Journal articles: 'Computed Torque Control'

1

YOSHIKAWA, Tsuneo. "Digital control by computed torque method." Journal of the Robotics Society of Japan 7, no. 3 (1989): 237–42. http://dx.doi.org/10.7210/jrsj.7.3_237.

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YOSHIDA, Koichi, Takayuki YAMADA, Takeshi TSUJIMURA, and Tetsuro YABUTA. "Computed Torque Method of Manipulator Using Disturbance Compensation Control." Transactions of the Society of Instrument and Control Engineers 27, no. 3 (1991): 341–48. http://dx.doi.org/10.9746/sicetr1965.27.341.

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Lammerts, I. M. M., F. E. Veldpaus, M. J. G. Van de Molengraft, and J. J. Kok. "Adaptive Computed Reference Computed Torque Control of Flexible Robots." Journal of Dynamic Systems, Measurement, and Control 117, no. 1 (March 1, 1995): 31–36. http://dx.doi.org/10.1115/1.2798520.

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This paper presents a motion control technique for flexible robots and manipulators. It takes into account both joint and link flexibility and can be applied in adaptive form if robot parameters are unknown. It solves the main problems that are related to the fact that the number of degrees of freedom exceeds both the number of actuators and the number of output variables. The proposed method results in trajectory tracking while all state variables remain bounded. Global, asymptotic stability is ensured for all values of the stiffnesses of joints and links. To show the characteristics of the proposed control law, some simulation results are presented.

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Chung, Wen-Yeuan, and Kenneth J. Waldron. "An Integrated Control Strategy for Multifingered Systems." Journal of Dynamic Systems, Measurement, and Control 117, no. 1 (March 1, 1995): 37–42. http://dx.doi.org/10.1115/1.2798521.

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A method of force allocation by optimizing the friction angles at finger contacts was combined with the computed torque method to find the torques to be commanded at finger joints for multifingered systems. In this way, slip can be avoided when the object is grasped or manipulated. The proposed method can be used to efficiently find the input torques, and is applicable for real-time computation. A history-based method is also proposed to improve the smoothness of the input torque commands. Three-dimensional simulation results are given.

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Lee, Jyh-Jone, and Lung-Wen Tsai. "Torque Resolver Design for Tendon-Driven Manipulators." Journal of Mechanical Design 115, no. 4 (December 1, 1993): 877–83. http://dx.doi.org/10.1115/1.2919282.

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Given a set of desired joint torques in an n-DOF tendon-driven manipulator with n + 1 control tendons, the determination of tendon forces is an indeterminate problem. Usually, the pseudo-inverse technique is used to solve for such a problem. In this paper, rather than using the pseudo-inverse technique, an efficient methodology for transforming joint torques (n elements) to motor torques (n + 1 elements) has been developed. This technique, called “torque resolver,” utilizes two circuit-like operators to transform torques between the two different vector spaces. It can be easily programmed on a digital computer or implemented into an analog-circuit system. It is hoped that this technique will make real-time computed-torque control feasible. The technique has been demonstrated through the dynamic simulation of a three-DOF manipulator.

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Rahmani, Mehran, and Ahmad Ghanbari. "Computed Torque Control of a Caterpillar Robot Manipulator Using Neural Network." Advanced Engineering Forum 15 (February 2016): 106–18. http://dx.doi.org/10.4028/www.scientific.net/aef.15.106.

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This paper presents a neural computed torque controller, which employs to a Caterpillar robot manipulator. A description to exert a control method application neural network for nonlinear PD computed torque controller to a two sub-mechanisms Caterpillar robot manipulator. A nonlinear PD computed torque controller is obtained via utilizing a popular computed torque controller and using neural networks. The proposed controller has some advantages such as low control effort, high trajectory tracking and learning ability. The joint angles of two sub-mechanisms have been obtained by using the numerical simulations. The discovered figures show that the performance of the neural computed torque controller is better than a conventional computed torque controller in trajectory tracking and reduction of setting time. Finally, snapshots of gain sequences are demonstrated.

