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Artículos de revistas sobre el tema "Tracking control"

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Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 21 de febrero de 2023

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1

XING, YIFAN. "TRACKING CONTROL OF QUANTUM VON NEUMANN ENTROPY". International Journal of Psychosocial Rehabilitation 24, n.º 04 (28 de febrero de 2020): 880–87. http://dx.doi.org/10.37200/ijpr/v24i4/pr201061.

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2

Olejár, M., V. Cviklovič, D. Hrubý y O. Lukáč. "Autonomous control of biaxial tracking photovoltaic system". Research in Agricultural Engineering 61, Special Issue (2 de junio de 2016): S48—S52. http://dx.doi.org/10.17221/29/2015-rae.

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Tracking photovoltaic systems maximize solar energy on the photovoltaic cells surface in order to maximize the energy gain at a given moment. Energy gain is dependent on the accuracy of photovoltaic cells direction, control method and tracking period. The control of tracking systems is based on theoretical calculations of sun position for a specific position in specific time. Designed control algorithm of the biaxial tracking photovoltaic system is able of autonomous navigation directed to the sun without knowing the position. It is based on the sun position sensor. The designed solution increases the solar gain by 33.8% in comparison with stable photovoltaic systems. It is usable in the research focused on the control method of step-controlled biaxial tracking photovoltaic devices.

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3

Ogata, Tokoku, Naohide Sakimura, Tatsuya Nakazaki, Kiyoshi Ohishi, Toshimasa Miyazaki, Daiichi Koide, Haruki Tokumaru y Yoshimichi Takano. "Tracking Control System with Equivalent Perfect Tracking Control for Optical Disks". IEEJ Transactions on Industry Applications 132, n.º 12 (2012): 1121–30. http://dx.doi.org/10.1541/ieejias.132.1121.

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4

Youssef, Ayman, Mohamed El Telbany y Abdelhalim Zekry. "Reinforcement Learning for Online Maximum Power Point Tracking Control". Journal of Clean Energy Technologies 4, n.º 4 (2015): 245–48. http://dx.doi.org/10.7763/jocet.2016.v4.290.

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5

Gierlak, Piotr y Wiesław Żylski. "Tracking Control of Manipulator". IFAC Proceedings Volumes 42, n.º 13 (2009): 623–28. http://dx.doi.org/10.3182/20090819-3-pl-3002.00108.

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6

FUNAHASHI, YASUYUKI y HISAO KATOH. "Robust-tracking deadbeat control". International Journal of Control 56, n.º 1 (julio de 1992): 213–25. http://dx.doi.org/10.1080/00207179208934310.

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7

Stanković, S. S., D. M. Stipanović y M. S. Stanković. "Decentralized overlapping tracking control". International Journal of General Systems 43, n.º 3-4 (4 de marzo de 2014): 282–93. http://dx.doi.org/10.1080/03081079.2014.883713.

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8

Grujić, Ljubomir T. y William Pratt Mounfield. "Natural tracking PID process control for exponential tracking". AIChE Journal 38, n.º 4 (abril de 1992): 555–62. http://dx.doi.org/10.1002/aic.690380409.

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9

Moon, Jung-Hwan, Jang-Heon Kim, Il-Du Kim, Jung-Joon Kim y Bum-Man Kim. "Gate-Bias Control Technique for Envelope Tracking Doherty Power Amplifier". Journal of Korean Institute of Electromagnetic Engineering and Science 19, n.º 8 (31 de agosto de 2008): 807–13. http://dx.doi.org/10.5515/kjkiees.2008.19.8.807.

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10

Lu, Ping. "Tracking Control of Nonlinear Systems with Bounded Controls and Control Rates". IFAC Proceedings Volumes 29, n.º 1 (junio de 1996): 2307–12. http://dx.doi.org/10.1016/s1474-6670(17)58017-9.

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11

Lu, Ping. "Tracking control of nonlinear systems with bounded controls and control rates". Automatica 33, n.º 6 (junio de 1997): 1199–202. http://dx.doi.org/10.1016/s0005-1098(97)00033-2.

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12

Postoyan, Romain, Nathan van de Wouw, Dragan Nesic y W. P. Maurice H. Heemels. "Tracking Control for Nonlinear Networked Control Systems". IEEE Transactions on Automatic Control 59, n.º 6 (junio de 2014): 1539–54. http://dx.doi.org/10.1109/tac.2014.2308598.

