Статті в журналах: "Tracking trajectory"

1

Howard, Srimant. "Multiple Trajectory Tracking." Scholarpedia 7, no. 4 (2012): 11287. http://dx.doi.org/10.4249/scholarpedia.11287.

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2

Han, Mei, Wei Xu, Hai Tao, and Yihong Gong. "Multi-object trajectory tracking." Machine Vision and Applications 18, no. 3-4 (March 31, 2007): 221–32. http://dx.doi.org/10.1007/s00138-007-0071-5.

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3

Gu, Jinheng, Shicheng He, Jianbo Dai, Dong Wei, Haifeng Yan, Chao Tan, Zhongbin Wang, and Lei Si. "A Walking Trajectory Tracking Control Based on Uncertainties Estimation for a Drilling Robot for Rockburst Prevention." Machines 12, no. 5 (April 28, 2024): 298. http://dx.doi.org/10.3390/machines12050298.

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A walking trajectory tracking control approach for a walking electrohydraulic control system is developed to reduce the walking trajectory tracking deviation and enhance robustness. The model uncertainties are estimated by a designed state observer. A saturation function is used to attenuate sliding mode chattering in the designed sliding mode controller. Additionally, a walking trajectory tracking control strategy is proposed to improve the walking trajectory tracking performance in terms of response time, tracking precision, and robustness, including walking longitudinal and lateral trajectory tracking controllers. Finally, simulation and experimental results are employed to verify the trajectory tracking performance and observability of the model uncertainties. The results testify that the proposed approach is better than other comparative methods, and the longitudinal and lateral trajectory tracking average absolute errors are controlled in 10.23 mm and 22.34 mm, respectively, thereby improving the walking trajectory tracking performance of the walking electrohydraulic control system for the coal mine drilling robot for rockburst prevention.

4

Vitalii, Berdyshev. "OBSERVER’S TRAJECTORY TRACKING OBJECT BYPASSING OBSTACLE ON THE SHORTEST CURVE." Eurasian Journal of Mathematical and Computer Applications 9, no. 4 (December 2021): 4–16. http://dx.doi.org/10.32523/2306-6172-2021-9-4-4-16.

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Motion of some object is considered. The object t moves from the initial point t∗ to the final one t ∗ . But since absent of the direct path, he should bypass an obstacle a connected bodily set G. It is supposed that t moves by the most short trajectory T = Tt , and the trajectory T is a convex curve. The observer’s f task is to find the trajectory Tf that provides tracking the object on the most part of the object’s motion and, if possible, the lesser object’s stealth of motion along the trajectory T . The latency is defined by the distance that the observer must pass to see the object in the field of vision. The object and observer start at the same initial instant, and their velocities are equal. In the paper, examples of the trajectories Tf in R 2 are presented, on which the observer can see the object’s trajectory T ; also, the value of the object’s latency is shown for the invisible parts of the trajectory T . The variant of Tf in R 3 is shown.

5

Rozumnyi, Denys, Jan Kotera, Filip Šroubek, and Jiří Matas. "Tracking by Deblatting." International Journal of Computer Vision 129, no. 9 (June 22, 2021): 2583–604. http://dx.doi.org/10.1007/s11263-021-01480-w.

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AbstractObjects moving at high speed along complex trajectories often appear in videos, especially videos of sports. Such objects travel a considerable distance during exposure time of a single frame, and therefore, their position in the frame is not well defined. They appear as semi-transparent streaks due to the motion blur and cannot be reliably tracked by general trackers. We propose a novel approach called Tracking by Deblatting based on the observation that motion blur is directly related to the intra-frame trajectory of an object. Blur is estimated by solving two intertwined inverse problems, blind deblurring and image matting, which we call deblatting. By postprocessing, non-causal Tracking by Deblatting estimates continuous, complete, and accurate object trajectories for the whole sequence. Tracked objects are precisely localized with higher temporal resolution than by conventional trackers. Energy minimization by dynamic programming is used to detect abrupt changes of motion, called bounces. High-order polynomials are then fitted to smooth trajectory segments between bounces. The output is a continuous trajectory function that assigns location for every real-valued time stamp from zero to the number of frames. The proposed algorithm was evaluated on a newly created dataset of videos from a high-speed camera using a novel Trajectory-IoU metric that generalizes the traditional Intersection over Union and measures the accuracy of the intra-frame trajectory. The proposed method outperforms the baselines both in recall and trajectory accuracy. Additionally, we show that from the trajectory function precise physical calculations are possible, such as radius, gravity, and sub-frame object velocity. Velocity estimation is compared to the high-speed camera measurements and radars. Results show high performance of the proposed method in terms of Trajectory-IoU, recall, and velocity estimation.

6

Hu, Zhen, Daqi Zhu, Caicha Cui, and Bing Sun. "Trajectory Tracking and Re-planning with Model Predictive Control of Autonomous Underwater Vehicles." Journal of Navigation 72, no. 2 (September 21, 2018): 321–41. http://dx.doi.org/10.1017/s0373463318000668.

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The trajectory tracking of Autonomous Underwater Vehicles (AUV) is an important research topic. However, in the traditional research into AUV trajectory tracking control, the AUV often follows human-set trajectories without obstacles, and trajectory planning and tracking are separated. Focusing on this separation, a trajectory re-planning controller based on Model Predictive Control (MPC) is designed and added into the trajectory tracking controller to form a new control system. Firstly, an obstacle avoidance function is set up for the design of an MPC trajectory re-planning controller, so that the re-planned trajectory produced by the re-planning controller can avoid obstacles. Then, the tracking controller in the MPC receives the re-planned trajectory and obtains the optimal tracking control law after calculating the object function with a Sequential Quadratic Programming (SQP) optimisation algorithm. Lastly, in a backstepping algorithm, the speed jump can be sharp while the MPC tracking controller can solve the speed jump problem. Simulation results of different obstacles and trajectories demonstrate the efficiency of the proposed MPC trajectory re-planning tracking control algorithm for AUVs.

