials Research 998-999 (July 2014): 700–703. http://dx.doi.org/10.4028/www.scientific.net/amr.998-999.700.

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The optical path closed-loop control system mainly applies to large high power equipment that makes the optical path adjust automatically. The paper introduces the principle of optical path closed-loop system. In addition, it introduces the laser spot recognition algorithm and the PC real-time monitoring system. After the test, the system not only can accurately calculate the position of spot, but also realize the monitoring and adjustment of spot.

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46

Ebrahimpour, MohammadReza, and Mihai Lungu. "Finite-Time Path-Following Control of Underactuated AUVs with Actuator Limits Using Disturbance Observer-Based Backstepping Control." Drones 9, no. 1 (2025): 70. https://doi.org/10.3390/drones9010070.

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This paper presents a three-dimensional (3D) robust adaptive finite-time path-following controller for underactuated Autonomous Underwater Vehicles (AUVs), addressing model uncertainties, external disturbances, and actuator magnitude and rate saturations. A path-following error system is built in a path frame using the virtual guidance method. The proposed cascaded closed-loop control scheme can be described in two separate steps: (1) A kinematic law based on a finite-time backstepping control (FTBSC) is introduced to transform the 3D path-following position errors into the command velocities;

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47

Li, Yibo, Qi Cao, and Fang Liu. "Design of control system for driverless tractor." MATEC Web of Conferences 309 (2020): 04001. http://dx.doi.org/10.1051/matecconf/202030904001.

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This paper uses Tianjin TieNiu crawler tractor as the experimental platform to design an unmanned agricultural machinery control system based on RTK-GPS (Differential GPS Technology). The article expounds the overall design of the control system and proposes a path tracking control algorithm. Simulation and experimental results show that the control algorithm can complete the path tracking requirements of unmanned agricultural machinery. The system can control the unmanned agricultural machinery to follow a predetermined route. The speed is 0.55m/s, the maximum error of linear path tracking of

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48

Mut, Murat, and Tamás Terlaky. "On the volumetric path." Optimization Letters 6, no. 4 (2011): 687–93. http://dx.doi.org/10.1007/s11590-011-0293-3.

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49

Krustev, Evgeny, and Ljubomir Lilov. "Extended kinematic path control of robot arms." Robotica 5, no. 1 (1987): 45–53. http://dx.doi.org/10.1017/s0263574700009632.

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SUMMARYPath planning a robot arm motion essentially requires that the constraints of the joint variables and the vector of the joint motion rates are taken into account. In order to satisfy the constraints of the joint variables a sliding mode is being employed together with the developed kinematic path control method. The extended form of the kinematic path control method, here proposed, treats simultaneously the constraints of the joint variables and the vector of joint motion rates in path planning a robot arm motion.

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50

Klauer, Christian, Manuel Schwabe, and Hamid Mobalegh. "Path Tracking Control for Urban Autonomous Driving." IFAC-PapersOnLine 53, no. 2 (2020): 15705–12. http://dx.doi.org/10.1016/j.ifacol.2020.12.2569.

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