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13

SZABOLCSI, Róbert. "MODEL PREDICTIVE CONTROL APPLIED IN UAV FLIGHT PATH TRACKING MISSIONS". Review of the Air Force Academy 17, n.º 1 (24 de mayo de 2019): 49–62. http://dx.doi.org/10.19062/1842-9238.2019.17.1.7.

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14

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 (28 de febrero de 2018): 435–38. http://dx.doi.org/10.31142/ijtsrd9419.

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15

Fang, Zaojun y De Xu. "Vision Based Modeling and Control for Fillet Weld Seam Tracking". Abstracts of the international conference on advanced mechatronics : toward evolutionary fusion of IT and mechatronics : ICAM 2010.5 (2010): 557–62. http://dx.doi.org/10.1299/jsmeicam.2010.5.557.

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16

Liu, Tingrui, Yan Ding, Pan Wang, Kang Zhao y Jiahao Jia. "Stability Control of Transport Robot Based on Iterative Learning Control". Journal of Physics: Conference Series 2173, n.º 1 (1 de enero de 2022): 012061. http://dx.doi.org/10.1088/1742-6596/2173/1/012061.

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Abstract In this study, stability control for transport process of transport robot subjected to 2R manipulator movement, is investigated based on iterative learning control (ILC). The joint positions, speeds and accelerations are used as variables to establish the expression of driving torques of manipulator joints. According to the experience, the linear interference torque in the process of motion is determined. Three ILC algorithms are applied to achieve stability control, and good trajectory tracking results are obtained. Position tracking, speed tracking, and the maximum position error in the process of tracking are illustrated, and through the absolute value of the maximum error, the optimal iterative algorithm is finally determined.

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17

MARTINEZ FLORES, MIRNA MARICELA, María Aracelia Alcorta García, SANTOS MENDEZ DIAZ, JOSE ARMANDO SAENZ ESQUEDA, GERARDO MAXIMILIANO MENDEZ y NORA ELIZONDO VILLAREAL. "TEMPERATURE CONTROL IN AN EVAPORATOR APPLYING RISK-SENSITIVE CONTROL". DYNA 97, n.º 4 (1 de julio de 2022): 345. http://dx.doi.org/10.6036/10498.

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This work designs a stochastic model of an evaporator of a refrigeration system, subject to specific conditions, applying the Risk-Sensitive (R-S) stochastic control equations with tracking, to control the evaporator temperature achieving great energy savings, where the actuator is the expansion valve (EEV).

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18

Seraji, Homayoun y Richard Colbaugh. "Force Tracking in Impedance Control". International Journal of Robotics Research 16, n.º 1 (febrero de 1997): 97–117. http://dx.doi.org/10.1177/027836499701600107.

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19

Rothman, Adam, Tak-San Ho y Herschel Rabitz. "Quantum observable homotopy tracking control". Journal of Chemical Physics 123, n.º 13 (octubre de 2005): 134104. http://dx.doi.org/10.1063/1.2042456.

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20

Pachter, M., P. R. Chandler y M. Mears. "Reconfigurable tracking control with saturation". Journal of Guidance, Control, and Dynamics 18, n.º 5 (septiembre de 1995): 1016–22. http://dx.doi.org/10.2514/3.21499.

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21

Jung, Hewon, Jong Youp Shim y DaeGab Gweon. "Tracking control of piezoelectric actuators". Nanotechnology 12, n.º 1 (8 de enero de 2001): 14–20. http://dx.doi.org/10.1088/0957-4484/12/1/304.

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22

Ilchmann, Achim y Eugene P. Ryan. "Performance funnels and tracking control". International Journal of Control 82, n.º 10 (6 de agosto de 2009): 1828–40. http://dx.doi.org/10.1080/00207170902777392.

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23

ALTINTEN, AYLA y SEBAHAT ERDOĞAN. "TRACKING PERFORMANCE OF CONTROL METHODS". Chemical Engineering Communications 181, n.º 1 (septiembre de 2000): 21–36. http://dx.doi.org/10.1080/00986440008912813.

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24

Watheq El-Kharashi, M. y M. A. Sheirah. "Tracking Fuzzy Sliding Mode Control". IFAC Proceedings Volumes 30, n.º 6 (mayo de 1997): 729–35. http://dx.doi.org/10.1016/s1474-6670(17)43452-5.