7

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.

8

Mullier, Olivier, and Julien Alexandre dit Sandretto. "Validated Trajectory Tracking using Flatness." Acta Cybernetica 25, no. 1 (February 3, 2021): 85–99. http://dx.doi.org/10.14232/actacyb.285729.

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The problem of a safe trajectory tracking is addressed in this paper. It consists in using the results of a validated path planner providing a set of safe trajectories to produce the set of controls to apply to remain inside this set of planned trajectories while avoiding static obstacles. This computation is performed using the differential flatness of many dynamical systems. The method is illustrated in the case of the Dubins car.

9

Lange, Ralph, Frank Dürr, and Kurt Rothermel. "Efficient real-time trajectory tracking." VLDB Journal 20, no. 5 (June 12, 2011): 671–94. http://dx.doi.org/10.1007/s00778-011-0237-7.

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10

Qu, Li Ping, Yong Yin Qu, and Hao Han Zhou. "Study on Iterative Learning Control of Mobile Robot." Applied Mechanics and Materials 775 (July 2015): 319–23. http://dx.doi.org/10.4028/www.scientific.net/amm.775.319.

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In order to solve the mobile robot trajectory tracking problem better, an iterative learning control (ILC) was applied. And the efficiency of mobile robot trajectory tracking was improved. From the simulation result, ILC with forgetting factor has very good performance for solving mobile robot trajectory tracking problem, and the smooth of trajectory tracking process also improved well.

11

Chen, Yung-Hsiang, and Yung-Yue Chen. "Trajectory Tracking Design for a Swarm of Autonomous Mobile Robots: A Nonlinear Adaptive Optimal Approach." Mathematics 10, no. 20 (October 20, 2022): 3901. http://dx.doi.org/10.3390/math10203901.

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This research presents a nonlinear adaptive optimal control approach to the trajectory tracking problem of a swarm of autonomous mobile robots. Mathematically, finding an analytical adaptive control solution that meets the H2 performance index for the trajectory tracking problem when controlling a swarm of autonomous mobile robots is an almost impossible task, due to the great complexity and high dimensions of the dynamics. For deriving an analytical adaptive control law for this tracking problem, a particular formulation for the trajectory tracking error dynamics between a swarm of autonomous mobile robots and the desired trajectory is made via a filter link. Based on this prior analysis of the trajectory tracking error dynamics, a closed-form adaptive control law is analytically derived from a high-dimensional nonlinear partial differential equation, which is equivalent to solving the trajectory tracking problem of a swarm of autonomous mobile robots with respect to an H2 performance index. This delivered adaptive nonlinear control solution offers the advantages of a simple control structure and good energy-saving performance. From the trajectory tracking verification, this proposed control approach possesses satisfactory trajectory tracking performance for a swarm of autonomous mobile robots, even under the effects of huge modeling uncertainties.

12

Wu, Qiong, Hua Chen, and Baolong Liu. "A Multi-DOF Manipulator Joint Trajectory Tracking and Monitoring Method Based on Decision Tree." Mathematical Problems in Engineering 2022 (October 15, 2022): 1–10. http://dx.doi.org/10.1155/2022/6375727.

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Understanding trajectory tracking control concerns is crucial for industrial-grade manipulators to provide precise and risk-free operations for the safe environment. Consequently, the robot arms need precise aim for tracking a given target trajectory by the trajectory control input driving torque which can use smart AI-based techniques for precision. Similarly, a decision tree is a soft computing-based method of feature space partitioning which can certainly allow the movement of robots in an accurate manner. The control of robot arms is an important aspect for automating the process of sustainable development. Aiming at the problem of poor tracking accuracy of traditional Multi-Degree-of-Freedom Manipulator Joint Trajectory Monitoring (Multi-DoF MJTM) and long monitoring delay, this article proposes a Multi-DOF manipulator joint trajectory tracking method based on decision tree. The Multi-DoF manipulator is developed for the adaptive control object of the working machine, and it is combined with the output response feature to construct the kinematics model of the Multi-DoF manipulator mechanism of the walking machine. The joint trajectory reconstruction of the running trajectory is used to obtain the joint trajectory deviation of the Multi-DoF manipulator’s running trajectory through the multi-measurement system. Based on this, the Multi-DoF manipulator’s running trajectory joint trajectory tracking control equation is obtained to realize the joint trajectory tracking and monitoring of the manipulator. The features for a safe environment are also integrated. The experimental results show that the proposed method has high accuracy in tracking the trajectory of Multi-DoF manipulator joints, and the time delay for tracking and monitoring the trajectory of manipulator’s joints is also optimized.

13

Yu, Yi, and Peng Han. "Trajectory tracking method based on the circulation of feasible path planning." PLOS ONE 16, no. 6 (June 7, 2021): e0252542. http://dx.doi.org/10.1371/journal.pone.0252542.

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The control method is the central point of the unmanned vehicles. As the core system to guarantee the properties of self-decision and trajectory tracking of the unmanned vehicles, a new kind of trajectory tracking method based on the circulation of feasible path planning for the unmanned vehicles are proposed in this article which considered the dynamics and kinematics characteristics of vehicles. The multi-trace-points cooperative trajectory tracking control strategy on the basis of the circulation of feasible path generation method is proposed and the lateral controller is designed for trajectory tracking. The process of feasible path generation is conducted once the tracking error exceeded. A simulation platform of the trajectory tracking simulation of unmanned vehicles is built considering the mechanical properties of system elements and the mechanical characteristics. Finally, the proposed trajectory tracking method is verified. The tracking error would be reduced to make sure the vehicles move along the pre-set virtual track.

