• 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.

• Journal of the Korean Society of Industry Convergence
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• v.23 no.1
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• pp.49-55
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• 2020

This paper presents the system modeling, analysis, and controller design and implementation with a inverted pendulum system in order to test Linear Quadratic control based robust algorithm for inverted pendulum robot. The balancing of an inverted pendulum robot by moving pendulum robot like as 'segway' along a horizontal track is a classic problem in the area of control. This paper will describe two methods to swing a pendulum attached to a cart from an initial downwards position to an upright position and maintain that state. The results of real experiment show that the proposed control system has superior performance for following a reference command at certain initial conditions.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.22 no.5
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• pp.837-844
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• 2013

This study performs erection of a pendulum hanging at a free end of an arm by rotating the arm to the upright position. A mathematical model of a rotary inverted pendulum system (RIPS) is derived. A brain emotional learning based intelligent controller (BELBIC) is designed and used as a controller for swinging up and balancing the pendulum of the RIPS. In simulations performed in the study, a pendulum is initially inclined at $45^{\circ}$ with respect to the upright position. A simulation is also performed for evaluating the adaptiveness of the designed BELBIC in the case of system variation. In addition, a simulation is performed for evaluating the robustness of the designed BELBIC against a disturbance in the control input.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.3057-3059
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• 1999

The inverted pendulum is an unstable, nonlinear system exposed to disturbances and its system parameters change. This paper presents the Time Delay Control design of the inverted pendulum using robot minipulator. The results obtained from a simulations indicated a reference tracking of the system. This paper will implement, the time delay control of the inverted pendulum using a robot manipulator, It will be that the time delay can control the inverted pendulum using a manipulator.

• 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 ...

• 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.

• 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 Institute of Control, Robotics and Systems
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• v.8 no.11
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• pp.916-921
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• 2002

We presents a new method of constructing an equivalent T-S fuzzy model by using the sum of products of linearly independent scalar functions from nonlinear dynamics. This method exactly expresses nonlinear systems and automatically determines the number of rules. We design a stabilizing controller f3r ul inverted pendulum system by using the concep of parallel distributed compensation (PDC) and linear matrix inequalities (LMIs) based on the proposed T-S fuzzy modeling method. We show effectiveness of a systematically designed fuzzy controller based on the proposed T-S fuzzy modeling method through the simulation and experiment of an inverted pendulum system.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1267-1271
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• 2003

This paper proposes an indirect adaptive fuzzy controller for general SISO nonlinear systems. No a priori information on bounding constants of uncertainties including reconstruction errors and optimal fuzzy parameters is needed. The control law and the update laws for fuzzy rule structure and estimates of fuzzy parameters and bounding constants are determined so that the Lyapunov stability of the whole closed loop system is guaranteed. The computer simulation results for an inverted pendulum system show the performance of the proposed robust adaptive fuzzy controller.