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Lima Costa, Thamiris, Fabian Andres Lara-Molina, Aldemir Aparecido Cavalini Junior, and Erik Taketa. "Robust H∞ Computed torque Control for Manipulators." IEEE Latin America Transactions 16, no. 2 (February 2018): 398–407. http://dx.doi.org/10.1109/tla.2018.8327392.

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8

Shao, Bing, En Tao Yuan, and Zhong Hai Yu. "The Real-Time Control of Space Robot by Computed Torque Control Law." Advanced Materials Research 225-226 (April 2011): 978–81. http://dx.doi.org/10.4028/www.scientific.net/amr.225-226.978.

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Lie groups and Lie algebras are used to research the dynamics and computed torque law control of free flying space robot systems. First the adjoint transformations and adjoint operators of Lie groups and Lie algebras are discussed. Then the free flying base systems are transformed to fixed base systems. The inverse dynamics and forward dynamics are described with Lie groups and Lie algebras. The computed torque control law is used to simulate with the results of dynamics. The simulation results show that with the method the dynamical simulation problems of space robot can be solved quickly and efficiently. This built the foundation of real-time control based on dynamics. The computed torque control law has good performance.

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9

Goldenberg, A. A., J. A. Apkarian, and H. W. Smith. "An Approach to Adaptive Control of Robot Manipulators Using the Computed Torque Technique." Journal of Dynamic Systems, Measurement, and Control 111, no. 1 (March 1, 1989): 1–8. http://dx.doi.org/10.1115/1.3153014.

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Manipulator’s control system based on computed torque techniques incorporates a model of the manipulator dynamics. The nominal torque, computed using this mathematical model, does not reflect the effects of unknown loadings and uncertainty in modelling the parameters. An approach is presented which compensates for unknown loading and parameter uncertainty. This compensation is based on the “recursive” identification of a new dynamics operator which maps a vector of generalized coordinates into the vector of generalized forces (joint torques). The identification is based on a least-square approximation. Using the identified operator, which provides the compensated nominal torque, the system is controlled in closed-loop to generate regulation of the error in joint coordinates. The regulation is obtained using a common discrete optimization feedback law which is based on a recursive identification of the first order approximation of the dynamics model. The approach is illustrated with simulation results.

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10

Kang, Bong Soo, Soo Hyun Kim, Yoon Keun Kwak, and Craig C. Smith. "Robust Tracking Control of a Direct Drive Robot." Journal of Dynamic Systems, Measurement, and Control 121, no. 2 (June 1, 1999): 261–69. http://dx.doi.org/10.1115/1.2802464.

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This paper presents a robust controller for tracking control of a direct-drive robot. The proposed controller consists of two portions: a computed torque method which precompensates for dynamics of the modeled plant and an H∞ controller which postcompensates for residual errors which are not completely removed by the computed torque method. Experimental methods for identifying appropriate model structure and parameters are presented, and three specific controller types are compared. Using the robot designed in our laboratory, the combined controller reduced tracking errors by one half compared to computed torque control alone, and by one sixth compared to conventional independent joint control.

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11

Meddahi, Y., and K. Zemalache Meguenni. "PD-computed torque control for an autonomous airship." IAES International Journal of Robotics and Automation (IJRA) 8, no. 1 (March 1, 2019): 44. http://dx.doi.org/10.11591/ijra.v8i1.pp44-51.

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For the trajectory following problem of an airship, the standard computed torque control law is shown to be robust with respect to unknown dynamics by judiciously choosing the feedback gains and the estimates of the nonlinear dynamics. In the first part of this paper, kinematics and dynamics modeling of the airships is presented. Euler angles and parameters are used in the formulation of this model and the technique of Computed Torque control is introduced. In the second part of the paper, we develop a methodology of control that allows the airship to accomplish a prospecting mission of an environment, as the follow-up of a trajectory by the simulation who results show that Computed Torque control method is suitable for airships.