14

Sal, Firat. "Variance constrained trajectory tracking for tandem-rotor helicopters." Aircraft Engineering and Aerospace Technology 92, no. 3 (January 20, 2020): 398–403. http://dx.doi.org/10.1108/aeat-10-2019-0205.

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Purpose This paper aims to present novel results for trajectory tracking of tandem-rotor helicopters via variance-constrained controllers. Design/methodology/approach Regarding these purposes, linearized tandem-rotor helicopter models are benefitted. Findings Variance-constrained controllers are very beneficial for trajectory tracking of tandem-rotor helicopters while there exists variance bounds on outputs of interest. Practical implications Variance-constrained controllers can be used for cheaper tandem-rotor helicopter operations with high trajectory tracking quality. Originality/value Applying variance-constrained control strategy for tandem-rotor helicopters during trajectory tracking. This also causes less fuel consumption and green environment, and good trajectory tracking quality.

15

Cao, Cuiping, Hai Yu, and Yun Liu. "Automatic Tracking Method of Basketball Flight Trajectory Based on Data Fusion and Sparse Representation Model." Complexity 2021 (September 29, 2021): 1–9. http://dx.doi.org/10.1155/2021/9568753.

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The appearance model of flying basketball obtained by the traditional basketball flight trajectory tracking method is not accurate, which leads the anti-interference performance of trajectory tracking not ideal. Based on data fusion and sparse representation model, a new automatic trajectory tracking method is proposed. Firstly, the relevant technologies of basketball flight trajectory automatic tracking are studied and summarized, and then the method is studied. The specific implementation steps of this method are as follows: the features of flying basketball images were extracted by the target feature extraction algorithm, and the appearance model of flying basketball was built based on sparse representation. Data fusion technology and particle filter algorithm are combined to realize automatic tracking of basketball flight path. Through three axial basketball trajectories of automatic tracking test and noise test and verify the design method under the 3D world coordinate system to achieve the X, Y, and Z axis up more accurate tracking, at the same time, after applying measurement signal to noise, automatic trajectory tracking results affected by some, but still managed to realize the trajectory tracking.

16

Guo, Zhenqi, Junfeng Zhang, Fancong Zeng, Zhijiang Zuo, Libo Pan, and Han Li. "A trajectory tracking control system for paddle boat in intelligent aquaculture." PLOS ONE 18, no. 8 (August 17, 2023): e0290246. http://dx.doi.org/10.1371/journal.pone.0290246.

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Trajectory tracking plays a notable role in unmanned surface vehicles (USV), especially for the emerging intelligent aquaculture, as the level of integration, high-efficiency, and low-labor-intensity of such USV is determined by trajectory tracking. Here, we report a generic trajectory tracking control system for a paddle boat by establishing a three-degree-of-freedom kinematics model, which could precisely characterize the relationship between velocities, forces and moments of the paddle boat. A Pixhawk 4 as the core controller of the hardware system could be integrated with the other hardware submodules and could complete the wireless data transmission, monitoring and remote control functions. Meanwhile, we establish a fuzzy rule table, consider the advantages of line-of-sight (LOS) guidance and fuzzy adaptive proportional-integral-differential (PID) algorithm, combine the two parts and apply them as the key algorithm in the trajectory tracking of the paddle boat. Demonstrations include trajectory tracking effect at different velocities, turning effect at left-turn moment, and trajectory tracking effect at different turning angles. The results show that the paddle boat is able to travel under the trajectory formed by following the planned waypoints within the error allowed, which is called effective trajectory tracking. And can offer an alternative pathway toward achieving effective trajectory tracking control in advanced intelligent aquaculture USV for smartly and wirelessly operated pond drug spraying.

17

Bingul, Zafer, and Kursad Gul. "Intelligent-PID with PD Feedforward Trajectory Tracking Control of an Autonomous Underwater Vehicle." Machines 11, no. 2 (February 17, 2023): 300. http://dx.doi.org/10.3390/machines11020300.

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This paper investigates the model-free trajectory tracking control problem for an autonomous underwater vehicle (AUV) subject to the ocean currents, external disturbances, measurement noise, model parameter uncertainty, initial tracking errors, and thruster malfunction. A novel control architecture based on model-free control principles is presented to guarantee stable and precise trajectory tracking performance in the complex underwater environment for AUVs. In the proposed hybrid controller, intelligent-PID (i-PID) and PD feedforward controllers are combined to achieve better disturbance rejections and initial tracking error compensations while keeping the trajectory tracking precision. A mathematical model of an AUV is derived, and ocean current dynamics are included to obtain better fidelity when examining ocean current effects. In order to evaluate the trajectory tracking control performance of the proposed controller, computer simulations are conducted on the LIVA AUV with a compelling trajectory under various disturbances. The results are compared with the two degrees-of-freedom (DOF) i-PID, i-PID, and PID controllers to examine control performance improvements with the guaranteed trajectory tracking stability. The comparative results revealed that the i-PID with PD feedforward controller provides an effective trajectory tracking control performance and excellent disturbance rejections for the entire trajectory of the AUV.

18

Yoon, Namkyung, Dongjae Lee, Kiseok Kim, Taehoon Yoo, Hyeontae Joo, and Hwangnam Kim. "STEAM: Spatial Trajectory Enhanced Attention Mechanism for Abnormal UAV Trajectory Detection." Applied Sciences 14, no. 1 (December 27, 2023): 248. http://dx.doi.org/10.3390/app14010248.

