• The Journal of Korea Robotics Society
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• v.5 no.2
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• pp.110-119
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• 2010

This paper aims to add the autonomous driving capability to the inverted pendulum system which maintains the inverted pendulum upright stably. For the autonomous driving from the starting position to the goal position, the motion control algorithm is proposed based on the dynamics of the inverted pendulum robot. To derive the dynamic model of the inverted pendulum robot, a three dimensional robot coordinate is defined and the velocity jacobian is newly derived. With the analysis of the wheel rolling motion, the dynamics of inverted pendulum robot are derived and used for the motion control algorithm. To maintain the balance of the inverted pendulum, the autonomous driving strategy is derived step by step considering the acceleration, constant velocity and deceleration states simultaneously. The driving experiments of inverted pendulum robot are performed while maintaining the balance of the inverted pendulum. For reading the positions of the inverted pendulum and wheels, only the encoders are utilized to make the system cheap and reliable. Even though the derived dynamics works for the slanted surface, the experiments are carried out in the standardized flat ground using the inverted pendulum robot in this paper. The experimental data for the wheel rolling and inverted pendulum motions are demonstrated for the straight line motion from a start position to the goal position.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.4
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• pp.341-349
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• 2004

In this paper, the decentralized neural network control of the reference compensation technique is proposed to control a 2-DOF inverted pendulum on an x-y plane. The cart with the 2-DOF inverted pendulum moves on the x-y plane and the 2-DOF inverted pendulum rotates freely on the x-y axis. Since the 2-DOF inverted pendulum is divided into two 1-DOF inverted pendulums, the decentralized neural network control is applied not only to balance the angle of pendulum, but also to control the position tracking of the cart. Especially, a circular trajectory tracking is tested for position tracking control of the cart while maintaining the angle of the pendulum. Experimental results show that position control of the inverted pendulum system is successful.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.328-328
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• 2000

In this paper, An inverted pendulum system is typical of a nonlinear model. We propose a stable the inverted pendulum with fuzzy controller and state observer of nonlinear system. we represent the fuzzy system as a Takagj-Sugeno fuzzy model in addition, full-order state observer of inverted pendulum. As the result show fuzzy controller of inverted pendulum with nonlinear model of full-order state observer.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.3
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• pp.200-208
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• 2014

This paper presents the control of an inverted pendulum system using intelligent algorithms, such as fuzzy logic and neural networks, for advanced control education. The swing up balancing control of the inverted pendulum system was performed using fuzzy logic. Because the switching time from swing to standing motion is important for successful balancing, the fuzzy control method was employed to regulate the energy associated with the angular velocity required for the pendulum to be in an upright position. When the inverted pendulum arrived within a range of angles found experimentally, the control was switched from fuzzy to proportional-integral-derivative control to balance the inverted pendulum. When the pendulum was balancing, a joystick was used to command the desired position for the pendulum to follow. Experimental results demonstrated the performance of the two intelligent control methods.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.176.1-176
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• 2001

Conventional researches almost have been focused on the one dimensional inverted pendulum. Recently, Sprenger et al[2] have researched a two dimensional inverted pendulum Observing human's action to control an inverted pendulum, one can recognize that human uses a three dimensional metier including the up and down motion. In this paper, we propose a fuzzy logic controller(FLC) of a new three dimensional inverted pendulum system. We derive a dynamic equation of the mechanism including a 3-axis cartesian robot and a inverted pendulum. We propose a design method of a fuzzy controller of the yaw and pitch angles of a inverted pendulum. In the design, the redundant degree-of-freedom(DOF) of the robot ...

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.124-124
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• 2000

In this paper, The PID controller for stabilization of an inverted pendulum system is proposed. The PR control rule is very common in control systems. It is the basic tool for solving most process control problem. We consider the inverted pendulum system containing two PID controllers. The first controls the angle of the pendulum. The second is used to control the position of the cart. We can show stabilization of the PID controller through simulation of the inverted pendulum system.

• Journal of Ocean Engineering and Technology
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• v.23 no.2
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• pp.1-7
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• 2009

This paper presents a sliding mode controller based on Ackermann's formula and applies it to stabilizing a two-wheeled mobile inverted pendulum in equilibrium. The mobile inverted pendulum is a system with an inverted pendulum on a mobile cart. The dynamic modeling of the mobile inverted pendulum was established under the assumptions of a cart with no slip and a pendulum with only planar motion. The proposed sliding mode controller was based upon a class of nonlinear systems whose nonlinear part of the modeling can be linearly parameterized. The sliding surface was obtained in an explicit form using Ackermann's formula, and then a control law was designed from reachability conditions and made the sliding surface attractive to the equilibrium state of the mobile inverted pendulum. The proposed controller was implemented in a Microchip PIC16F877 micro-controller. The developed overall control system is described. The simulation and experimental results are presented to show the effectiveness of the modeling and controller.

• Proceedings of the IEEK Conference
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• 2003.07c
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• pp.2569-2572
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• 2003

In this paper, we Proposed the inverted pendulum control method using single neuron neural network that have weights as PID parameters. The proposed method has three inputs(proportion, integration, differentiation term of the error), and uses weights as P, I, D parameters. In order to verify the effectiveness of the proposed method, we experimented on the rotary inverted pendulum with load effect disturbance. The results showed the effectiveness and robustness of the proposed pendulum controller.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.9 no.4
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• pp.309-314
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• 2009

This paper presents implementation of the adaptive neuro-fuzzy control method. Control performance of the adaptive neuro-fuzzy control method for a popular inverted pendulum system is evaluated. The inverted pendulum system is designed and built as an education kit for educational purpose for engineering students. The educational kit is specially used for intelligent control education. Control purpose is to satisfy balancing angle and desired trajectory tracking performance. The adaptive neuro-fuzzy controller has the Takagi-Sugeno(T-S) fuzzy structure. Back-propagation algorithm is used for updating weights in the fuzzy control. Control performances of the inverted pendulum system by PID control method and the adaptive neuro-fuzzy control method are compared. Control hardware of a DSP 2812 board is used to achieve the real-time control performance. Experimental studies are conducted to show successful control performances of the inverted pendulum system by the adaptive neuro-fuzzy control method.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.690-693
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• 2005

In this paper, a hybrid PD-servo state feedback control algorithm for swing up inverted pendulum system is proposed. It consists of two parts. The first part is the PD position control for swinging up the pendulum from the natural pendent position to around the upright position and the second part is the servo state feedback control for stabilizing the inverted pendulum in upright position. The first controller is PD controller and it is tuned to control the position of the pendulum by moving the cart back and forth until the pendulum swings up around the upright position. Then the second controller will be switched to stabilize the inverted pendulum in its upright position. The controller in this stage is the servo state feedback controller designed by pole placement. Experimental results of PD type swinging up control system, of stabilizing servo state feedback control system and of the proposed hybrid PD-servo state feedback control system to swing up and stabilize inverted pendulum show that the proposed method is effective and reliable for actual implementation while it is simple.