• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2170-2172
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• 2003

This paper presents the position tracking control of an inverted pendulum on an inclined railway. In general, inclining the railway leads to errors in the pendulum angle even though the pendulum is stabilized, which results in errors in the cart position. To solve this problem, a linear quadratic regulation (LQR) controller with an integrator is used for compensating the resulting error in the cart position. The proposed method is evaluated by comparing LQR controllers with and without an integrator. Experimental results show that the LQR controller with an integrator is better in performance than one without an integrator.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1382-1386
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• 2003

In this paper, we propose a controller for the position of a cart and the angle of a pendulum. To achieve both purposes simultaneously, we divide the system into the dominant subsystem and the dominated one after partial feedback linearization. The proposed controller is composed of a nonlinear controller stabilizing the dominant subsystem and a linear quadratic controller. Using the proposed controller, the controllable region is increased by the nonlinear control part and the control input minimized by the linear control part (LQR).

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2242-2244
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• 2003

This paper considers a networked control system (NCS) that consists of an inverted cart pendulum, a digital controller, and a controller area network (CAN) in which the actuator and sensors of the pendulum are connected to form a closed-loop system. The worst-case message response time (WCMRT) in the CAN is analyzed and the analysis results are applied to the target control system. For the case where the control system cannot satisfy the WCMRT condition and therefore time delays are inevitable, the Luck and Ray method is used to compensate the network-induced time delays. Simulations are carried out to show the feasibility of the proposed scheme.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.2
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• pp.155-160
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• 2006

In the paper a robust indirect adaptive fuzzy controller is proposed for balancing and position control of the inverted pendulum system. Because balancing control rules of the pendulum and position control rules of the cart can be opposite, it is difficult to design an adaptive fuzzy controller that satisfy both objectives. To stabilize the pendulum at a specified position, the proposed fuzzy controller consists of a robust indirect adaptive fuzzy controller for balancing and a supervisory fuzzy controller which emulates heuristic control strategy and arbitrate two control objectives. It is proved that the signals in the overall system are bounded. Simulation results are given to verify the proposed adaptive fuzzy control method.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.11
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• pp.483-490
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• 2006

When the nonlinearities, such as friction and backlash, are not considered in the controller design, undesirable oscillations can occur in the steady-state response of a control system. This paper deals with a method to reduce oscillations that often appear in the steady-state response of a pendulum system, which is controlled by a state feedback controller based on the linearized system model. With an assumption that the oscillations shown in the steady-state are caused by the Coulomb friction, we improve the performance of stabilization and tracking by estimating and compensating for the Coulomb friction in the pendulum system. Experimental results show that the control performance can be improved sufficiently by the proposed method, when it is applied to an inverted cart pendulum which is a multi-variable unstable system. Furthermore, we could see that the Coulomb friction model used in the estimation of the friction is valid in applying the suggested method.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.8
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• pp.835-841
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• 2014

In this paper we propose a swing-up strategy for a single inverted pendulum. The proposed method has a feature whereby can handle the input and output constraint of a pendulum in a systematic way. For the swing-up of a pendulum, we adopt a 2-DOF control structure that combines the feedforward and feedback control. In order to generate the swing-up feedforward trajectories that satisfy the input and output constraint, we formulate the problem of generating feedforward trajectories as a nonlinear optimal control problem subject to constraints. We illustrate that the proposed method is more flexible than the existing method and provides great freedom in choosing the actuator of the inverted pendulum. Through an experiment, we show that the proposed method can swing a pendulum upward effectively while satisfying all the imposed constraints.

• Proceedings of the KIEE Conference
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• 2002.11c
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• pp.58-62
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• 2002

This paper proposes a new nonlinear controller to swing-up an inverted pendulum system mounted on a car. This controller considers not only the pendulum but also the displacement of the cart. A single-input multi-output system is considered to control the inverted pendulum by using partial feedback linearization and nonlinear additional input. The asymptotic stability of the system is shown by using Lyapunov function. The simulation results show effectiveness of the proposed controller.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.05a
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• pp.529-532
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• 2012

This paper presents the system modeling, analysis, and controller design and implementation with a inverted pendulum system in order to test robust algorithm for sweeping machine. The balancing of an inverted pendulum 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.

• 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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.23 no.9A
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• pp.2211-2220
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• 1998

This paper presetns an adaptive critic self-learning control system with cerebellar model articulation controller (CMAC)-based decoder integrated with the associative search element (ASE) and adatpive critic element(ACE)- based scheme. The tast of the system is to balance a pole that is hinged to a movable cart by applying forces to the cart's base. The problem is that error feedback information is limited. This problem can be sloved when some adaptive control devices are involved. The ASE incorporates prediction information for reinforrcement from a critic to produce evaluative information for the plant. The CMAC-based decoder interprets one state to a set of patways into the ASE/ACE. These signals correspond to te current state and its possible preceding action states. The CMAC's information interpolation improves the learning speed. And design inverted pendulum hardware system to show control capability with neural network.