• Journal of Advanced Marine Engineering and Technology
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• v.36 no.8
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• pp.1104-1110
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• 2012

The inverted pendulum system is a common, interesting control problem that involves many basic elements of control theory. In the early, controls of stabilization for the inverted pendulum system were used classical methods like PD, PID controller. In recently, however, control methods based on modern and intelligent control theory are widely applied. The fuzzy logic controller which is often used in nonlinear control is a little too hard to design due to increasing fuzzy rules rapidly if the given system like inverted pendulum has many state variables. Also, in case the state variables are divided into two parts, two fuzzy controllers are needed in the control system. In this paper, the authors propose FCSC(Fuzzy Controller based on State variables Combination) that reorganized into two new signals depending on the physical meaning of the four state variables of the inverted pendulum system. The proposed method is applied to the inverted pendulum system and simulations are accomplished to illustrate the control performance.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.5
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• pp.577-584
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• 1999

In this paper, the double inverted pendulum having a single actuator is built and the controller for the system is proposed. The lower link of the target pendulum system is hinged on the plate to free for rotation in the specified range($10^{\cire}$) on the x-z plane. The upper link is connected to the lower link through a DC motor. The double inverted pendulum built can be kept upright posture by controlling the position of the upper link even though it has no actuator in lower hinge. The algorithm to control the inverted pendulum consists of a state feedback controller within a linearizable range and a fuzzy logic controller coupled with a nonlinear feedback compensator for the rest of the range. Conventional state feedback control is employed, and the fuzzy controller is responsible for generating the reference joint angle of the upper link for the nonlinear feedback compensator which drives a DC motor to generate an indirect torque to the lower joint. As a result, we can get the upright posture of the proposed pendulum system. Simulations and experiments are conducted to show the validity of the proposed controller.

• Proceedings of the IEEK Conference
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• 2002.06e
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• pp.5-8
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• 2002

In this paper, we applied a PC interface and an embedded system in order to design a non-linear system and implement the PID algorithm as our control one. We used the inverted pendulum, one of the most generally used non-linear system models, to control uncertain factors in the environment. This paper showed how to use this non-linear system model to control the factors completely as well as to understand the PID algorithm. Furthermore, this paper applied and understood the embedded system.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.172-175
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• 1996

In the paper is proposed a hierarchical self-learning fuzzy controller for balancing and position control of an circular inverted pendulum system. To stabilize the pendulum at a specified position, the hierarchical fuzzy controller consists of a supervisory controller, a self-learning fuzzy controller, and a forced disturbance generator. Simulation example shows the effectiveness of the proposed method.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.207-210
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• 1997

In this paper, a hierarchical fuzzy controller for stabilization of the inverted pendulum system is proposed. The facility of this hierarchical fuzzy controller which has a swing-up control mode and a stabilization one, moves a pendulum in an initial natural stable equilibrium point and a cart in arbitrary position to an unstable equilibrium point and a center of rail. Specially, the virtual equilibrium point (.PHI.$_{VEq}$ ) which describes functionally considers the interactive dynamics between a position of cart and a angle of inverted pendulum is introduced. And comparing with the convention optimal controller, the proposed hierarchical fuzzy inference made substantially the inverted pendulum system robust and stable.e.

• International Journal of Control, Automation, and Systems
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• v.2 no.1
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• pp.92-99
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• 2004

In this paper, the decoupled neural network reference compensation technique (DRCT) is applied to the control of a two degrees-of-freedom inverted pendulum mounted on an x-y table. Neural networks are used as auxiliary controllers for both the x axis and y axis of the PD controlled inverted pendulum. The DRCT method known to compensate for uncertainties at the trajectory level is used to control both the angle of a pendulum and the position of a cart simultaneously. Implementation of an on-line neural network learning algorithm has been implemented on the DSP board of the dSpace DSP system. Experimental studies have shown successful balancing of a pendulum on an x-y plane and good position control under external disturbances as well.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.7
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• pp.162-169
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• 2000

In this paper, we develop a parallel inverted pendulum system that has the characteristics of the strongly coupled dynamics of motion by an elastic spring, the time-variant system parameters, and inherent instability, and so on. Hence, it is possible to approximate some kinds of a physical system into this representative system and to apply the various control theories to this system in order to verie their fidelity and efficiency. For this purpose, an experimental system of the parallel inverted pendulum has been implemented, and a control scheme using the eigenstructure assignment for decoupling control is presented in comparison with the conventional LQR optimal control method. Furthermore, this system can be utilized as a testbed to develop and evaluate new control algorithms through various setups. Finally, in this paper, the results of the experiment are compared with those of numerical simulations for validation.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2008.11a
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• pp.395-396
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• 2008

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.2
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• pp.109-114
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• 2002

Various inverted pendulum systems have been frequently used as a model for the performance test of the proposed control system. We first identify a rotary-type inverted pendulum system by the Euler-Lagrange method and then design a FLC (Fuzzy Logic Controller) fur the plant. FLC`s are one of useful control schemes fur plants having difficulties in deriving mathematical models or having performance limitations with conventional linear control schemes. Many FLC`s imitate the concept of conventional PD (Proportional-Derivative) or PI (Proportional-Integral) controller. That is, the error e and the change-of-error are used as antecedent variables and the control input u the change of control input Au is used as its consequent variable for FLC`s. In this paper we design a simple-structured FLC for the rotary inverted pendulum system. We also perform some computer simulations to examine the tracking performance of the closed-loop system.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.45-49
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• 1997

In this paper, we controlled a Rotary Inverted Pendulum System using Neuro-Fuzzy Controller(NFC). The inverted pendulum system is widely used as a typical example of an unstable nonlinear control system which is difficult to control. Fuzzy theory have been because membership functions and rules of a fuzzy controller are often given by experts or a fuzzy logic control system. This controller is a feedforward multilayered network which integrates the basic elements and functions of a tradtional fuzzy logic controller into a connectionist structure which has distributed learning abilities. Such NFC can be constructed from training examples by learning rule, and the structure can be trained to develop fuzzy logic rules and find optimal input/output membership functions. Using this controller, we presented the results that controlled a Rotary Inverted Pendulum System and the associated algorithms.