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12

Meddahi, Y., K. Zemalache Meguenni, and H. Aoued. "The nonlinear computed torque control of a quadrotor." Indonesian Journal of Electrical Engineering and Computer Science 20, no. 3 (December 1, 2020): 1221. http://dx.doi.org/10.11591/ijeecs.v20.i3.pp1221-1229.

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This paper work focused on the study of the nonlinear computed torque control of a quadrotor helicopter. In order to model the dynamic of the vehicles, kinematics and dynamics modeling of the X4 is presented. Euler angles and parameters are used in the formulation of this model and the technique of Computed Torque control is introduced. In the second part of the paper, we develop a methodology of control that allows the quadrotor to accomplish a prospecting mission of an environment, as the follow-up of a trajectory by the simulation. Results show that Computed Torque control method is suitable for X4.

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13

Abdessemed, Foudil, and Yakoub Bazi. "SVM Based Computed Torque for Robot Manipulator Control." IFAC Proceedings Volumes 42, no. 13 (2009): 593–98. http://dx.doi.org/10.3182/20090819-3-pl-3002.00103.

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Magyar, Bálint, and Gábor Stépán. "Time-optimal computed-torque control in contact transitions." Periodica Polytechnica Mechanical Engineering 56, no. 1 (2012): 43. http://dx.doi.org/10.3311/pp.me.2012-1.07.

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15

Fescioğlu Ünver, Nilgün, S. Turgut Tümer, and M. Kemal Özgören. "Simulation of human gait using computed torque control." Technology and Health Care 8, no. 1 (February 1, 2000): 53–66. http://dx.doi.org/10.3233/thc-2000-8105.

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16

Vidaković, Jelena, Vladimir Kvrgić, Mihailo Lazarević, and Pavle Stepanić. "COMPUTED TORQUE CONTROL FOR A SPATIAL DISORIENTATION TRAINER." Facta Universitatis, Series: Mechanical Engineering 18, no. 2 (July 29, 2020): 269. http://dx.doi.org/10.22190/fume190919003v.

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A development of a robot control system is a highly complex task due to nonlinear dynamic coupling between the robot links. Advanced robot control strategies often entail difficulties in implementation, and prospective benefits of their application need to be analyzed using simulation techniques. Computed torque control (CTC) is a feedforward control method used for tracking of robot’s time-varying trajectories in the presence of varying loads. For the implementation of CTC, the inverse dynamics model of the robot manipulator has to be developed. In this paper, the addition of CTC compensator to the feedback controller is considered for a Spatial disorientation trainer (SDT). This pilot training system is modeled as a 4DoF robot manipulator with revolute joints. For the designed mechanical structure, chosen actuators and considered motion of the SDT, CTC-based control system performance is compared with the traditional speed PI controller using the realistic simulation model. The simulation results, which showed significant improvement in the trajectory tracking for the designed SDT, can be used for the control system design purpose as well as within mechanical design verification.

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17

An, C. H., C. G. Atkeson, J. D. Griffiths, and J. M. Hollerbach. "Experimental evaluation of feedforward and computed torque control." IEEE Transactions on Robotics and Automation 5, no. 3 (June 1989): 368–73. http://dx.doi.org/10.1109/70.34773.

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18

Middleton, R. H., and G. C. Goodwin. "Adaptive computed torque control for rigid link manipulations." Systems & Control Letters 10, no. 1 (January 1988): 9–16. http://dx.doi.org/10.1016/0167-6911(88)90033-3.

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19

Canudas Wit, C. De, K. J. Åström, and N. Fixot. "Computed torque control via a non-linear observer." International Journal of Adaptive Control and Signal Processing 4, no. 6 (November 1990): 443–52. http://dx.doi.org/10.1002/acs.4480040603.

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20

Hasan, SK, and Anoop K. Dhingra. "Development of a model reference computed torque controller for a human lower extremity exoskeleton robot." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 235, no. 9 (April 14, 2021): 1615–37. http://dx.doi.org/10.1177/09596518211009032.