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25

Wei Qu y D. Schonfeld. "Robust Control-Based Object Tracking". IEEE Transactions on Image Processing 17, n.º 9 (septiembre de 2008): 1721–26. http://dx.doi.org/10.1109/tip.2008.2001391.

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26

Liu, Chia-Shang y Huei Peng. "Disturbance Observer Based Tracking Control". Journal of Dynamic Systems, Measurement, and Control 122, n.º 2 (15 de mayo de 1997): 332–35. http://dx.doi.org/10.1115/1.482459.

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A disturbance observer based tracking control algorithm is presented in this paper. The key idea of the proposed method is that the plant nonlinearities and parameter variations can be lumped into a disturbance term. The lumped disturbance signal is estimated based on a plant dynamic observer. A state observer then corrects the disturbance estimation in a two-step design. First, a Lyapunov-based feedback estimation law is used. The estimation is then improved by using a feedforward correction term. The control of a telescopic robot arm is used as an example system for the proposed algorithm. Simulation results comparing the proposed algorithm against a standard adaptive control scheme and a sliding mode control algorithm show that the proposed scheme achieves superior performance, especially when large external disturbances are present. [S0022-0434(00)00802-9]

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27

Christian, Peter H. "Capital tracking & project control". Computers & Industrial Engineering 13, n.º 1-4 (enero de 1987): 361–65. http://dx.doi.org/10.1016/0360-8352(87)90115-x.

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28

Christian, Peter H. "Capital tracking and project control". Computers & Industrial Engineering 20, n.º 1 (enero de 1991): 71–75. http://dx.doi.org/10.1016/0360-8352(91)90041-4.

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29

Gao, Hongliang, Xiaoling Li, Chao Gao y Jie Wu. "Neural Network Supervision Control Strategy for Inverted Pendulum Tracking Control". Discrete Dynamics in Nature and Society 2021 (23 de marzo de 2021): 1–14. http://dx.doi.org/10.1155/2021/5536573.

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This paper presents several control methods and realizes the stable tracking for the inverted pendulum system. Based on the advantages of RBF and traditional PID, a novel PID controller based on the RBF neural network supervision control method (PID-RBF) is proposed. This method realizes the adaptive adjustment of the stable tracking signal of the system. Furthermore, an improved PID controller based on RBF neural network supervision control strategy (IPID-RBF) is presented. This control strategy adopts the supervision control method of feed-forward and feedback. The response speed of the system is further improved, and the overshoot of the tracking signal is further reduced. The tracking control simulation of the inverted pendulum system under three different signals is given to illustrate the effectiveness of the proposed method.

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30

Lou, Wenjie, Ming Zhu y Xiao Guo. "Spatial trajectory tracking control for unmanned airships based on active disturbance rejection control". Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 233, n.º 6 (10 de mayo de 2018): 2231–40. http://dx.doi.org/10.1177/0954410018774124.

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In this paper, to address the spatial trajectory tracking problem of unmanned airships, a robust controller based on active disturbance rejection control is presented. By transforming the airship model to a standardized form, a straightforward design approach is adopted for the design of the controller. Active disturbance rejection control is composed of a tracking differentiator, an extended state observer, and a nonlinear state error feedback. The proposed controller replaces the conventional tracking differentiator with a third-order differentiator. The new tracking differentiator provides higher tracking precision and smoother transient process. The external disturbances and model uncertainties are observed by the extended state observer and compensated in the controller design, subsequently. Comparisons with technologies frequently used in the trajectory tracking are made through numerical simulation. The comparisons validate that the proposed controller provides satisfying performance and robustness in the presence of model uncertainty and external disturbance.

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31

Yang, Can y Jie Liu. "Trajectory Tracking Control of Intelligent Driving Vehicles Based on MPC and Fuzzy PID". Mathematical Problems in Engineering 2023 (3 de febrero de 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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32

Cong, GENG, ZHANG Jian y LIU Jinxiong. "Path Tracking Control Based on Adaptive Control Period". IFAC-PapersOnLine 53, n.º 5 (2020): 592–97. http://dx.doi.org/10.1016/j.ifacol.2021.04.148.

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33

MIKAJIRI, Satoshi y Tomiji HISAMURA. "Adaptive Tracking Control with Amplitude Suppression of Control". Transactions of the Society of Instrument and Control Engineers 22, n.º 7 (1986): 788–90. http://dx.doi.org/10.9746/sicetr1965.22.788.

