• Journal of Institute of Control, Robotics and Systems
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• v.7 no.12
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• pp.993-1000
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• 2001

Configuration-dependent nonlinear coefficient matrices in the dynamic equation of robot manipulator impose computa- tional burden in real-time implementation of tracking control based on the inverse dynamics controller. However, parallel manipulators such as Stewart platform have relatively small workspace compared to serial manipulators. Based on the characteristics of small motion range. nonlinear coefficient matrices can be approxiamted to constant ones. The modeling errors caused by such approximation are compensated for by H-infinity controller that treats the modeling errors disturbance. The proposed inverse dynamics controller with approximate dynamics combined with H-infinity control shows good tracking performance even for fast tracking control in which computation of full inverse dynamics is not easy to implement.

• Korean Journal of Applied Biomechanics
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• v.15 no.2
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• pp.57-67
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• 2005

The purpose of this study was to analyze the kinetic factors of the golf driver swing using the Inverse Dynamics function. For this purpose, joint force were calculated. In order to test the possibility of Inverse Dynamics function(motion-dependent interaction), a triple segmental system was set for wrist, left shoulder and lumbar and joint force working on the anatomical joint region was estimated. For this study, 7 professional golfers were sampled, and then, their driver swings were recorded with two high-speed digital video cameras (180 frames/sec.) to be synthesized into 3-dimensional images and coordinated. Then, Eular's equation was used to produce some kinematic data, which were used to calculate joint force and torque with Newton's function. All data were calculated using LabVIEW 6.1 graphic program. The results of this study can be summarized as follows; It was found that the joint force was generated on wrist, shoulder and lumbar joints in the direction of the target, and that the joint force was stronger in the direction of target immediately before impact. The joint force was generated towards the target to activate the nodes, and then, it was generated in the reverse direction to increase the speed during impact.

• Journal of Advanced Marine Engineering and Technology
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• v.21 no.4
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• pp.414-420
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• 1997

An intelligent speed control system for marine diesel engines is presented. The approach adopt¬ed is to use a conventional PID controller for normal operation and a feedforward controller for adaptive control. The feedforward controller is a neural network. The neural network is the inverse dynamics model of the plant, which is being trained on line. The parametric model of the diesel engine is represented in a linear second-order system, with a first-order combustion part and a revolution part each at a normal operating point. The time delay in the control of the com¬bustion part is approximated to the first-order system. The tuned PID parameters are set based on the model for normal operating point. To obtain the inverse dynamics of the diesel engine system, two neural networks are used, one for inverse, the other for forward dynamics. The former is posi¬tioned across the plant to learn its inverse dynamics during operation, and the latter is placed in series with the controlled plant. Simulation results are presented to illustrate the applicability of the proposed scheme to intelligent adaptive control of diesel engines.

• Journal of Advanced Marine Engineering and Technology
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• v.20 no.3
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• pp.286-286
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• 1996

A neural predictive tracking system for the control of structure-unknown dynamic system is presented. The control system comprises a neural network modelling mechanism for the the forward and inverse dynamics of a plant to be controlled, a feedforward controller, feedback controller, and an error prediction mechanism. The feedforward controller, a neural network model of the inverse dynamics, generates feedforward control signal to the plant. The feedback control signal is produced by the error prediction mechanism. The error predictor adopts the neural network models of the forward and inverse dynamics. Simulation results are presented to demonstrate the applicability of the proposed scheme to predictive tracking control problems.

• Journal of Advanced Marine Engineering and Technology
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• v.20 no.3
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• pp.154-161
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• 1996

A neural predictive tracking system for the control of structure-unknown dynamic system is presented. The control system comprises a neural network modelling mechanism for the the forward and inverse dynamics of a plant to be controlled, a feedforward controller, feedback controller, and an error prediction mechanism. The feedforward controller, a neural network model of the inverse dynamics, generates feedforward control signal to the plant. The feedback control signal is produced by the error prediction mechanism. The error predictor adopts the neural network models of the forward and inverse dynamics. Simulation results are presented to demonstrate the applicability of the proposed scheme to predictive tracking control problems.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2000.05a
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• pp.6-10
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• 2000

In this paper a methodology for designing a controller based on inverse dynamics for speed control of DC motors is presented. The proposed controller consists of a low-pass prefilter the inverse dynamic model of a system and the PI controller. The low-pass prefilter prevents high frequency effects from the inverse dynamic model. The model is characterized by a nonlinear friction model. The PI controller regulates the error between the set-point and the system output which is caused by modeling error disturbances and variations f parameters. The parameters of the model and the PI controller are optimized offlinely by genetic algorithm. The experimental results on a DC motor system illustrate the performance of the proposed controller.

• Journal of Advanced Marine Engineering and Technology
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• v.24 no.5
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• pp.97-102
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• 2000

In this paper, a methodology for designing a controller based on inverse dynamics for speed control of DC motors is presented. The proposed controller consists of a prefilter, the inverse dynamic model of a system and the PI controller. The prefilter prevents high frequency effects from the inverse dynamic model. The model of the system in characterized by a nonlinear equation with coulomb friction. The PI controller regulates the error between the set-point and the system output which may be caused by modeling error, variations of parameters and disturbances. The output which may be caused by modeling error, variations of parameters and disturbances. The parameters of the model and the PI controller are adjusted offlinely by a genetic algorithm. An experimental work on a DC motor system is carried out to illustrate the performance of the proposed controller.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.138-138
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• 2000

In this paper, a methodology for designing a controller based on inverse dynamics for speed control of DC motors is presented. The proposed controller consists of a prefilter, the inverse dynamic model of a system and a fuzzy logic controller. The prefilter prevents high frequency effects from the inverse dynamic model. The model of the system is characterized by a nonlinear equation with coulomb friction. The fuzzy logic controller regulates the error between the set-point and the system output which may be caused by disturbances and it simultaneously traces the change o( the reference input. The parameters of the model are estimated by a genetic a]gorithm. An experimental work on a DC motor system is carried out to illustrate the performance of the proposed controller

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.13 no.5
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• pp.90-97
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• 2014

This paper presents a simplified dynamics model, dynamics simulations, and computed torque control experiments of the Delta high-speed parallel robot. Using the typical Newton-Euler method, a simplified but accurate dynamics model with practical assumptions is derived. Accuracy and fast calculations of the dynamics are essential in the computed torque control for high-speed applications. It was found that the simplified dynamics equation is in very god agreement with the ADAMS model, and the calculation time of the inverse kinematics and inverse dynamics is about 0.04 msec. From the dynamics simulations, the cycle trajectory along the y-axis requires less peak motor torque and a lower angular velocity and less power than that along the x-axis. The computed torque control scheme can reduce the position error by half as compared to a PD control scheme. Finally, the developed Delta parallel robot prototype, half the size of the ABB Flexpicker robot, can achieve a cycle time of 0.43 sec with a 1.0kg payload.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.22 no.2
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• pp.185-191
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• 2013

In this paper, the dynamics of a novel 2-DOF planar Translational Parallel Manipulator (TPM) is analyzed. The suggested TPM is made up of two PPa (Prismatic-planar Parallelogram) legs. Since all the linear actuators are mounted on the base, the proposed TPM can be applied for high speed positioning applications. The Lagrangian equations of the first type is employed to derive the inverse dynamic equations. It is shown that the analytical inverse dynamics equations match very well with ADAMS simulations. These analytical inverse dynamics equations will be used for the real-time computed torque control in the further work.