• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.3013-3015
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• 2000

The stabilization control of Inverted Pendulum(IP) system is difficult because of its nonlinearity and structural unstability. Futhermore, a series of conventional techniques such as the pole placement and the optimal control based on the local linearizations have narrow stabilizable regions, At the same time, the fine tunings of their gain parameters are also troublesome, Thus, in this paper, an Evolving Neural Network ControlleY(ENNC) which its structure and its connection weights are optimized simultaneously by Real Variable Elitist Genetic Algorithm (RVEGA) was presented for stabilization of an IP system with nonlinearity, This proposed ENNC was described by a simple genetic chromosome. Through the simulation and experimental results, we showed that the finally acquired optimal ENNC was very useful in the stabilization control of IP system.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1474-1479
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• 2004

In this paper, the design of the network system using the CAN and the analysis of effects of time delay in the system are presented. A conventional implementation technique induces many problems because of the amount and complexity of wiring and maintenance problems. The network system reduces these problems, but it cause another problem; time delay. Time delay in a sampling time does not have much effects on the system, but time delay over the sampling time changes the control frequency and ended up makes the system unstable. It is verified that time delay between each parts has different effects on the entire system. The results from this paper will be a base for studying algorithms to reduce effects of time delay in the system using the CAN.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.21 no.8
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• pp.32-42
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• 2022

Asymptotic tracking of a non-minimum phase nonlinear system has been a popular topic in control theory and application. In this paper, we propose a new control scheme to achieve asymptotic output tracking in anon-minimum phase nonlinear system for periodic trajectories through an iterative learning control with the stable inversion. The proposed design method is robust to parameter uncertainties and periodic external disturbances since it is based on iterative learning. The performance of the proposed algorithm was demonstrated through the simulation results using a typical non-minimum nonlinear system of an inverted pendulum on a cart.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.11a
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• pp.243-247
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• 1996

A general procedure for motion capture and mimic system has been delineated. Utilizing sensors operated in the magnetic fields, complicated and optimized movements are easily digitized to analyze and reproduce. The system consists of a motion capture module, a motion visualization module, a motion plan module, a motion mimic module, and a GUI module. Design concepts of the system are modular, open, and user friendly to ensure the overall system performance. This procedure is being implemented on a virtual cyber cube. and an inverted pendulum. With modifications, this procedure can applied for complicated motion controls.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2135-2137
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• 2002

In this paper to overcome drawback of FLC an adaptive fuzzy sliding mode controller is proposed. The fuzzy basis function to describe the fuzzy system is introduced. The system parameter in sliding mode are estimated by the indirect adaptive fuzzy control. Adaptive laws for fuzzy parameters and fuzzy rule structure are established so that the whole system is suable in the sense of Lyapunov stability. The computer simulation results for inverted pendulum system show the performance of the proposed fuzzy sliding mode controller.

• Proceedings of the KIEE Conference
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• 1994.07a
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• pp.468-471
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• 1994

This paper presents the digital implementation of an optimal and robust VSS controller with sliding observer. Firstly, a discrete-time VSS control law which enables the system state to move into a sliding sector where the closed-loop system is stable is designed. Then optimal control theory is used to design an optimal sliding sector. Secondly, a sliding observer which provide robust state estimation against model-plant mismatches due to parameter uncertainties is designed for the sampled-data multivariable systems. Finally, modified sliding observer which effectively reduce chattering of state variables in state estimation was proposed. The proposed scheme was applied 10 a two degree of freedom inverted pendulum with passive joint to verify robust motion control. Computer simulation results confirm the viability of the proposed observer-based controller.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2372-2377
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• 2003

Nonlinear dynamic system exist widely in many types of systems such as chemical processes, biomedical processes, and the main steam temperature control system of the thermal power plant. Up to the present time, PID Controllers have been used to operate these systems. However, it is very difficult to achieve an optimal PID gain with no experience, because of the interaction between loops and gain of the PID controller has to be manually tuned by trial and error. This paper suggests control approaches by immune fuzzy for the nonlinear control system inverted pendulum, through computer simulation. This paper defines relationship state variables $x,{\dot{x}},{\theta},\dot{\theta}$ using immune fuzzy and applied its results to stability.

• Journal of Industrial Technology
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• v.24 no.A
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• pp.99-107
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• 2004

This paper considers reinforcement learning control with the self-organizing map. Reinforcement learning uses the observable states of objective system and signals from interaction of the system and the environments as input data. For fast learning in neural network training, it is necessary to reduce learning data. In this paper, we use the self-organizing map to parition the observable states. Partitioning states reduces the number of learning data which is used for training neural networks. And neural dynamic programming design method is used for the controller. For evaluating the designed reinforcement learning controller, an inverted pendulum of the cart system is simulated. The designed controller is composed of serial connection of self-organizing map and two Multi-layer Feed-Forward Neural Networks.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2380-2382
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• 2000

Fuzzy system which are based on membership functions and rules, can control nonlinear, uncertian, complex system well. However, Fuzzy controller has problems: It is difficult to design a stable for amateur. To update the then-part membership functions of the fuzzy controller can be designed using the error back-propagation algorithm to be minimized error. Then we could be optimized the system choosing a good performance index. The proposed fuzzy controller based on neural network is applied to control an inverted pendulum for demonstration of the robustness of proposed methodology.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.15 no.4
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• pp.315-323
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• 1990

A design technique of dynamic stabilization controller for the intrinsic unstable inverted pendulum system is introduced. Mathematical modelling with the more complex nonlinearity and the stabilized control theory presented by C.D.Johnson are adapted to this system by using the state-space approach. And the Stabilized controller with the designed optimal regulator type which can be fastly tracked and can be accurately counteracted aginst all effects of the constant distrubances and the parameteric variations is simulated and is implemeted successfully on the microcomputer.