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Accurate unmanned aerial vehicle (UAV) trajectory tracking is crucial for the successful execution of UAV missions. Traditional global positioning system (GPS) methods face limitations in complex environments, and visual observation becomes challenging with distance and in low-light conditions. To address this challenge, we propose a comprehensive framework for UAV trajectory verification, integrating a range-based ultra-wideband (UWB) positioning system and advanced image processing technologies. Our key contribution is the development of the Spatial Trajectory Enhanced Attention Mechanism (STEAM), a novel attention module specifically designed for analyzing and classifying UAV trajectory patterns. This system enables real-time UAV trajectory tracking and classification, facilitating swift and accurate assessment of adherence to predefined optimal trajectories. Another major contribution of our work is the integration of a UWB system for precise UAV location tracking, complemented by our advanced image processing approach that includes a deep neural network (DNN) for interpolating missing data from images, thereby significantly enhancing the model’s ability to detect abnormal maneuvers. Our experimental results demonstrate the effectiveness of the proposed framework in UAV trajectory tracking, showcasing its robust performance irrespective of raw data quality. Furthermore, we validate the framework’s performance using a lightweight learning model, emphasizing both its computational efficiency and exceptional classification accuracy.

19

QI, Zhaohui, Jin ZHANG, Yuanzhuo WANG, Jia WANG, Mengrong XU, and Cheng ZHANG. "Trajectory tracking control method based on adaptive super-twisting sliding mode." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 40, no. 5 (October 2022): 1109–15. http://dx.doi.org/10.1051/jnwpu/20224051109.

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Aiming at the problem of trajectory tracking control in the process of missile network formation flying, based on the optimal nominal trajectory obtained by solving the two-point boundary value problem, combined with the anti-jamming characteristics of the sliding mode controller, a trajectory tracking control method based on the adaptive super-twisting sliding mode is proposed. First, on the basis of the terminal guidance section model, the two-point boundary value problem is solved through the idea of nonlinear programming to obtain the optimal nominal trajectory; Secondly, the tracking controller based on state deviation is designed in combination with the adaptive super-twisting sliding mode algorithm; Finally, the LQR trajectory tracking control method is introduced as a comparison method, and the effectiveness and feasibility of the sliding mode trajectory tracking method in the presence of initial state errors are verified by simulations, and the Monte Carlo simulation shows that the proposed method has good trajectory tracking control effect in the presence of different initial state errors.

20

Li, Lei, Jun Li, and Shiyi Zhang. "Review article: State-of-the-art trajectory tracking of autonomous vehicles." Mechanical Sciences 12, no. 1 (April 16, 2021): 419–32. http://dx.doi.org/10.5194/ms-12-419-2021.

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Abstract. Air pollution, energy consumption, and human safety issues have aroused people's concern around the world. This phenomenon could be significantly alleviated with the development of automatic driving techniques, artificial intelligence, and computer science. Autonomous vehicles can be generally modularized as environment perception, path planning, and trajectory tracking. Trajectory tracking is a fundamental part of autonomous vehicles which controls the autonomous vehicles effectively and stably to track the reference trajectory that is predetermined by the path planning module. In this paper, a review of the state-of-the-art trajectory tracking of autonomous vehicles is presented. Both the trajectory tracking methods and the most commonly used trajectory tracking controllers of autonomous vehicles, besides state-of-art research studies of these controllers, are described.

21

Saleh, Ameer L., Maab A. Hussain, and Sahar M. Klim. "Optimal Trajectory Tracking Control for a Wheeled Mobile Robot Using Fractional Order PID Controller." Journal of University of Babylon for Engineering Sciences 26, no. 4 (February 20, 2018): 292–306. http://dx.doi.org/10.29196/jubes.v26i4.1087.

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This paper present an optimal Fractional Order PID (FOPID) controller based on Particle Swarm Optimization (PSO) for controlling the trajectory tracking of Wheeled Mobile Robot(WMR).The issue of trajectory tracking with given a desired reference velocity is minimized to get the distance and deviation angle equal to zero, to realize the objective of trajectory tracking a two FOPID controllers are used for velocity control and azimuth control to implement the trajectory tracking control. A path planning and path tracking methodologies are used to give different desired tracking trajectories. PSO algorithm is using to find the optimal parameters of FOPID controllers. The kinematic and dynamic models of wheeled mobile robot for desired trajectory tracking with PSO algorithm are simulated in Simulink-Matlab. Simulation results show that the optimal FOPID controllers are more effective and has better dynamic performance than the conventional methods.

22

Zhai, Guanyu, Jundong Zhang, Shuyun Wu, and Yongkang Wang. "Predefined-Time Tracking Control of Unmanned Surface Vehicle under Complex Time-Varying Disturbances." Electronics 13, no. 8 (April 16, 2024): 1510. http://dx.doi.org/10.3390/electronics13081510.

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Aiming at the unmanned surface vehicle (USV) trajectory tracking control under complex time-varying environment, a predefined-time convergence sliding mode disturbance observer (PTC-SMO) is introduced to effectively handle the internal parameter uncertainties and external environmental disturbances, thereby guaranteeing precise compensation of the lumped disturbance term within a set time. Then, in order to achieve precise tracking of the desired trajectory using USV under a predetermined time constraint, a novel fast trajectory tracking control strategy with predefined-time convergence (PTC-FTTCS) is established to improve tracking performance and ensure that the trajectory tracking error converges quickly in the predefined time. Through rigorous comparative simulation under ideal conditions and time-varying disturbances, the results demonstrate reliable trajectory tracking and disturbance handling effects, and the tracking performance and disturbance observation performance are significantly better than state-of-the-art methods.

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Liu, Xin, Shuwei Ren, Lei Zhang, Wei Shen, and Yubo Tu. "Research on Dynamic Path Planning and Tracking Control for Ship Collision Avoidance." Journal of Physics: Conference Series 2607, no. 1 (October 1, 2023): 012012. http://dx.doi.org/10.1088/1742-6596/2607/1/012012.

