• International journal of advanced smart convergence
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• v.7 no.3
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• pp.15-22
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• 2018

Nowadays multiple control methods are used in robot control systems. A model, predictor or error estimator is often used as feedback controller to control a robot. While robots have become more and more intensive with algorithms capable to acquiring independent knowledge from raw data. This paper represents experimental results of real time machine learning control that does not require explicit knowledge about the plant. The controller can be applied on a broad range of tasks with different dynamic characteristics. We tested our controller on the balancing problem of a single propeller pendulum. Experimental results show that the use of a supervised machine learning algorithm in a single propeller pendulum allows the stable swing of a given angle.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2003.03a
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• pp.465-472
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• 2003

The main concept of base isolation system is to reduce the member forces by decreasing the earthquake forces transmitted to superstructure instead of the conventional techniques of strengthening the structural members. There are two important advantages in friction pendulum systems. The functions of carrying the vertical load and of providing horizontal stiffness are effectively separated. This results in a more stable system that eliminates the need of a fail-safe mechanism. Friction pendulum systems are less sensitive to variations in the frequency content of ground excitation and tend to limit the intensity of the farce imparted to the superstructure. This study investigates the friction coefficients on the FPS test specimens according to the velocity, bearing pressure and test waveform.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.227-233
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• 1996

The Inverted Pendulum has been one of most popular nonlinear dynamic systems for the exploration of control techniques. This paper presents a new linear optimal control techniques and nonlinear neural network learning methods. The multiayered neural networks are used to add nonlinear effects on the linear optimal regulator(LQR). The new regulator can compensate nonlinear system uncertainties that are not considered in the LQR design, and can tolerated a wider range of uncertainties than the LQR alone. The new regulator has two neural networks for modeling and control. The neural network for modeling is used to obtain a more accurate model than the given mathematical equations. The neural network for control is used to overcome deficiencies by adding corrections to the linear coefficients of the LQR and by adding nonlinear effects on the LQR. Computer simulations are performed to show the applicability and a more robust regulator than the LQR alone.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2008.04a
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• pp.374-379
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• 2008

The purpose of seismic isolation system among them is to lengthen the period of structure and make its period shift from the dominant period of earthquake. In this study, the seismic behavior of arch structure with lead rubber bearing(LRB) and friction pendulum system(FPS) is analyzed. The arch structure is the simplest structure and has the basic dynamic characteristics among large spatial structures. Also, Large spatial structures have large vertical response by horizontal seismic vibration, unlike seismic behavior of normal rahmen structures. When horizontal seismic load is applied to the large spatial structure with isolation systems, the horizontal acceleration response of the large spatial structure is reduced and the vertical seismic response is remarkably reduced.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.1
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• pp.70-78
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• 2004

This paper proposes non-linear control method using immune algorithm based fuzzy logic. 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 gain of the PID controller has to be manually tuned by trial and error. An inverted pendulum control problem is selected to illustrate the efficiency of the proposed method and defines relationship state variables $\chi$, $\chi$, $\theta$, $\theta$ using immune fuzzy.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 1998.04a
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• pp.399-406
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• 1998

An investigation into the stochastic bifurcation and response statistits of an autoparameteric system under broad-band random excitation is made. The specific system examined is a spring-pendulum system with internal resonance, which is known to be a good model for a variety of engineering systems, including ship motions with nonlinear coupling between pitching and rolling motions. The Fokker-Planck equations is used to generate a general first-order differential equation in the dynamic moment of response coordinates. By means of the Gaussian and non-Gaussian closure methods the dynamic moment equations for the random responses of the system are reduced to a system of autonomous ordinary differential equations. In view of equilibrium solutions of this system and their stability we examine the stochastic bifurcation and response statistics. The analytical results are compared with results obtained by Monte Carlo simulation.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.499-502
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• 1996

In this paper, we propose a fuzzy iterative learning controller(FILC). It can control fully unknown dynamic plants through iterative learning. To design learning controllers based on the steepest descent method, it is one of the difficult problems to identify the change of plant output with respect to the change of control input(.part.e/.part.u). To solve this problem, we propose a method as follows: first, calculate .part.e/.part.u using a similarity measure and information in consecutive time steps, then adjust the fuzzy logic controller(FLC) using the sign of .part.e/.part..u. As learning process is iterated, the value of .part.e/.part.u is reinforced. Proposed FILC has the simple architecture compared with previous other controllers. Computer simulations for an inverted pendulum system were conducted to verify the performance of the proposed FILC.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.9
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• pp.929-938
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• 2009

This paper presents a novel control methodology for communication network based nonlinear systems with time delay nature. We construct a nominal nonlinear control law for representing a linear model and a reset control system which is aimed for corrective control strategy to compensate system error due to uncertain time delay through wireless communication network. Next, online neural control approach is proposed for overcoming nonstationary statistical nature in the network topology. Additionally, DBN (Dynamic Bayesian Network) technique is accomplished for modeling of its dynamics in terms of casuality, which is then utilized for estimating prediction of system output. We evaluate superiority and reliability of the proposed control approach through numerical simulation example in which a nonlinear inverted pendulum model is employed as a networked control system.

• Structural Engineering and Mechanics
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• v.57 no.1
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• pp.161-178
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• 2016

This research is aimed to design and analyze the performance of double dynamic vibration absorber (DVA) using a pendulum and a spring-mass type absorber for reducing vibration of two-DOF vibration system. The conventional fixed-points method and genetics algorithm (GA) optimization procedure are utilized in designing the optimal parameter of DVA. The frequency and damping ratio are optimized to determine the optimal absorber parameters. The simulation results show that GA optimization procedure is more effective in designing the double DVA in comparison to the fixed-points method. The experimental study is conducted to verify the numerical result.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.5
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• pp.537-542
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• 2014

This paper proposes a control system to keep the balance of a unicycle robot. The robot consists of the disk and wheel, for balancing and driving respectively, and the tile angle is measured and used for balancing by the IMU sensor. A PID controller is designed based on a non-model based algorithm to prove that it is possible to control the unicycle robot without any approximated linear system model such as the sliding mode control algorithm. The PID controller has the advantage that it is simple to design the controller and it does not require an unnecessary complex formula. In this paper, assuming that the pitch and roll axis are dynamically decoupled, each of the two controllers are designed separately. A reaction wheel pendulum method is used for the control of the roll axis, that is, for balancing and an inverted pendulum concept is used for the control of the pitch axis. To confirm the performance of the proposed controllers using MATLAB Simulink, the dynamic equations of the robot are derived.