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34

Shih, Ming-Chang y Jiann-Bang Lee. "Pressure Tracking Control of a Pneumatic Control System". Proceedings of the JFPS International Symposium on Fluid Power 1999, n.º 4 (1999): 697–702. http://dx.doi.org/10.5739/isfp.1999.697.

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35

Zhang, Zhihua, Thomas Leifeld y Ping Zhang. "Finite Horizon Tracking Control of Boolean Control Networks". IEEE Transactions on Automatic Control 63, n.º 6 (junio de 2018): 1798–805. http://dx.doi.org/10.1109/tac.2017.2754947.

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36

M. Rouyan, Nurhana, Renuganth Varatharajoo, Samira Eshghi, Ermira Junita Abdullah y Shinji Suzuki. "Aircraft pitch control tracking with sliding mode control". International Journal of Engineering & Technology 7, n.º 4.13 (9 de octubre de 2018): 62. http://dx.doi.org/10.14419/ijet.v7i4.13.21330.

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Sliding mode control (SMC) is one of the robust and nonlinear control methods. An aircraft flying at high angles of attack is considered nonlinear due to flow separations, which cause aerodynamic characteristics in the region to be nonlinear. This paper presents the comparative assessment for the flight control based on linear SMC and integral SMC implemented on the nonlinear longitudinal model of a fighter aircraft. The controller objective is to track the pitch angle and the pitch rate throughout the high angles of attack envelope. Numerical treatments are carried out on selected conditions and the controller performances are studied based on their transient responses. Obtained results show that both SMCs are applicable for high angles of attack.

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37

Li Keyu, 李克玉 y 田福庆 Tian Fuqing. "On-axis tracking control technology of electro-optical tracking system". High Power Laser and Particle Beams 25, n.º 8 (2013): 2106–10. http://dx.doi.org/10.3788/hplpb20132508.2106.

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38

Do, Khac Duc. "Path-tracking control of underactuated ships under tracking error constraints". Journal of Marine Science and Application 14, n.º 4 (27 de octubre de 2015): 343–54. http://dx.doi.org/10.1007/s11804-015-1329-3.

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39

Aulia, Lathifatul, Widowati Widowati, R. Heru Tjahjana y Sutrisno Sutrisno. "MODELING PREDICTIVE TRACKING CONTROL FOR MAX-PLUS LINEAR SYSTEMS IN MANUFACTURING". Journal of Fundamental Mathematics and Applications (JFMA) 3, n.º 2 (23 de noviembre de 2020): 133–47. http://dx.doi.org/10.14710/jfma.v3i2.8605.

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Discrete event systems, also known as DES, are class of system that can be applied to systems having an event that occurred instantaneously and may change the state. It can also be said that a discrete event system occurs under certain conditions for a certain period because of the network that describes the process flow or sequence of events. Discrete event systems belong to class of nonlinear systems in classical algebra. Based on this situation, it is necessary to do some treatments, one of which is linearization process. In the other hand, a Max-Plus Linear system is known as a system that produces linear models. This system is a development of a discrete event system that contains synchronization when it is modeled in Max-Plus Algebra. This paper discusses the production system model in manufacturing industries where the model pays the attention into the process flow or sequence of events at each time step. In particular, Model Predictive Control (MPC) is a popular control design method used in many fields including manufacturing systems. MPC for Max-Plus-Linear Systems is used here as the approach that can be used to model the optimal input and output sequences of discrete event systems. The main advantage of MPC is its ability to provide certain constraints on the input and output control signals. While deciding the optimal control value, a cost criterion is minimized by determining the optimal time in the production system that modeled as a Max-Plus Linear (MPL) system. A numerical experiment is performed in the end of this paper for tracking control purposes of a production system. The results were good that is the controlled system showed a good performance.

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40

Ngongi, Werneld E. y Jialu Du. "Controller Design for Tracking Control of an Under-Actuated Surface Ship". International Journal of Computer Theory and Engineering 7, n.º 6 (diciembre de 2015): 469–75. http://dx.doi.org/10.7763/ijcte.2015.v7.1004.

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41

Kalaiarassan, G. y K. Krishnamurthy. "Digital hydraulic single-link trajectory tracking control through flow-based control". Measurement and Control 52, n.º 7-8 (13 de mayo de 2019): 775–87. http://dx.doi.org/10.1177/0020294019842889.