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Abstract Ship collisions are prevalent every year, leading to significant maritime traffic accidents. This paper presents research on dynamic path planning and tracking control for ship collision avoidance by integrating ship automatic avoidance technology to address this issue. We conducted a comprehensive study on artificial potential fields, trajectory tracking, and route trajectory tracking in response to the current state of ship collision avoidance and trajectory tracking. The study employed vector decomposition and slider control as research methods to analyze, optimize, and modify ship collision avoidance methods. Additionally, we carried out collision avoidance simulations using MATLAB to verify the stability and safety of ship trajectory tracking under various methods to advance the research on ship collision avoidance and trajectory. The proposed approach has the potential to significantly reduce ship collisions and enhance ship trajectory safety.

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Zak, Andrzej. "Trajectory-Tracking Control of Underwater Vehicles." Solid State Phenomena 196 (February 2013): 156–65. http://dx.doi.org/10.4028/www.scientific.net/ssp.196.156.

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The main aim of paper is to introduce the results of research concentrated on controlling remotely operated underwater vehicle which task is trajectory tracking with high accuracy. Firstly the problem of trajectory tracking and its formal and mathematical description were introduced. Next the proposed fuzzy autopilot which assure high precision of trajectory tracking by underwater vehicle was presented. At the end the example results of research on trajectory tracking in environment without and with disturbance were presented. The paper is finished by summary which include conclusions derive from results of research.

25

Qin, Hao. "Trajectory Tracking Method of Volleyball Player’s Arm Hitting Image Based on D-P Algorithm." Scientific Programming 2021 (December 3, 2021): 1–9. http://dx.doi.org/10.1155/2021/4848036.

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Aiming at the problems of poor image tracking effect, low precision, and long time in the process of image tracking of volleyball player’s arm hitting, a volleyball player's arm hitting image tracking method based on D-P algorithm is proposed. This paper analyzes the basic concept, basic principle, and basic equation of D-P algorithm and collects the arm stroke trajectory image of volleyball players under the three-dimensional visual model. Using wavelet multiscale decomposition method, the arm stroke trajectory of volleyball players is filtered, and the edge contour feature points of the arm stroke image of volleyball players are extracted. Using the gray histogram feature extraction method, the gray information of volleyball player's arm hitting trajectory image is enhanced. Combined with pixel adaptive enhancement technology, the key action feature points of volleyball player's arm hitting image trajectory are located. Based on D-P algorithm, the volleyball player's arm hitting image trajectory is adjusted and modified to realize the correct tracking of volleyball player's arm hitting image trajectory. The experimental results show that the trajectory tracking effect of volleyball player's arm hitting image is better, which can effectively improve the tracking accuracy and shorten the tracking time.

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Chen, Yung-Hsiang, and Yung-Yue Chen. "Nonlinear Adaptive Fuzzy Control Design for Wheeled Mobile Robots with Using the Skew Symmetrical Property." Symmetry 15, no. 1 (January 12, 2023): 221. http://dx.doi.org/10.3390/sym15010221.

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This research presents a nonlinear adaptive fuzzy control method as an analytical design and a simple control structure for the trajectory tracking problem in wheeled mobile robots with skew symmetrical property. For this trajectory tracking problem in wheeled mobile robots, it is not easy to find an analytical adaptive fuzzy control solution due to the complicated error dynamics between the controlled wheeled mobile robots and desired trajectories. For deriving the analytical adaptive fuzzy control law of this trajectory tracking problem, a filter link is firstly adopted to find the solvable error dynamics, then the research is based on the skew symmetrical property of the transformed error dynamics. This proposed nonlinear adaptive fuzzy control solution has the advantages of low computational resource consumption and elimination of modeling uncertainties. From the results for tracking two simulation scenarios (an S type trajectory and a square trajectory), the proposed nonlinear adaptive fuzzy control method demonstrates a satisfactory trajectory tracking performance for the trajectory tracking problem in wheeled mobile robots with huge modeling uncertainties and outperforms the existing H2 control method.

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Dong, Yu Bing, Ying Sun, and Ming Jing Li. "Moving Target Tracking Based on Trajectory Prediction." Advanced Materials Research 1070-1072 (December 2014): 2062–65. http://dx.doi.org/10.4028/www.scientific.net/amr.1070-1072.2062.

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An improved tracking method based on trajectory prediction is proposed and studied. The moving target tracking system is given and described. In order to fast and efficient tracking, a mathematical model of trajectory prediction of moving target is established. A large of experiments are carried by MALTAB. The results show that the improved method is better, improves the tracking speed and tracking precision.

28

A, Henna. "Autonomous Trajectory Tracking and Contouring Control of Three Dimensional CNC." International Journal of Trend in Scientific Research and Development Volume-2, Issue-2 (February 28, 2018): 435–38. http://dx.doi.org/10.31142/ijtsrd9419.

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Li, Menggang, Kun Hu, Weiwei He, Eryi Hu, Chaoquan Tang, and Gongbo Zhou. "Research on Trajectory Planning and Tracking Methods for Coal Mine Mobile Robots." Applied Sciences 13, no. 17 (August 30, 2023): 9789. http://dx.doi.org/10.3390/app13179789.

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Coal Mine Mobile Robots (CMRRs) are generally large in size and inertia, while narrow laneway space and bumpy terrain pose great challenges to CMRR’s planning and control. Aiming at the trajectory planning and tracking problems of CMRR, a new trajectory class MINCO is derived in detail based on the properties of differential flat systems. A trajectory planning method based on MINCO trajectory and safety corridor constraints constructed with underground environmental constraints is further proposed. A trajectory tracking method based on model predictive control (MPC) is further proposed. The prediction model of MPC is constructed by a kinematics model and transformed into a standard quadratic programming problem according to the cost function of a trajectory tracking target. Finally, large quantities of field tests were carried out for the proposed approaches. The results show that the proposed planning algorithm based on MINCO trajectory can achieve good avoidance effects within 10 planning attempts in different obstacle scenarios, and the trajectory is smoother compared to the Fast-Planner algorithm, with shorter trajectory length and less planning time. The tracking error of MPC is always less than 0.05 m in different underground scenarios, having a more adaptable trajectory tracking effect than PID.