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Exoskeleton robot–based neurorehabilitation has received a lot of attention recently due to positive evidence supporting its ability to provide different forms of physical therapy and in helping evaluate the patient recovery rate accurately. The performance of exoskeleton robot–based physical therapy depends on the accuracy of the motion control system. While the computed torque control scheme based on inverse dynamics is ideal from a theoretical perspective, the stability and tracking performance strongly depends on the model accuracy. Expecting a deterministic payload for a rehabilitation robot is impractical, which makes the computed torque controller unrealistic for such an application. In this article, a 7-degree-of-freedom human lower extremity dynamic model is developed using the Lagrange method and a novel Model Reference Computed Torque Controller is utilized for control. The computed torque controller is used to estimate the joint torque requirements for tracking a trajectory. Calculated joint torques are applied to a similarly structured plant with different parameters. The deviation of the plant from the model is calculated. A proportional–integral–derivative controller is employed to force the plant to behave like the robot model. A realistic friction model is incorporated to simulate joint friction in the plant. The stability and tracking performance of the control system is presented for sequential as well as simultaneous joint movements. To verify the robustness of the developed controller, analysis of variance statistical technique is used.

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21

Li, Q., S. K. Tso, and A. N. Poo. "An enhanced computed-torque control algorithm for robot manipulators." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 212, no. 1 (February 1, 1998): 11–15. http://dx.doi.org/10.1243/0959651981539244.

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An enhanced computed-torque control approach, which is developed based on the intuitive design concept of the internal model control structure, is proposed in this paper. Both theoretical analyses and simulation studies on a two-link robot prove that the robustness of this enhanced algorithm can surpass that of the conventional computed-torque control scheme by a large extent.

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22

Ishihara, Tadashi. "Direct digital design of computed torque controllers." Journal of Robotic Systems 11, no. 3 (1994): 197–209. http://dx.doi.org/10.1002/rob.4620110306.

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23

Chen, Y. H. "Robust Computed Torque Schemes for Mechanical Manipulators: Nonadaptive Versus Adaptive." Journal of Dynamic Systems, Measurement, and Control 113, no. 2 (June 1, 1991): 324–27. http://dx.doi.org/10.1115/1.2896385.

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We consider the tracking control problem of mechanical manipulators in the presence of uncertainty. Two classes of control algorithms are proposed. If the possible bound of the uncertainty is known, a class of nonadaptive robust computed torque control schemes is used. The control guarantees the tracking error to be confined within a specified region after a finite time. If the bound of uncertainty is unknown, a class of adaptive robust computed torque control schemes is used. The control guarantees the tracking error to converge to zero. Both classes of controls are continuous. No statistical information on the uncertainty is ever assumed.

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24

Chen, Chao, Chengrui Zhang, Tianliang Hu, Hepeng Ni, and WeiChao Luo. "Model-assisted extended state observer-based computed torque control for trajectory tracking of uncertain robotic manipulator systems." International Journal of Advanced Robotic Systems 15, no. 5 (September 1, 2018): 172988141880173. http://dx.doi.org/10.1177/1729881418801738.

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Computed torque control is an effective control scheme for trajectory tracking of robotic manipulators. However, computed torque control requires precise dynamic models of robotic manipulators and is severely affected by uncertain dynamics. Thus, a new scheme that combines a computed torque control and a novel model-assisted extended state observer is developed for the robust tracking control of robotic manipulators subject to structured and unstructured uncertainties to overcome the disadvantages of computed torque control and exploit its merits. The model-assisted extended state observer is designed to estimate and compensate these uncertain dynamics as a lumped disturbance online, which further improves the disturbance rejection property of a robotic system. Global uniform ultimate boundedness stability with an exponential convergence of a closed-loop system is verified through Lyapunov method. Simulations are performed on a two degree-of-freedom manipulator to verify the effectiveness and superiority of the proposed controller.

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Lin, F. J., S. L. Chiu, and Y. S. Lin. "Slider-crank mechanism control using adaptive computed torque technique." IEE Proceedings - Control Theory and Applications 145, no. 3 (May 1, 1998): 364–76. http://dx.doi.org/10.1049/ip-cta:19982051.

