• The Transactions of the Korean Institute of Power Electronics
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• v.13 no.1
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• pp.1-8
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• 2008

A dynamic model for II-SEPIC(Isolated Inverse-SEPIC) is developed based on the state-space averaging method and its control characteristics are investigated in this paper. Equations for circuit design of II-SEPIC are derived through steady state analysis and the resulted circuit parameters are used in the consequent simulation and experiment works. A structure of control system is devised to obtain better control performance. In order to verify validity and effectiveness of the design equations and dynamic model derived, dynamic control responses of II-SEPIC system against line and load variation are illustrated in both simulation and experiment.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.7
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• pp.654-661
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• 2015

Stairs are the most popular obstacles in buildings and factories. To enlarge the application areas of a field robotic platform, stair-climbing is very important mission. One important reason why a stair-climbing is difficult is that stairs are various in sizes. To achieve autonomous climbing of various-sized stairs, dynamic modeling is essential. In this research, an inverse dynamic modeling is performed to enable an autonomous stair climbing. Stair-climbing robotic platform with flip locomotion, named FilpBot, is analyzed. The FlipBot platform has advantages of robust stair-climbing of various sizes with constant speed, but the autonomous operation is not yet capable. Based on external constraints and the postures of the robot, inverse dynamic models are derived. The models are switched by the constraints and postures to analyze the continuous motion during stair-climbing. The constraints are changed according to the stair size, therefore the analysis results are different each other. The results of the inverse dynamic modeling are going to be used in motor design and autonomous control of the robotic platform.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.280-285
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• 1991

Neural network control has many innovative potentials for fast, accurate and intelligent adaptive control. In this paper, a learning control architecture for the dynamic control of a robot manipulator is developed using inverse dynamic neurocontroller and linear neurocontroher. The inverse dynamic neurocontrouer consists of a MLP (multi-layer perceptron) and the linear neurocontroller consists of SLPs (single layer perceptron). Compared with the previous type of neurocontroller which is using an inverse dynamic neurocontroller and a fixed PD gain controller, proposed architecture shows the superior performance over the previous type of neurocontroller because linear neurocontroller can adapt its gain according to the applied task. This superior performance is tested and verified through the control of PUMA 560. Without any knowledge on the dynamic model, its parameters of a robot , (The robot is treated as a complete black box), the neurocontroller, through practice, gradually and implicitly learns the robot's dynamic properties which is essential for fast and accurate control.

• Proceedings of the KSME Conference
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• 2004.04a
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• pp.881-886
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• 2004

This paper presents the inverse dynamic analysis of the wheel loader manipulator based on the experimental data. A three dimensional rigid multi-body model of the wheel loader manipulator was built up. The inverse dynamic analysis for the typical operation mode was carried out by the ADAMS program. In order to verify the analysis result with the measured one, the hydraulic pressure and displacements of the cylinders were measured and the inverse dynamic analysis was carried out using experimental data. From the results of the analysis and measurement, it was concluded that the computational driving force showed good agreement with the measured one.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.1
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• pp.33-38
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• 2002

In this paper, a Two-Degree-of-Freedom-Controller(TDFC) for DC motors based on inverse dynamics and the fuzzy technique is presented. The proposed controller includes the inverse dynamic model of a DC motor system, a prefilter and a fuzzy compensator. The model of the system is characterized by a nonlinear equation with coulomb friction. The prefilter eliminates high frequency effects occurring when the inverse dynamic model is implemented. The fuzzy compensator is designed for tracking the change of the reference input and simultaneously regulating the error between the reference input and the system output which can be caused by disturbances. The optimal parameters of both the model and the compensator are identified by a real-coded genetic algorithm. An experimental work on a DC motor system is carried out to verify the performance of the proposed controller.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.33.1-33
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• 2001

We address a novel approach to identify a nonlinear dynamic system for Wiener models, which are composed of a linear dynamic system part followed by a nonlinear static part. The aim of system identification here is to provide the optimal mathematical model of both the linear dynamic and the nonlinear static parts in some appropriate sense. Assuming the nonlinear static part is invertible, we approximate the inverse function by a piecewise linear function. We estimate the piecewise linear inverse function by using the evolutionary computation approach such as genetic algorithm (GA) and evolution strategies (ES), while we estimate the linear dynamic system part by the least squares method. The results of numerical simulation studies indicate the usefulness of proposed approach to the Wiener model identification.

• 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 Machine Tool Engineers Conference
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• 1999.10a
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• pp.11-15
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• 1999

Monitoring of the cutting force signals in cutting process has been well emphasized in machine tool communities. Although the cutting force can be directly measured by a tool dynamometer, this method is not always feasible because of high cost and limitations in setup. In this paper an indirect cutting force monitoring system is developed so that the cutting force in turning process is estimated based on a AC spindle drive model. This monitoring system considers the cutting force as a disturbance input to the spindle drive and estimates the cutting force based on the inverse dynamic model. The inverse dynamic model represents the dynamic relation between the cutting force, the motor torque and the motor power. The proposed monitoring system is realized on a CNC lathe and its estimation performance is evaluated experimentally.

• Nuclear Engineering and Technology
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• v.55 no.7
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• pp.2642-2649
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• 2023

A real-time unmeasured dynamic response prediction process for the nuclear power plant pressure pipeline is proposed and its performance is tested in the test-loop system (KAERI). The aim of the process is to predict unmeasurable or unreachable dynamic responses such as acceleration, velocity, and displacement by using a limited amount of directly measured physical responses. It is achieved by combining a well-constructed finite element model and robust inverse force identification algorithm. The pressure pipeline system is described by using the displacement-pressure vibro-acoustic formulation to consider fully filled liquid effect inside the pipeline structure. A robust multiphysics modal projection technique is employed for the real-time sensor synchronized prediction. The inverse force identification method is also derived and employed by using Bathe's time integration method to identify the full-field responses of the target system from the modal domain computation. To validate the performance of the proposed process, an experimental test is extensively performed on the nuclear power plant pressure pipeline test-loop under operation conditions. The results show that the proposed identification process could well estimate the unmeasured acceleration in both frequency and time domain faster than 32,768 samples per sec.