30

Gan, Wenyang, Tianxing Xia, and Zhenzhong Chu. "A Prognosis Technique Based on Improved GWO-NMPC to Improve the Trajectory Tracking Control System Reliability of Unmanned Underwater Vehicles." Electronics 12, no. 4 (February 12, 2023): 921. http://dx.doi.org/10.3390/electronics12040921.

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The dynamics model of the unmanned underwater vehicle (UUV) system is highly nonlinear, multi-degree-of-freedom, strongly coupled, and time-varying. Its motion control has been a complex problem due to the unknown information about and the uncertainty of the working environment. To improve the performance and reliability of UUV trajectory tracking control, a trajectory tracking method based on nonlinear model predictive control is designed, and an improved gray wolf optimization (IGWO) is proposed for the optimization of nonlinear model predictive control. The convergence factor of IGWO is designed as a nonlinear attenuation function, and the memory function is added to the position update equation to enhance the effect of trajectory tracking control. Through the simulation in the ROS environment, the influence of the convergence factor on the convergence rate of trajectory tracking error and tracking control performance is obtained. By comparing the tracking effects of several groups of reference trajectories, it is shown that the proposed method is universally applicable and effective to the trajectory tracking control of UUV. Compared with traditional gray wolf optimization (GWO), SQP, and other optimization algorithms, the reliability of the proposed method for UUV trajectory tracking control is demonstrated.

31

Wang, Zhiwei, and Yuxiang Hu. "Analysis of Badminton Movement Cognition Algorithm Based on Track Linear Capture." Wireless Communications and Mobile Computing 2022 (June 9, 2022): 1–10. http://dx.doi.org/10.1155/2022/7137659.

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At present, more and more sports science and technology are being explored and applied in competitive sports. The birth and popularization of video tracking and capturing technology have provided more fair and just perspectives for many sports events. Track linear capture can replay the player’s behavior in real time, the flight path of the badminton can be analyzed in 3D stereoscopic analysis, and the ball’s motion trajectory can be calculated more accurately. In this paper, an objective trajectory tracking and prediction model is constructed based on the motion cognition algorithm, and the motion characteristics of the objective are extracted from the limited historical trajectory of the objective to achieve more accurate trajectory tracking. Then, the trajectory tracking model is applied to the objective tracking framework to obtain ideal objective tracking results. At the same time, in order to make use of the interaction between scene information and objective, this paper improves the trajectory tracking model. The trajectory prediction model based on neural network is constructed, which learns the pedestrian motion characteristics from the pedestrian trajectory data of the target tracking scene offline and uses its “memory” online to generate the implicit depth motion characteristics of the target according to the limited historical information of the target. It also predicts the most likely location of the future target and calculates the motion similarity between the targets. Finally, a simulation experiment platform is built to prove the effectiveness of the trajectory tracking model and objective tracking algorithm proposed in this paper. Through the research results of this paper, it can play a role in verifying the referee’s judgment on the penalty of some key balls, which is more conducive to maintaining the fairness of the game, and more helpful for athletes to optimize their exercise results according to scientific basis, and has the function of improving their performance.

32

Yin, Chengqiang, Shourui Wang, Jie Gao, and Xiaowei Li. "Trajectory tracking for agricultural tractor based on improved fuzzy sliding mode control." PLOS ONE 18, no. 4 (April 6, 2023): e0283961. http://dx.doi.org/10.1371/journal.pone.0283961.

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Trajectory tracking is one of the key technologies for tractor automatic navigation. Its main purpose is to adjust the steering mechanism of the tractor to follow the planned trajectory. Thus, in this paper a trajectory tracking control system is designed for an agricultural tractor with the electric power steering mechanism. A DC brush motor is added on the steering column of the tractor and the hardware circuits for the steering controller are designed to control the front wheel angel. The three degrees of freedom model of the tractor is established, and a trajectory tracking control system is proposed including a fuzzy sliding mode controller and a steering angle tracking controller designed according to the internal mode control and minimized sensitivity theory. The effectiveness of the designed trajectory tracking control system is demonstrated by simulation analyses in reference to the planed trajectory.

33

Shi, Zhang Song, Pi Xu Zhang, Rui Li, and Shen Wang. "An Adaptive Tracking Algorithm of Maneuvering Target." Advanced Materials Research 383-390 (November 2011): 2179–83. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.2179.

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In the existing process of maneuvering target tracking, the target’s actual trajectory can not be foreseen precisely. A commonly attempt is under the assumption that the target’s trajectory is prefixed. The prefixed model is usually not inosculated with the target’s actual trajectory, and the target’s tracking precision can not be guaranteed. In this paper a parameter identification model is presented. This model identifies the target’s trajectory dynamically, adapts to the variation of working cycle, and covers kinds of possible state of the maneuvering target. An adaptive filtering is then employed to analyze the tracking. We call it as a whole adaptive tracking in this paper. Simulations verify that this adaptive tracking algorithm effectively improves the tracking precision.

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Ratajczak, A. "Trajectory reproduction and trajectory tracking problem for the nonholonomic systems." Bulletin of the Polish Academy of Sciences Technical Sciences 64, no. 1 (March 1, 2016): 63–70. http://dx.doi.org/10.1515/bpasts-2016-0008.