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26

Kelly, Rafael. "Adaptive computed torque plus compensation control for robot manipulators." Mechanism and Machine Theory 25, no. 2 (January 1990): 161–65. http://dx.doi.org/10.1016/0094-114x(90)90117-3.

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27

KATOH, Takuya, Satoshi TAKEZAWA, and Takeshi TURUGA. "416 Robot Manipulator for Computed Torque Control with Bilateral." Proceedings of Conference of Hokkaido Branch 2005.44 (2005): 148–49. http://dx.doi.org/10.1299/jsmehokkaido.2005.44.148.

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28

Sabet, Sahand, and Mohammad Poursina. "Computed torque control of fully-actuated nondeterministic multibody systems." Multibody System Dynamics 41, no. 4 (May 29, 2017): 347–65. http://dx.doi.org/10.1007/s11044-017-9577-4.

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29

Qu, Zhihua, John F. Dorsey, Xinfan Zhang, and Darren M. Dawson. "Robust control of robots by the computed torque law." Systems & Control Letters 16, no. 1 (January 1991): 25–32. http://dx.doi.org/10.1016/0167-6911(91)90025-a.

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30

Peng, W., J. Su, and Z. Lin. "Computed torque control-based composite nonlinear feedback controller for robot manipulators with bounded torques." IET Control Theory & Applications 3, no. 6 (June 1, 2009): 701–11. http://dx.doi.org/10.1049/iet-cta.2008.0259.

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31

Siami, Mohsen, S. Asghar Gholamian, and Mosayeb Yousefi. "A Comparative Study Between Direct Torque Control and Predictive Torque Control for Axial Flux Permanent Magnet Synchronous Machines." Journal of Electrical Engineering 64, no. 6 (November 1, 2013): 346–53. http://dx.doi.org/10.2478/jee-2013-0052.

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Abstract This paper presents a comparative study between direct torque control (DTC) and predictive torque control (PTC) of Axial Flux Permanent magnet Machines (AFPM). In conventional DTC method for permanent magnet machines, only six actives voltage vectors of inverter are used to control torque and flux of machine. But in predictive torque control, in addition to six active voltage vectors, zero voltage vectors are used to control machine. So number of voltage vectors to control AFPM increases that leads to lower ripple of torque and flux. In predictive torque control, the response of torque and flux are computed for all possible switching states of inverter at every sample time according to discrete time model of machine, and then the switching state that optimizes ripple of torque and flux, will be applied in next discrete-time interval. Simulation results which compare the results of implementation of both methods and confirm the good performance of the proposed predictive torque control are presented.

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32

Guo, Tong Ying, Jie Jia Li, and Hai Chen Wang. "Research on Grinding Robot for Trajectory Control Based on Computed Torque." Applied Mechanics and Materials 58-60 (June 2011): 2392–95. http://dx.doi.org/10.4028/www.scientific.net/amm.58-60.2392.

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In this paper, in order to achieve high-precision trajectory control of grinding robot, the method of computed torque control is proposed based on PD feedback, a single-joint robot experimental platform was built, position and velocity tracking experiment is carried out with empty Load and load. Experimental results show that the method of computed torque based on PD feedback control has the characteristic of quick response speed and small position tracking error.

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33

TESHIMA, Hirofumi, and Yasuo YOSHIDA. "Hyblid Position / Force Control Using Computed Torque Method and Velocity Control Method." Proceedings of Conference of Tokai Branch 2004.53 (2004): 215–16. http://dx.doi.org/10.1299/jsmetokai.2004.53.215.

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34

Cooper, Scott E., John H. Martin, and Claude Ghez. "Effects of Inactivation of the Anterior Interpositus Nucleus on the Kinematic and Dynamic Control of Multijoint Movement." Journal of Neurophysiology 84, no. 4 (October 1, 2000): 1988–2000. http://dx.doi.org/10.1152/jn.2000.84.4.1988.