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Recent advancement in controllability of digital hydraulic is similar to the performance of a proportional/servo hydraulic system, and several studies show that digital hydraulic will be an alternative for proportional/servo hydraulic. In this paper, tip point tracking of a single-link arm is taken as the subject of the study. Here, the single-link arm is controlled by a digital hydraulic system, which is established with parallel-connected on/off valves. In order to attain stepwise flow control, the pulse code modulation technique is used. By referring to the previous work, the control signal for the trajectory tracking is calculated by taking account of cylinder chamber pressure and velocity. But in this study, the required volume flow rate for trajectory tracing is taken into account for generating control signal. This approach improves the performance of the digital hydraulic system at lower velocity tracking and also reduces the computational complexity. The analysis is conducted with the proposed algorithm for 4-bit and 5-bit digital flow control units and tip point response of single link is presented. The results show that the 5-bit system has significantly better performance than the 4-bit system. In addition, the analysis is conducted with different stroke lengths such as 200, 100 and 50 mm for studying the behaviour of the system at lower velocity tracking. Better controllability is achieved at lower velocity tracking, and the results obtained with the proposed algorithm have nearly 2% tracking error.

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42

Martin, Philippe, Pierre Rouchon y Joachim Rudolph. "Invariant tracking". ESAIM: Control, Optimisation and Calculus of Variations 10, n.º 1 (enero de 2004): 1–13. http://dx.doi.org/10.1051/cocv:2003037.

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43

Xue, Wentao, Xunnan Zhu, Xiaofei Yang, Hui Ye y Xuan Chen. "A Moving Target Tracking Control of Quadrotor UAV Based on Passive Control and Super-Twisting Sliding Mode Control". Mathematical Problems in Engineering 2021 (11 de mayo de 2021): 1–17. http://dx.doi.org/10.1155/2021/6627495.

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A novel asymptotic tracking controller for an underactuated quadrotor unmanned aerial vehicle (UAV) is proposed to solve a moving target tracking problem. Firstly, the control system is decoupled into the position control system and the attitude control system. Secondly, a method combined artificial potential field with passivity control (APF&PC) is introduced for the positioning system to achieve high-precision tracking of moving target at a fixed distance. Thirdly, a super-twisting sliding mode (STSM) method with an improved reaching law for the attitude system is applied to ensure that the attitude converges to the desired value. Furthermore, the stabilities of two subsystems are proved, and sufficient stability conditions are derived based on the passive method and Lyapunov method, respectively. Finally, simulation results of the moving target tracking verify the superiority and robustness of the proposed control method in the presence of parameter uncertainties and external disturbances.

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44

Dong, Chaoyang, Yao Lu y Qing Wang. "Tracking Control Based on Control Allocation with an Innovative Control Effector Aircraft Application". Mathematical Problems in Engineering 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/5037678.

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This paper proposes a control allocation method for the tracking control problem of a class of morphing aircraft with special actuators which are different from the conventional actuation surfaces. This design of actuators can bring about some potential advantages to the flight vehicles; however, due to the integral constraints, the desired control cannot be performed accurately; therefore, it leads to undesirable tracking errors, so influencing the performance of the system. Because the system could be control allocated, based on the designed cost function that describes the tracking errors, the cuckoo search algorithm (CSA) is introduced to search for the optimum solution within the calculated actuator execution commands that are equivalent to the desired commands. Several improvement measures are proposed for boosting the efficiency of the CSA and ensuring reasonable solutions. Simulation results show that the proposed control allocation method is necessary and effective, and the improvement measures are helpful in obtaining the optimum solution.

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45

Cao, Jian, Yushan Sun, Guocheng Zhang, Wenlong Jiao, Xiangbin Wang y Zhaohang Liu. "Target tracking control of underactuated autonomous underwater vehicle based on adaptive nonsingular terminal sliding mode control". International Journal of Advanced Robotic Systems 17, n.º 2 (1 de marzo de 2020): 172988142091994. http://dx.doi.org/10.1177/1729881420919941.

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This article addresses the design of adaptive target tracking control for an underactuated autonomous underwater vehicle subject to uncertain dynamics and external disturbances induced by ocean current. Firstly, based on the line-of-sight method, the moving target tracking guidance strategy is designed, and the target tracking reference speed and reference angular velocity are given. According to the obtained reference speed and reference angular velocities, the reference control quantity is differentiated and filtered based on dynamic surface control. The target tracking controller is designed based on radial basis function neural network and nonsingular terminal sliding mode control and adaptive techniques. Lyapunov stability principle is utilized to ensure the asymptotic stability of the target tracking controller. Simulation of target tracking is carried out to illustrate the effectiveness of the proposed controller.

