• Journal of Advanced Marine Engineering and Technology
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• v.32 no.4
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• pp.618-623
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• 2008

This paper presents a design method of the Interpolation-based adaptive LQ controller that is accomplished by getting the final controller interpolated with each gain of sub-LQ controllers. The Lagrange interpolation method is used in the scheme. The proposed controller is useful to control nonlinear systems which are especially changed the system parameters. The design method is illustrated by an application to the stabilization and tracking problems of an inverted pole system on a cart. Several cases of simulations are carried out in order to validate the control effectiveness and robustness. The simulation results are compared with those of LQ controller and prove the better control performance than LQ controller.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.3
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• pp.372-384
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• 1993

The inverted pendulum system has interesting and challenging problems related to robotics and rocket attitude control view of both control theory and applications. Generally approximately linearized plant models are employed to control the system. In this paper a recently developed control theory based on differentiable manifold theory is used to control the inverted pendulum system which is typically nonlinear. First, the nonlinear model is transformed into the approximate feedback linearized system by nonlinear state feedback. Secondly, the linear controller is designed using the pole-placement method for the approximate feedback linearized plant model, the output of which are finally inverse-transformed to yield the control input to the actual system of the inverted pendulum. The proposed method is evaluated by the computer simulation to compare with the 3rd order linearization model.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.510-512
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• 1999

In this paper, we present a method for the analysis and design of fuzzy controller for nonlinear systems. In the design procedure, we represent the dynamics of nonlinear systems using a Takagi-Sugeno fuzzy model and formulate the controller rules, which shares the same fuzzy sets with the fuzzy system, using parallel distributed compensation method. Then, after the feedback gain of each local state feedback controller is obtained using the existing optimal pole-placement scheme, we construct an overall fuzzy logic controller by blending all local state feedback controller. Finally, the effectiveness and feasibility of the proposed fuzzy-model-based controller design method has been evaluated through an inverted pendulum system.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.144-149
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• 1993

This paper discusses a design of a nonlinear control for a class of single-input double-output nonlinear mechanical systems. When conventional linearization methods are applied to the mechanical systems, some problems of oscillation and unstable phenomena arise. The proposed nonlinear control system resolves these problems. In this design the eigenvalues of the closed-loop nonlinear system are assigned to desired locations and local asymptotic stability of the closed-loop system. is guaranteed. The design method is applied to an inverted pendulum system with a moving weight mechanism. Experimental results show that the proposed nonlinear controller is more effective for stability than the usual linear controller.

• Journal of Advanced Marine Engineering and Technology
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• v.38 no.6
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• pp.710-715
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• 2014

This paper investigates the design scheme of fuzzy-based adaptive controller to give adaptability for controlling nonlinear systems. For this, a nonlinear system is linearized by the several subsystems depending on the operating point or parameter changes. Then, the sub-controller is designed by linear control scheme for each subsystem and the sub-controllers are fused with each gain of sub-controllers using fuzzy rules. The proposed method is applied to an inverted pole system which has structurally instability and nonlinearity, and simulation works are shown to illustrate the effectiveness by comparison with the interpolation-based adaptive Controller.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.862-864
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• 1999

Adaptive Fuzzy control system is very powerful in nonlinear system, but That system require exactly membership function and parameter. If the membership function and parameter are not exact, the system will generate chattering. Using the Pole assignment compensator can remove the chattering and steepest descent method can reduce the convergence time. In this Paper, this algorithm applicate to the Inverted pendulum, so save proof of algorithm that is to be vigorous.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.3
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• pp.268-274
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• 2013

Any new method for controlling a nonlinear system has widely been reported. An inverted pendulum system has typically been used as a target system for demonstrating its usefulness. In this paper, we propose an algorithm to control a flexible joint cart based inverted pendulum system. Two carts are connected with a spring and one is a driving cart and the other is no driving cart with a pole. We here present a system modeling and a good fuzzy logic based control algorithm. We also introduce LQR (Linar Quadratic Regulator) technique for reducing the number of control variables. By using this technique, the number of input variables for a fuzzy logic controller is become only two not six. So the computational complexity is largely reduced. Moreover, a two-input fuzzy logic controller has a control rule table with a skew-symmetric property. And it will lead the design of a single-input fuzzy logic controller. In order to demonstrate the usefulness of the proposed method and prove the superiority of the proposed method, some computer simulations are presented.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.5
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• pp.593-597
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• 2004

This paper presents the new design method of fuzzy control system for nonlinear system. Many conventional design methods for fuzzy controller find the control gain for stabilizing fuzzy controller with some mathematical approaches. However, there exist some controllers which are hard to design with mathematical approach. In order to solve these problems, we propose the intelligent design method for fuzzy controller by using genetic algorithm with evolution strategy. The genetic algorithm with evolution strategy finds the control gain by changing the evolution region of chromosome. Finally, an application example of stabilizing a cart-pole typed inverted pendulum system will be given to show the stabilizability of the fuzzy controller.