• Journal of the Korean Society for Precision Engineering
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• v.22 no.8 s.173
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• pp.34-41
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• 2005

Generally, it is not easy to get a suitable controller for multi variable systems. If the modeling equation of the system can be found, it is possible to get LQR control as an optimal solution. This paper suggests an LQR learning method to design LQR controller without the modeling equation. The proposed algorithm uses the same cost function with error and input energy as LQR is used, and the LQR controller is trained to reduce the function. In this training process, the Jacobian matrix that informs the converging direction of the controller Is used. Jacobian means the relationship of output variations for input variations and can be approximately found by the simple experiments. In the simulations of a hydrofoil catamaran with multi variables, it can be confirmed that the training of LQR controller is possible by using the approximate Jacobian matrix instead of the modeling equation and this controller is not worse than the traditional LQR controller.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.7
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• pp.808-813
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• 1999

A fuzzy learning method to control an unstable and multivariable system is presented in this paper, Because the multivariable system has generally a coupling effect between the inputs and outputs, it is difficult to find its modeling equation or parameters. If the system is unstable, initial condition rules are needed to make it stable because learning is nearly impossible. Therefore, this learning method uses the initial rules and introduces a cost function composed of the actual error and error-rate of each output without the modeling equation. To minimize the cost function, we experimentally got the Jacobian matrix in the operating point of the system. From the Jacobian matrix, we can find the direction of the convergence in the learning, and the optimal control rules are finally acquired when the fuzzy rules are updated by changing the portion of the errors and error rates.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1816-1819
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• 1997

Using the vision system, robotic tasks in unstructured environments can be accompished, which reduces greatly the cost and steup time for the robotic system to fit to he well-defined and structured working environments. This paper proposes a dynamic control scheme for robot manipulator with eye-in-hand camera configuration. To perfom the tasks defined in the image plane, the camera motion Jacobian (image Jacobian) matrix is used to transform the camera motion to the objection position change. In addition, the dynamic learning controller is designed to improve the tracking performance of robotic system. the proposed control scheme is implemented for tasks of tracking moving objects and shown to outperform the conventional visual servo system in convergence and robustness to parameter uncertainty, disturbances, low sampling rate, etc.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.49 no.7
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• pp.365-368
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• 2000

Traditionally, load flows have been calculated using the Gauss-Seidel and Newton-Raphson Method. DistFlow Method which is proposed by Wu and Baran is superior to the other two methods because it does not require the admittance matrix calculation to optimize the distribution system. This paper introduces a new alternative algorithm to the DistFlow Method which is slow and complex to find solutions as the number of lateral and sublateral increases. The proposed load flow method can construct System Jacobian easily. We can minimize the off-diagonal elements of the branch Jacobian and submatrices in the System Jacobian. Simulation results show that progressive performances of the proposed algorithm have a better convergence time.

• Journal of Mechanical Science and Technology
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• v.16 no.6
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• pp.762-769
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• 2002

This paper presents how effective it is to use many features for improving the speed and accuracy of visual servo systems. Some rank conditions which relate the image Jacobian to the control performance are derived. The focus is to describe that the accuracy of the camera position control in the world coordinate system is increased by utilizing redundant features in this paper. It is also proven that the accuracy is improved by increasing the number of features involved. Effectiveness of the redundant features is evaluated by the smallest singular value of the image Jacobian which is closely related to the accuracy with respect to the world coordinate system. Usefulness of the redundant features is verified by the real time experiments on a Dual-Arm robot manipulator made by Samsung Electronic Co. Ltd..

• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.43-45
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• 2000

Traditionally, load flows have been calculated using the Gauss-Seidel and Newton-Raphson Method. DistFlow Method which is proposed by Wu and Baran is superior to the other two methods because it does not require the admittance matrix calculation to optimize the distribution system. This paper introduces a new alternative algorithm to the DistFlow Method which is slow and complex to find solutions as the number of lateral and sublateral increases. The proposed load flow method can construct System Jacobian easily. We can minimize the off-diagonal elements of the branch Jacobian and submatrices in the System Jacobian. Simulation results show that progressive performances of the proposed algorithm.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.2
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• pp.85-93
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• 2003

Generally, it is very difficult to get a modeling equation because multi-variable system has coupling relations between its inputs and outputs. To design an optimal controller without the modeling equation, this paper proposes a neural-network (NN) controller being learned by Jacobian matrix. Another major characteristic is that the controller consists of two separated NN controllers, namely, proportional control part and derivative control part. Simulation results for a catamaran system show that the proposed NN controller is superior to LQR in the regulation and tracking problems.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.43 no.2
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• pp.189-196
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• 1994

This paper describes a parallel computing algorithm in Gauss elimination of Jacobian matrix to large-scale power system. The structure of Jacobian matrix becomes different according to ordering method of buses. In sequential computation buses are ordered to minimize the number of fill-in in the triangulation of the Jacobian matrix. The proposed method develops the parallelism in the Gauss elimination by using ND(nested dissection) ordering. In this procedure the level structure of the power system network is transformed to be long and narrow by using end buses which results in balance of computing load among processes and maximization of parallel computation. Each processor uses the sequential computation method to preserve the sqarsity of matrix.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.4
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• pp.578-585
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• 2015

Electromagnetic levitation system(EMLS) is one of the well known nonlinear systems. Often, it is not easy to control an EMLS due to its high nonlinearity. In this paper, we first apply two linearization method(jacobian and input-output feedback linearization) to design two feedback controllers for an EMLS. Then, by observing the advantages of each controller, we design a switching control algorithm which engage two controllers depending on the position of the steel ball in order to achieve the improved performance over each controller. The validity of our switching control approach is verified via both simulation and actual experimental results.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.6
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• pp.554-560
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• 2006

In this paper, we propose the topological map building method about unknown environment using the ultrasonic sensors. An ultrasonic sensor inherently has the range error due to the specular reflection. To decrease this error, we estimate the obstacle states(position and velocity) using the local minimum sensor values and Jacobian. Estimated states are used to avoid the obstacles and build the topological map similar to the type that human being memorizes an environment. When a mobile robot is faced with three problems(comer way, cross way and dead end), it senses the movable directions using FIS(Fuzzy Inference System). Among these directions, it can select the target direction using binary decision tree(Turn Side Selector). Proposed algorithm has been verified with three simulations and three implementations.