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46

Wang, Ning, Mohammed Abouheaf, Wail Gueaieb y Nabil Nahas. "Model-Free Optimized Tracking Control Heuristic". Robotics 9, n.º 3 (29 de junio de 2020): 49. http://dx.doi.org/10.3390/robotics9030049.

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Many tracking control solutions proposed in the literature rely on various forms of tracking error signals at the expense of possibly overlooking other dynamic criteria, such as optimizing the control effort, overshoot, and settling time, for example. In this article, a model-free control architectural framework is presented to track reference signals while optimizing other criteria as per the designer’s preference. The control architecture is model-free in the sense that the plant’s dynamics do not have to be known in advance. To this end, we propose and compare four tracking control algorithms which synergistically integrate a few machine learning tools to compromise between tracking a reference signal and optimizing a user-defined dynamic cost function. This is accomplished via two orchestrated control loops, one for tracking and one for optimization. Two control algorithms are designed and compared for the tracking loop. The first is based on reinforcement learning while the second is based on nonlinear threshold accepting technique. The optimization control loop is implemented using an artificial neural network. Each controller is trained offline before being integrated in the aggregate control system. Simulation results of three scenarios with various complexities demonstrated the effectiveness of the proposed control schemes in forcing the tracking error to converge while minimizing a pre-defined system-wide objective function.

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47

Zak, Andrzej. "Trajectory-Tracking Control of Underwater Vehicles". Solid State Phenomena 196 (febrero de 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.

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48

Yang, Liu, Dongjie Li y Donghao Xu. "Robust tracking control with discrete-time LQR control for micromanipulators". Modern Physics Letters B 32, n.º 18 (27 de junio de 2018): 1850201. http://dx.doi.org/10.1142/s0217984918502019.

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This paper presents a robust tracking control with discrete-time linear quadratic regulation (LQR) method for micromanipulators. The micromanipulator is composed of three piezoelectric actuators (PEAs), which results in achieving three-degree-of-freedom motion. PEAs have been widely used in micromanipulation for biomedicine because of the advantages of its infinitely small displacement resolution and precision. However, owning to the nonlinear effects of PEAs, mainly hysteresis, can drastically degrade the tracking control accuracy. Therefore, it is desirable to develop advanced controllers to compensate hysteresis effect for improving the trajectory tracking performance. Before the controller design, a compensation for motion coupling error in vertical plane is concerned. Then, a controller consisting of three parts which are a nominal feedforward control input, a LQR control input and a control input based on system uncertainties compensation is designed. At last, the robust stability of the designed controller is proved through a Lyapunov stability analysis. The simulation results demonstrate that the proposed controller is effective in tracking applications, which can provide a high resolution performance.

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Kim, Sung Hyun. "H∞Output-Feedback Tracking Control for Networked Control Systems". Mathematical Problems in Engineering 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/724389.

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This paper investigates the observer-basedH∞tracking problem of networked output-feedback control systems with consideration of data transmission delays, data-packet dropouts, and sampling effects. Different from other approaches, this paper offers a single-step procedure to handle nonconvex terms that appear in the process of designing observer-based output-feedback control, and then establishes a set of linear matrix inequality conditions for the solvability of the tracking problem. Finally, two numerical examples are given to illustrate the effectiveness of our result.

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50

Eryilmaz, Bora y Bruce H. Wilson. "Improved Tracking Control of Hydraulic Systems". Journal of Dynamic Systems, Measurement, and Control 123, n.º 3 (21 de junio de 1999): 457–62. http://dx.doi.org/10.1115/1.1386394.

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Tracking control for hydraulic systems is a key system requirement, as these devices must often follow prescribed motions. Tracking control of hydraulic systems has been approached using both linear and nonlinear control laws. The latter provides improved performance, but at the expense of additional sensors. Further, the control laws often employ hydraulic fluid bulk modulus—a difficult-to-characterize quantity—as a parameter. To overcome these difficulties, we have developed a control design procedure that requires no additional sensors and is robust to variations in the bulk modulus. A dual approach of singular perturbation theory and Lyapunov techniques form the basis for the procedure. For the cases of a small-amplitude sinusoidal input and large-amplitude polynomial input, a candidate system achieved good tracking performance and exhibited outstanding robustness. The ability to accomplish good tracking in a robust manner with no additional sensors provides advantages over other nonlinear tracking algorithms.

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