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We previously showed that inactivating the anterior interpositus nucleus in cats disrupts prehension; paw paths, normally straight and accurate, become curved, hypometric, and more variable. In the present study, we determined the joint kinematic and dynamic origins of this impairment. Animals were restrained in a hammock and trained to reach and grasp a cube of meat from a narrow food well at varied heights; movements were monitored using the MacReflex analysis system. The anterior interpositus nucleus was inactivated by microinjection of the GABA agonist muscimol (0.25–0.5 μg in 0.5 μL saline). For each joint, we computed the torque due to gravity, inertial resistance (termed self torque), interjoint interactions (termed interaction torque), and the combined effects of active muscle contraction and passive soft tissue stretch (termed generalized muscle torque). Inactivation produced significant reductions in the amplitude, velocity, and acceleration of elbow flexion. However, these movements continued to scale normally with target height. Shoulder extension was reduced by inactivation but wrist angular displacement and velocity were not. Inactivation also produced changes in the temporal coordination between elbow, shoulder, and wrist kinematics. Dynamic analysis showed that elbow flexion both before and during inactivation was produced by the combined action of muscle and interaction torque, but that the timing depended on muscle torque. Elbow interaction and muscle torques were scaled to target height both before and during inactivation. Inactivation produced significant reductions in elbow flexor interaction and muscle torques. The duration of elbow flexor muscle torque was prolonged to compensate for the reduction in flexor interaction torque. Shoulder extension was produced by extensor interaction and muscle torques both before and during inactivation. Inactivation produced a reduction in shoulder extension, primarily by reduced interaction torque, but without compensation. Wrist plantarflexion, which occurred during elbow flexion, was driven by plantarflexor interaction and gravitational torques both before and during inactivation. Muscle torque acted in the opposite direction with a phase lead to restrain the plantarflexor interaction torque. During inactivation, there was a reduction in plantarflexor interaction torque and a loss of the phase lead of the muscle torque. Our findings implicate the C1/C3 anterior interpositus zone of the cerebellum in the anticipatory control of intersegmental dynamics during reaching, which zone is required for coordinating the motions of the shoulder and wrist with those of the elbow. In contrast, this cerebellar zone does not play a role in scaling the movement to match a target.

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Yang, Zhiyong, Jiang Wu, Jiangping Mei, Jian Gao, and Tian Huang. "Mechatronic Model Based Computed Torque Control of a Parallel Manipulator." International Journal of Advanced Robotic Systems 5, no. 1 (January 2008): 14. http://dx.doi.org/10.5772/5650.

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Verma, Varnita, A. Gupta, M. K. Gupta, and P. Chauhan. "Performance estimation of computed torque control for surgical robot application." Journal of Mechanical Engineering and Sciences 14, no. 3 (September 30, 2020): 7017–28. http://dx.doi.org/10.15282/jmes.14.3.2020.04.0549.

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In the current paradigm, the development in robotic technology has a huge impact to revolutionize the medical domain. Surgical robots have greater advantages over surgeon such as reduced operating time, reduced tremor, less blood loss, and high dexterity. To perform different operations during surgery a base robot is required with the task-specific end effector. In this paper, the selective compliant assembly robot arm (SCARA) has been considered as the base robot and the complete mathematical modeling of the robot is illustrated. The equation of Kinematics is derived from the D-H notation. SCARA dynamic model is derived from Euler Lagrange. In order to achieve trajectory tracking the Computed Toque Control technique (CTC) applied to the SCARA manipulator. The performance of the CTC technique for trajectory tracking of each joint of the SCARA robot has evaluated in contrast with tuned PD and PID controller. The simulation results were discussed and verified using MATLAB simulation software.

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Zelei, Ambrus, and Gábor Stépán. "Case studies for computed torque control of constrained underactuated systems." Periodica Polytechnica Mechanical Engineering 56, no. 1 (2012): 73. http://dx.doi.org/10.3311/pp.me.2012-1.11.

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38

Lammerts, I. M. M., F. E. Veldpaus, and J. J. Kok. "Composite Computed Torque Control of Robots with Elastic Motor Transmissions." IFAC Proceedings Volumes 24, no. 9 (September 1991): 351–55. http://dx.doi.org/10.1016/s1474-6670(17)51081-2.