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Abstract This paper introduces a new algorithm of trajectory reproduction and trajectory tracking for nonholonomic systems. The endogenous configuration space approach is employed as a guideline in the algorithm derivation. The derivation uses a trajectory reproduction error, which is an integral of the difference between the resultant trajectory and the desired trajectory over the motion horizon. Such a definition of the error allows to solve both the trajectory reproduction as well as the trajectory tracking problem. Considerable attention in the paper has been paid to the implementation aspects of the algorithm. The nonparametric approach is used together with a higher order of the integration method. The algorithm efficiency is illustrated with computer simulations accomplished for two nonholonomic systems: the dynamics of the double pendulum with a passive joint, and the kinematics of the unicycle.

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Sugiyama, Yuta, and Shinji Shinnaka. "A New Two-Dimensional Trajectory Tracking Method Using Trajectory Errors." IEEJ Transactions on Industry Applications 127, no. 11 (2007): 1148–56. http://dx.doi.org/10.1541/ieejias.127.1148.

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36

Chen, Yuxiao, Huei Peng, and Jessy W. Grizzle. "Fast Trajectory Planning and Robust Trajectory Tracking for Pedestrian Avoidance." IEEE Access 5 (2017): 9304–17. http://dx.doi.org/10.1109/access.2017.2707322.

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37

Ma, Hao, Wenhui Pei, and Qi Zhang. "Battery Energy Consumption Analysis of Automated Vehicles Based on MPC Trajectory Tracking Control." Electrochem 3, no. 3 (June 28, 2022): 337–46. http://dx.doi.org/10.3390/electrochem3030023.

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In the field of automated technology research and development, trajectory tracking plays a crucial role in the energy consumption of the vehicle’s power battery. Reducing the deviation between the actual trajectory and the reference trajectory is the focus of trajectory tracking research. This paper proposes the use of the model predictive control (MPC) method to reduce the deviation of lateral and longitudinal position between the actual driving trajectory and the reference trajectory. First, the driving conditions of the vehicle are reflected by establishing the vehicle dynamics model. Then, the MPC trajectory tracking controller is built by designing the objective function with constraints; Finally, the feasibility of this approach was verified by a joint Carsim-Simulink simulation. The simulation results show that the MPC controller designed in this paper can track the trajectory better, and reduce the lateral and longitudinal position deviation. To a certain extent, the battery energy consumption is reduced and the accuracy of the tracking trajectory and the safety of vehicle driving are improved.

38

Hao, Bo, Fan Li, and Jian Hui Zhao. "Research of Trajectory Tracking Control of Cruise Missile Based on Observer." Applied Mechanics and Materials 184-185 (June 2012): 1599–602. http://dx.doi.org/10.4028/www.scientific.net/amm.184-185.1599.

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To achieve cruise missile accurate trajectory tracking control, an observer-based tracking control method is designed. An observer is developed to estimate the states and control signals of desired trajectory as the inputs of the tracking controller. The linear quadratic optimal control is used to realize full-state feedback control for trajectory tracking. A certain cruise missile is used for the tracking simulation and the result shows satisfactory performance, the control method is simple and suitable for engineering applications.

39

Han, Wenyao, Aijuan Li, Xin Huang, Wei Li, Jiaping Cao, and Haixiang Bu. "Trajectory tracking of in-wheel motor electric vehicles based on preview time adaptive and torque difference control." Advances in Mechanical Engineering 14, no. 4 (April 2022): 168781322210899. http://dx.doi.org/10.1177/16878132221089909.

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In order to improve the accuracy of trajectory tracking of in-wheel motor electric vehicles, a preview time adaptive trajectory tracking method based on iterative algorithm and fuzzy control is proposed. Firstly, based on the vehicle’s three-degree-of-freedom model, the vehicle is controlled to track trajectory based on model predictive control (MPC). The preview step size and sampling period of MPC are adjusted by iterative function and fuzzy controller, respectively. Then, In order to optimize MPC active steering control, a differential torque controller is established to realize the trajectory tracking control of differential torque steering. Finally, Carsim/Simulink co-simulation analysis and real vehicle verification are done. The simulation results show that the controller can complete the trajectory tracking control of the in-wheel motor intelligent vehicle, and the stability and steering performance are good. The controller has good robustness and adaptability according to road adhesion conditions and vehicle speed changes. At the same time, the trajectory tracking accuracy of the MPC controller is better than sliding mode variable structure control (SMC). The real vehicle verification results show that when the real vehicle tracking under different speeds, the adaptive preview time controller designed in this paper has good trajectory tracking performance and stability.

40

Luo, Haifeng. "Automatic Manipulator Tracking Control Based on Moving Target Trajectory Prediction." Scientific Programming 2021 (November 30, 2021): 1–10. http://dx.doi.org/10.1155/2021/7944300.

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The core issue of automatic manipulator tracking control is how to ensure the given moving target follows the expected trajectory and adapts to various uncertain factors. However, the existing moving target trajectory prediction methods rely on highly complex and accurate models, lacking the ability to generalize different automatic manipulator tracking scenarios. Therefore, this study tries to find a way to realize automatic manipulator tracking control based on moving target trajectory prediction. In particular, a moving target trajectory prediction model was established, and its parameters were optimized. Next, a tracking-training-testing algorithm was proposed for manipulator’s automatic moving target tracking, and the operating flows were detailed for training module, target detection module, and target tracking module. The proposed model and algorithm were proved effective through experiments.

41

Tan, Wei, Mengfei Wang, and Ke Ma. "Research on Intelligent Vehicle Trajectory Tracking Control Based on Improved Adaptive MPC." Sensors 24, no. 7 (April 5, 2024): 2316. http://dx.doi.org/10.3390/s24072316.

