• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.963-965
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• 1999

A linear chromosome combined with a grid-based representation of the network and a new crossover operator allow the evolution of the architecture and the weights simultaneously. In our approach there is no need for a separate weight optimization procedure and networks with more than one type of activation function can be evolved. In this paper one evolutionary' strategy of a given dual neural controller was introduced and the simulation results were described in detail through applications to a stabilization control of an Inverted Pendulum System.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.878-883
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• 1991

In this paper, we propose the fuzzy-neural controller which is fuzzy controller with learning ability of neural network. The neural network in this controller is same as the membership function in current fuzzy controller and a parts of inference rules. And, it can be easily extend the control algorithm to multivariable systems. We can show effectiveness of the control algorithm through experiment of the inverted pendulum system.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 1995.04a
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• pp.142-146
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• 1995

In the paper, Fuzzy Logic Controller(FLC) that determines its optimal coefficients using Genetic Algorithms is considered. It is also applied to the inverted pendulum problem known popularly as a standard plant. Flexibility of the inverted pendulum has been taken into account. In the results, Fuzzy Logic Controller under consideration successfully controls both rigid mode and flexible mode. The rule base of Fuzzy Logic Controller is automatically tuned using not only trial-error method but also Genetic Algorithms.

• Journal of the Korea Society of Computer and Information
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• v.15 no.5
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• pp.13-21
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• 2010

The Optimal results of Kalman Filtering on the Inverted Pendulum System requires an effective factor such as the noise covariance matrix Q, the measurement noise covariance matrix R and the initial error covariance matrix $P_0$. We present a special case where the optimality of the filter is not destroyed and not sensitive to scaling of these covariance matrix because these factors are unknown or are known only approximately in the practical situation. Moreover, the error covariance matrices issued by this method predict errors in the state estimate consistent with the scaled covariance matrices and not the issued state estimates. Various results using the scalar gain $\delta$ are derived to described the relations among the three covariance matrices, Kalman Gain and the error covariance matrices. This paper is described as follows: Section III a brief overview of the Inverted Pendulum system. Section IV deals with the mathematical dynamic model of the system used for the computer simulation. Section V presents a various simulation results using the scalar gain.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.5
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• pp.652-658
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• 2010

This paper presents the implementation and control of a two wheeled mobile robot(TWMR) based on a balancing mechanism. The TWMR is a mobile inverted pendulum structure that combines an inverted pendulum system and a mobile robot system with two arms instead of a rod. To improve robustness due to disturbances, the radial basis function (RBF) network is used to control an angle and a position at the same time. The reference compensation technique(RCT) is used as a neural control method. Experimental studies are conducted to demonstrate performance of neural network controllers. The robot are implemented with the remote control capability.

• Journal of Advanced Navigation Technology
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• v.16 no.4
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• pp.703-708
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• 2012

This paper proposes the improvement of control performance in the wheeled inverted mobile robot system. and describes the modeling of a wheeled inverted pendulum type mobile robot driven by two different wheels for the position and velocity control. The system is sensitive on the parameter variation, therefore control signal should change to maintain desired state of the system in every instant. we designed proportional-plus-integral controller for our system, After linearization, the system was still unstable, throughout stability analysis of the system, we designed the values of the gains of a proportional-plus-integral controller. From the experimental results, we can find that the performance of the proposed method is better than of the manual tuning method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.7A
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• pp.967-977
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• 2000

In this paper, we apply a mixed $H_2/H_{\infty}$ control to a generalized plant of inverted pendulum system represented by an LFR(Linear Fractional Representation). First, in order to obtain the generalized plant, the linear model of the inverted pendulum represented by an LFR(Linear fractional Representation) is derived. In LFR, we consider system uncertainties as three nonlinear components and a pendulum mass uncertainty. Augmenting the LFR model by adding weighting functions, we get a generalized plant. And then, we design a mixed $H_2/H_{\infty}$ controller for the generalized plant. In order to design the mixed $H_2/H_{\infty}$ controller, we use the LMI technique. To evaluate control performances and robust stability of the mixed $H_2/H_{\infty}$ controller designed, we compare it with the $H_{\infty}$ controller through the simulation and experiment. In the result, with the fewer feedback information, the mixed $H_2/H_{\infty}$ controller shows the better control performances and robust stability than the $H_{\infty}$ controller in the sense of pendulum angle.

• Journal of Electrical Engineering and Technology
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• v.8 no.6
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• pp.1579-1589
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• 2013

We provide a step-by-step, easy-to-follow procedure for the method of controlled Lagrangian systems. We apply this procedure to solve the energy shaping problem for four benchmark examples: the inertial wheel pendulum, an inverted pendulum on a cart, the system of ball and beam and the Furuta pendulum.

• Journal of Advanced Marine Engineering and Technology
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• v.34 no.2
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• pp.310-317
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• 2010

In this paper, we propose a method for swing-up and stabilization of a SESIP(Self-Erecting Single Inverted Pendulum) system which is one of the typical nonlinear systems. We use PV(Proportional velocity) controller for swinging up the pendulum and employ a PI-type state-feedback controller for stabilizing the pendulum. Control is switched to a stabilizing controller, which is designed to balance the inverted position of pendulum and the cart position to the near vertical position. Computer simulations are performed to illustrate the control performance of the proposed scheme.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1104-1106
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• 1996

In this paper, a hierarchical fuzzy controller is proposed for the stabilization control of the inverted pendulum system. The design of controller for that system is difficult because of its complicated nonlinear mathematical model with unknown parameters. Conventional fuzzy control strategy based only on dynamics of pendulum made have failed to stabilize. However, proposed control strategies are to swing pendulum from natural stable up equilibrium point to an unstable equilibrium point and are to transport a cart from an arbitrary position toward a center of rail. Thus, the proposed fuzzy stabilization controller have a hierarchical fuzzy inference structure; that is, the lower level is for inference interface for the virtual equilibrium point and the higher level one for the position control of cart according to the firstly inferred virtual equilibrium point.