• Proceedings of the KIEE Conference
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• 1988.07a
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• pp.937-940
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• 1988

The problem of minimization of energy, consumed by the robot manipulator, is important, notably for larger manipulators, higher working speeds, and loads. Obviously, the stated problem requires the application of optimal control theory, which is being successfully applied for linear system and certain classes of nonlinear systems. However, the application of optimal control theory(in energy or time optimization) leads to substantial practical difficulties, so that significant simplifications are usually performed, either in model complexity or by neglecting the existing constraints. In this paper the problem of obtaining such an optimization method. which would take into account the complete system dynamics and all the constraints is considered. The only method found to be suitable for such a complex optimization should be based on dynamic programming.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.7
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• pp.597-604
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• 2001

Recently, the study of the moving sliding mode in the variable structure control is in progress ac-tively. The conventional time-invariant sliding model control can\`t guarantee the sliding mode in the reaching phase, which is robust against the uncertainty. But with the time-varying method, the controller makes the states track the desired trajectories and keeps the sliding mode. Nevertheless, the piecewise continuous method of the past still has the reaching mode. Thus we propose the continuously moving sliding surface by the fuzzy algorithm. The proposed algorithm is made of the fuzzy rule considering both the error and the error velocity, and may apply to the entire phase plane without sacrificing sliding mode. Especially the proposed scheme can rotate tot he slope-decreasing direction, needless to say rotating to the slope-increasing direction. For showing that the proposed controller guarantees the sliding model and ensures the robustness, we apply the proposed method to the two-link robot manipulator simulation.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.2
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• pp.168-172
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• 2001

The accuracy problem of robot manipulators has long been one of the principal concerns in robot design and control. A practical and economical way of enhancing the manipulator accuracy, without affecting its hardware, is kinematic calibration. In this paper an effective and practical method is presented for kinematic calibration of Stewart platforms. In our method differential errors in kinematical parameters are linearly related to differential errors in the platform pose, expressed through the forward kinematics. The algorithm is tested using simulated measurement in which measurement noise is included.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.651-653
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• 1998

This thesis propose the prefilter type control strategies using modified chaotic neural networks #or the trajectory control of robotic manipulator. Since the structure of chaotic neural networks and neurons, chaotic neural networks can show the robust characteristics for controlling highly nonlinear dynamics like robotic manipulators. For its application, the trajectory controller of the three-axis PUMA robot is designed by CNN. The CNN controller acts as the compensator of the PD controller. Simulation results show that learning error decrease drastically via on- line learning and the performance is excellent. The CNN controller have much better controllability and shorter calculation time compared to the RNN controller. Another advantage of the proposed controller could be attached to conventional robot controller without hardware changes.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1996.03a
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• pp.114-118
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• 1996

This paper proposes a new technique to the design of adaptive control system using DSPs(TMS320C50) for Digital signal processors are used in implementing real time adaptive control algorithms to provide motion for robotic manipulators. In the proposed scheme, adaptation laws are derived from the improved second stability analysis based on the indirect adaptive control theory. The proposed control scheme is simple in structure, fast in computation. an suitable for implementation of real-time control. Moreover, this scheme does not requre an accurate dynamic modeling, nor values of manipulator parameters and payload. Performance of the adaptive controller is illustrated by exeperimental reults for a SCARA robot.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.9
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• pp.1137-1153
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• 1995

It is not yet possible to solve the optimal number of neurons in hidden layer at neural networks. However, it has been proposed and proved by experiments that there is a limit in increasing the number of neuron in hidden layer, because too much incrememt will cause instability,local minima and large error. This paper proposes a module neural controller with pattern recognition ability to solve the above trade-off problems and to obtain fast learning convergence speed. The proposed neural controller is composed of several module having Multi-layer Perrceptron(MLP). Each module have the less neurons in hidden layer, because it learns only input patterns having a similar learning directions. Experiments with six joint robot manipulator have shown the effectiveness and the feasibility of the proposed the parallel module neural controller with pattern recognition perceptron.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2341-2344
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• 2001

In recent years the neural network known as a sort of the intelligent control strategy is used as a powerful tool for designing control system since it has learning ability. But it is difficult for neural network controllers to guarantee the stability of control systems. In this paper we try connecting a radial basis function network to an adaptive control strategy. Radial basis function networks are simpler and easier to handle than multilayer perceptrons. We use the radial basis function network to generate control input signals that are similar to the control inputs of adaptive control using linear reparameterization of the robot manipulator. We adopt the saturation function as an auxiliary controller. This paper also proves mathematically the stability of the control system under the existence of disturbances and modeling errors.

• Journal of Drive and Control
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• v.14 no.4
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• pp.23-28
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• 2017

Time-delay control with internal model (TDCIM) is the controller for robot manipulators that applies the time-delay estimation and the concept of internal model control (IMC). TDCIM is robust against unknown dynamics and non-linear friction like coulomb friction and static friction. It is simple and computationally efficient. This study presents the relationship between the discrete TDCIM and the discrete PID controller. The PID controller is the most popular control law in the real application. But often the PID controller can be difficult to achieve the desired level of control performance. The result in this study provides a good candidate solution to these situations.

• The Journal of Korea Robotics Society
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• v.16 no.3
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• pp.207-215
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• 2021

There is a risk of serious injury to workers who work at height in pier construction process. By using auto climbing formwork system that does not need to dismantle and reinstall formworks, it is possible to improve work efficiency and safety of workers. However, auto climbing formwork system still requires workers to work on a pier for rebar connection works and so on. In order to eliminate works by workers on the pier, robot manipulators with special end effectors are proposed. Through analysis of works on the pier, three specialized end effectors which are a gripper, a rebar coupler press, and a concrete vibrator, are suggested. Also, new pier construction scenario by the suggested system is confirmed using 3d modeling. It is expected that the proposed system and method enables pier construction without workers on piers. It will increase safety and efficiency of pier construction.

• The Journal of Korea Robotics Society
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• v.16 no.4
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• pp.313-318
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• 2021

This paper proposes a new method to generate inverse reachability maps that are more efficient for mobile manipulators than the previous algorithms. The base positioning is important to perform the given tasks. Using the inverse reachability method, we can know where to place the robot's base for given tasks. For example, the robot successfully performed the task with relocation, even when the target is initially in a low manipulability area or outside the workspace. However, there are some inefficiencies in the online process of the classical inverse reachability method. We describe what inefficiencies appear in the online phase and how to change the offline process to make the online efficient. Moreover, we demonstrate that the proposed approach achieves better performance than usual inverse reachability approaches for mobile manipulation. Finally, we discuss the limitations and advantages of the proposed method.