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Intelligent vehicle trajectory tracking exhibits problems such as low adaptability, low tracking accuracy, and poor robustness in complex driving environments with uncertain road conditions. Therefore, an improved method of adaptive model predictive control (AMPC) for trajectory tracking was designed in this study to increase the corresponding tracking accuracy and driving stability of intelligent vehicles under uncertain and complex working conditions. First, based on the unscented Kalman filter, longitudinal speed, yaw speed, and lateral acceleration were considered as the observed variables of the measurement equation to estimate the lateral force of the front and rear tires accurately in real time. Subsequently, an adaptive correction estimation strategy for tire cornering stiffness was designed, an AMPC method was established, and a dynamic prediction time-domain adaptive model was constructed for optimization according to vehicle speed and road adhesion conditions. The improved AMPC method for trajectory tracking was then realized. Finally, the control effectiveness and trajectory tracking accuracy of the proposed AMPC technique were verified via co-simulation using CarSim and MATLAB/Simulink. From the results, a low lateral position error and heading angle error in trajectory tracking were obtained under different vehicle driving conditions and road adhesion conditions, producing high trajectory-tracking control accuracy. Thus, this work provides an important reference for improving the adaptability, robustness, and optimization of intelligent vehicle tracking control systems.

42

Peng, Yu, and Yun Li. "Autonomous Trajectory Tracking Integrated Control of Unmanned Surface Vessel." Journal of Marine Science and Engineering 11, no. 3 (March 7, 2023): 568. http://dx.doi.org/10.3390/jmse11030568.

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Trajectory tracking control of unmanned surface vessels (USVs) has become a popular topic. Regarding the problem of ship collision avoidance encountered in trajectory tracking, more attention needs to be paid to the algorithm application, namely the characteristics of flexibility and accessibility. Thus, a fusion framework of field theoretical planning and a model predictive control (MPC) algorithm is proposed in this paper to obtain a realizable collision-free tracking trajectory, where the trajectory smoothness and collision avoidance constraints under a complex environment need to be considered. Through the designed fast matching (FM) method based on the electric field model, the algorithm gains the direction trend of collision avoidance planning and then combines it with a flexible distance to reconstruct the architecture of the MPC and constraint system, generating the optimal trajectory tracking controller. The new algorithm was tested and validated for several situations, and it can potentially be developed to advance collision-free trajectory tracking navigation in multivessel situations.

43

KumarGupta, Mukul, Arun Kumar Singh, and Kamal Bansal. "Trajectory Tracking Control of Robot Manipulators." International Journal of Computer Applications 64, no. 10 (February 15, 2013): 28–32. http://dx.doi.org/10.5120/10672-5458.

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44

Wu, Minye, Haibin Ling, Ning Bi, Shenghua Gao, Qiang Hu, Hao Sheng, and Jingyi Yu. "Visual Tracking With Multiview Trajectory Prediction." IEEE Transactions on Image Processing 29 (2020): 8355–67. http://dx.doi.org/10.1109/tip.2020.3014952.

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45

Endo, Kimitaka, Koichi Tanaka, Kenichi Arakawa, and Naoki Mukawa. "Visual Trajectory Tracking by Preview Control." Journal of the Robotics Society of Japan 15, no. 4 (1997): 565–72. http://dx.doi.org/10.7210/jrsj.15.565.

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46

ZHANG, X. Y., K. FAROOQ, and X. X. YI. "INSTANTANEOUS DARK STATE AND TRAJECTORY TRACKING." International Journal of Quantum Information 11, no. 05 (August 2013): 1350046. http://dx.doi.org/10.1142/s0219749913500469.

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State steering and tracking have attracted a lot of interests due to their wide applications. In this paper, we present two concrete examples of trajectory tracking of an open quantum system with time-dependent Lindblad operator which has an eigenstate |ψs(t)〉 with eigenvalue 0. We work out the first case analytically while numerically the second case. From both the cases, we find that, if changing the Lindblad operator is sufficiently slow, then the system can follow the trajectory of |ψs(t)〉 very well.

47

Nagy, Endre. "Adaptive control through optimized trajectory tracking." IFAC Proceedings Volumes 37, no. 12 (August 2004): 379–84. http://dx.doi.org/10.1016/s1474-6670(17)31498-2.

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48

Pomirski, Janusz, Leszek Morawski, and Andrzej Rak. "Trajectory tracking control system for ship." IFAC Proceedings Volumes 37, no. 10 (July 2004): 251–55. http://dx.doi.org/10.1016/s1474-6670(17)31740-8.

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49

Avanzini, G. "Trajectory tracking for a helicopter model." Aeronautical Journal 105, no. 1044 (February 2001): 69–76. http://dx.doi.org/10.1017/s0001924000011519.

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Abstract In this paper an algorithm for the trajectory tracking of a three-dimensional trajectory, assigned as a function of time, is presented. The proposed control system is suitable for application on unmanned aerial vehicles (UAVs) or for aircraft that require accurate path tracking, as in the case of rotorcraft in nap-of-the-earth (NOE) flight conditions. The control system logic features (i) an external loop based on a simple guidance scheme and a two-time-scale inverse simulation algorithm, and (ii) an inner loop, based on a linear-quadratic (LQ) full-state-feedback controller. In this way the control action is split into two contributions, i.e. a feedforward command, in order to follow the trajectory generated by the guidance scheme, and a feedback increment, for compensating external disturbances and model uncertainties. A rotorcraft model is used to demonstrate the algorithm capability in a NOE–like flight task. System robustness is analysed and control system performance are discussed in terms of the error between vehicle state and desired trajectory at a given time. Simulation of a representative manoeuvre shows that the feedforward estimate of the control action is accurate and only minor compensation is required from the LQ tracker. The algorithm is suitable for a number of applications, as (i) no simplifying assumptions are postulated for the model, (ii) there are no restrictions on the flight condition, and (iii) the computational time should allow for real–time implementation.

50

Nagy, P. V. "Trajectory tracking control for industrial robots." Journal of Mechanical Working Technology 20 (September 1989): 273–81. http://dx.doi.org/10.1016/0378-3804(89)90037-5.

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