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39

Sobhani, Nasim, Farzin Piltan, Maryam Rahmani, Farzin Matin, Hamid Cheraghi, and Nasri Sulaiman. "Precision Improvement Based on Intelligent Hype-Plane Computed Torque Control." International Journal of Artificial Intelligence and Applications for Smart Devices 2, no. 2 (November 30, 2014): 9–22. http://dx.doi.org/10.14257/ijaiasd.2014.2.2.02.

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40

Becerra, Victor M., Steven Cook, and Jiamei Deng. "PREDICTIVE COMPUTED-TORQUE CONTROL OF A PUMA 560 MANIPULATOR ROBOT." IFAC Proceedings Volumes 38, no. 1 (2005): 229–34. http://dx.doi.org/10.3182/20050703-6-cz-1902.01308.

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41

Codourey, Alain. "Dynamic Modeling of Parallel Robots for Computed-Torque Control Implementation." International Journal of Robotics Research 17, no. 12 (December 1998): 1325–36. http://dx.doi.org/10.1177/027836499801701205.

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42

Aydogan, A., O. Hasturk, and E. Rogers. "Robust H∞ Computed Torque Control of Flexible Joint TVC Systems." IFAC-PapersOnLine 52, no. 12 (2019): 454–59. http://dx.doi.org/10.1016/j.ifacol.2019.11.285.

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43

Jung, Seul, and T. C. Hsia. "A study of neural network control of robot manipulators." Robotica 14, no. 1 (January 1996): 7–15. http://dx.doi.org/10.1017/s0263574700018890.

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SummaryThe basic robot control technique is the model based computer-torque control which is known to suffer performance degradation due to model uncertainties. Adding a neural network (NN) controller in the control system is one effective way to compensate for the ill effects of these uncertainties. In this paper a systematic study of NN controller for a robot manipulator under a unified computed-torque control framework is presented. Both feedforward and feedback NN control schemes are studied and compared using a common back-propagation training algorithm. Effects on system performance for different choices of NN input types, hidden neurons, weight update rates, and initial weight values are also investigated. Extensive simulation studies for trajectory tracking are carried out and compared with other established robot control schemes.

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44

Gourdeau, R., and H. M. Schwartz. "Adaptive Control of Robotic Manipulators Using an Extended Kalman Filter." Journal of Dynamic Systems, Measurement, and Control 115, no. 1 (March 1, 1993): 203–8. http://dx.doi.org/10.1115/1.2897401.

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This paper presents a new adaptive motion control scheme for robotic manipulators. This is an adaptive computed torque method (CTM) that requires only position measurements. These measurements and the input torques are used in an extended Kalman filter (EKF) to estimate the inertial parameters of the full non-linear robot model as well as the joint positions and velocities. These estimates are used by the CTM to generate the input torques. The theory behind Kalman filtering provides clear guide-lines on the selection of the design parameters for the controller when noise is present. Simulation results illustrate the performance of this scheme and demonstrate its noise rejection properties.

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45

Maliotis, G. "A Hybrid Model Reference Adaptive Control/Computed Torque Control Scheme for Robotic Manipulators." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 205, no. 3 (August 1991): 215–21. http://dx.doi.org/10.1243/pime_proc_1991_205_334_02.

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46

Viola, Jairo, and Luis Angel. "Tracking Control for Robotic Manipulators using Fractional Order Controllers with Computed Torque Control." IEEE Latin America Transactions 16, no. 7 (July 2018): 1884–91. http://dx.doi.org/10.1109/tla.2018.8447353.

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SZMIDT, Piotr. "Artillery-Missile System Control Under Disturbances Conditions Using a Modified Computed Torque Control Method." Problems of Mechatronics Armament Aviation Safety Engineering 10, no. 1 (March 31, 2019): 75–90. http://dx.doi.org/10.5604/01.3001.0013.0798.

