• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.2711-2713
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• 2005

This paper presents the tracking control method of robotic systems with uncertainties using self recurrent wavelet neural network (SRWNN) via the backstepping design technique. The SRWNN is used as the uncertainty observer of the robotic systems. The adaptation laws for weights of the robotic systems are induced from the Lyapunov stability theorem, which are used for on-line controlling robotic systems. Computer simulations of a three-link robot manipulator with uncertainties verify the validity of the proposed SRWNN controller.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.853-858
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• 1993

The error back-propagation(BP) algorithm is widely used for finding optimum weights of multi-layer neural networks. However, the critical drawback of the BP algorithm is its slow convergence of error. The major reason for this slow convergence is the premature saturation which is a phenomenon that the error of a neural network stays almost constant for some period time during learning. An inappropriate selections of initial weights cause each neuron to be trapped in the premature saturation state, which brings in slow convergence speed of the multi-layer neural network. In this paper, to overcome the above problem, Micro-Genetic algorithms(.mu.-GAs) which can allow to find the near-optimal values, are used to select the proper weights and slopes of activation function of neurons. The effectiveness of the proposed algorithms will be demonstrated by some computer simulations of two d.o.f planar robot manipulator.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.150-155
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• 1993

The neural network MRAC system is presented. The purpose of this paper is applied to a plant that is to be controlled in a strongly nonlinear environment. The proposed system has a learning and adaptive ability in the varying environment by using the back-propagation learning algorithm based on Lyapunov stability theory. N.N. regulator is a part of overall system and is guaranteed to be stable in initial stage. Nonlinear terms of the varying mass, colilori, centifugal, and gravity are compensated for by feedforward N.N. regulator. And the feedback controller (adaptive mechanism) works to eliminate errors of position, velocity which the feedforward controller cannot compensate for. Finally, the proposed system will be demonstrated by simulation of a two d.o.f robot manipulator.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.65-68
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• 1996

Robotic manipulators have become increasingly important in the field of flexible automation. High speed and high-precision trajectory tracking are indispensable capabilities for their versatile application. The need to meet demanding control requirement in increasingly complex dynamical control systems under significant uncertainties, leads toward design of intelligent manipulation robots. This paper presents a new approach to the design of neural control system using digital signal processors in order to improve the precision and robustness. The TMS32OC31 is used in implementing real time neural control to provide an enhanced motion control for robotic manipulators. In this control scheme, the networks introduced are neural nets with dynamic neurons, whose dynamics are distributed over all the, network nodes. The nets are trained by the distributed dynamic back propagation algorithm. The proposed neural network control scheme is simple in structure, fast in computation, and suitable for implementation of real-time, control. Performance of the neural controller is illustrated by simulation and experimental results for a SCARA robot.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.539-542
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• 1996

The forces exerted on an object by the end-effectors of multi-manipulators are decomposed into the motion-inducing force and the internal force. Motion-inducing force effects the motion of an object and internal force can't effect it. The motion of an object can't track exactly the desired motion because of internal force component, therefore internal force component must be considered. In this paper using the resolved acceleration control method and the fact that internal force lies in the null space of jacobian matrix, we construct independently the position, motion-inducing force and internal force controller. Secondly we construct the robust controller to preserve the robustness with respect to the uncertainty of manipulator parameters.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1179-1182
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• 2005

To facilitate the cell based robot research, we presented a micro-mechanical force measurement system for the biological muscle actuators, which utilize glucose as a power source for potential application in a human body or blood vessels. The system is composed of a micro-manipulator, a force transducer with a glass probe, a signal processor, an inverted microscope and video recoding system. Using this measurement system, the contractile force and frequency of the cardiac myocytes were measured in real time and the magnitude of the contractile force of each cardiac myocyte on a different condition was compared. From the quantitative experimental results, we estimated that the force of cardiac myocytes is about $20{\sim}40\;{\mu}$N, and showed that there is difference between the control cells and the micro-patterned cells.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2211-2216
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• 2005

This paper describes a novel navigation algorithm using a locomotion interface with two 6-DOF parallel robotic manipulators. The suggested novel navigation system can induce user's real walking and generate realistic visual feedback during navigation, using robotic manipulators. For realistic visual feedback, the virtual environment is designed with three components; 3D object modeler for buildings and terrains, scene manager and communication manager component. The walking velocity of the user is directly translated to VR actions for navigation. Finally, the functions of the RPC interface are utilized for each interaction mode. The suggested navigation system can allow a user to explore into various virtual terrains with real walking and realistic visual feedback.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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• pp.224-229
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• 1997

This paper compares the control techniques for position control experiments of a fixible robot moving in a vertical plane. The flexible manipulator is modeled as an Euler-beroulli beam. Elastic deformantion is representedusing the assumed model method. A comparison function which satisfies all geometric and natural boundary conditions of a cantilever beam with an end mass is used as an assumed mode shape. Lagrange's equation is utilized for the development of a discretized model. Control schemes are developed using PID control,pole placement control and discrete Linear Quadratic Regulater(LQQ). The effectiveness of the developed control schems are compared using computer simulation in view of practical experiment. The simulation results show that PID control is very effective in practical implementation.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.413-416
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• 1997

Recently, dynamic recurrent neural networks(DRNN) for identification of nonlinear dynamic systems have been researched extensively. In general, dynamic backpropagation was used to adjust the weights of neural networks. But, this method requires many complex calculations and has the possibility of falling into a local minimum. So, we propose a new approach to identify nonlinear dynamic systems using DRNN. In order to adjust the weights of neurons, we use evolution strategies, which is a method used to solve an optimal problem having many local minimums. DRNN trained by evolution strategies with mutation as the main operator can act as a plant emulator. And the fitness function of evolution strategies is based on the difference of the plant's outputs and DRNN's outputs. Thus, this new approach at identifying nonlinear dynamic system, when applied to the simulation of a two-link robot manipulator, demonstrates the performance and efficiency of this proposed approach.

• The Transactions of the Korea Information Processing Society
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• v.2 no.2
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• pp.209-214
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• 1995

It is very hard to identify the fuzzy rules and tune the membership functions of the fuzzy reasoning in fuzzy systems modeling .We propose a method which canautomatically identify the fuzzy rules and tune the membership functions of fuzzy reasoning simultaneously using artifical neural network. In this model,fuzzy rules are identified by backpropagation algorithm. The feasibility of the method is simulated by a simple robot manipulator.