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The paper addresses the issue of remote control of a artillery-missile system when the system is affected by dynamic and kinematic disturbances. The dynamic disturbances analysed in the paper includes disturbances from shots fired while kinematic disturbances are excitation related to the motion of the base on which the system is installed. The object of the study is a system model based on the ZU-23-2MR artillery-missile system produced and operated in Poland, designed to combat lightly armoured air, naval and ground targets. Once the system model and the assumed disturbance types are discussed, further in the paper the system control in azimuth and elevation angular position is analysed. Computed torque control with additional corrective components is presented. A certain inertia in system drive models is also adopted. Additionally, uncertainty of model identification is assumed, i.e. object control parameters are different from the parameters of the model which serves as basis for calculating the control parameters. Differences in weights, mass moments of inertia and friction torques arising in the system's drive elements are taken into account. The last part of the paper includes an analysis of the speed of target interception and precision of tracking a manoeuvring aerial target with the interference affecting the system. It was assumed that the system is located on a ship, therefore kinematic disturbances are related to the ship's movement on the sea waves, as well as dynamic disturbances are related to firing the weapon. All simulations were performed in the Scilab environment for a non-linear model of the system. Essential results are shown in a graphical form.

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Jung, Seul, and T. C. Hsia. "A new neural network control technique for robot manipulators." Robotica 13, no. 5 (September 1995): 477–84. http://dx.doi.org/10.1017/s0263574700018312.

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SummaryA new neural network (NN) control technique for robot manipulators is introduced in this paper. The fundamental robot control technique is the model-based computed-torque control which is subjected to performance degradation due. to model uncertainty. NN controllers have been traditionally used to generate a compensating joint torque to account for the effects of the uncertainties. The proposed NN control approach is conceptually different in that it is aimed at prefiltering the desired joint trajectories before they are used to command the computed-torque-controlled robot system (the plant) to counteract performance degradation due to plant uncertainties. In this framework, the NN controller serves as the inverse model of the plant, which can be trained on-line using joint tracking error. Several variations of this basic technique is introduced; Back-propagation training algorithms for the NN controller have been developed. Simulation results have demonstrated the excellent tracking performance of the proposed control technique.

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Shah, Jolly Atit, and S. S. Rattan. "Dynamic Analysis Of Two Link Robot Manipulator For Control Design Using PID Computed Torque Control." IAES International Journal of Robotics and Automation (IJRA) 5, no. 4 (December 1, 2016): 277. http://dx.doi.org/10.11591/ijra.v5i4.pp277-283.

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Due to their advantage of high speed, accuracy and repeatability, robot manipulators have become major component of manufacturing industries and even now a days they become part of routine life. Two link robot manipulator is a very basic classical and simple example of robot followed in understanding of basic fundamentals of robotic manipulator. The equation of motion for two link robot is a nonlinear differential equation. For higher degrees of freedom, as the closed form solutions are very difficult we have to use numerical solution. Here we focused mainly on control of robot manipulator to get the desired position using combination of two classical methods PID and computed torque control method after deriving the equation of motion. For the same simulation is represented using MATLAB and compared with computed torque control method.

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Wu, Han, Lin Lang, Honglei An, Qing Wei, and Hongxu Ma. "Fuzzy cerebellar model articulation controller-based adaptive tracking control for load-carrying exoskeleton." Measurement and Control 53, no. 7-8 (August 2020): 1472–81. http://dx.doi.org/10.1177/0020294020944962.

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Load-carrying exoskeletons need to cope with load variations, outside disturbances, and other uncertainties. This paper proposes an adaptive trajectory tracking control scheme for the load-carrying exoskeleton. The method is mainly composed of a computed torque controller and a fuzzy cerebellar model articulation controller. The fuzzy cerebellar model articulation controller is used to approximate model inaccuracies and load variations, and the computed torque controller deals with tracking errors. Simulations of an exoskeleton in squatting movements with model parameter changes and load variations are carried out, respectively. The results show a precise tracking response and high uncertainties toleration of the proposed method.

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Journal articles on the topic 'Computed Torque Control'

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