• Journal of Advanced Marine Engineering and Technology
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• v.31 no.4
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• pp.462-467
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• 2007

The design and synthesis of a state feedback controller assumes the feedback of all state variables of the system. However, some state variables are not physical quantifies so that sensors may not be available, or may be too expensive to measure. Hence, a state observer can be an alternative to estimate unmeasurable state variables. This paper therefore presents a scheme for state observer-based stabilization control of inverted pendulum systems. The feedback gain matrices of both the state feedback controller and the state observer are tuned by real-coded genetic algorithms(RCGAs) such that the given performance indices are minimized. The proposed method is demonstrated through simulations.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2004.05a
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• pp.555-558
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• 2004

Due to the asymptotic property, deadbeat control can hardly applied to the continuous time system control. But some delay element method can deal such a problem. Except delay element method, well-known digital deadbeat control can br used with the aid of som smoothing elements. In this paper, and order smoothing element is used for the smoothing of the digital deadbeat controller. And this element is argumented to the plant, and so control problem is to control digitally the argumented system. We simulated this control system using Matlab language and finally apply this algorithm to the rotary inverted pendulum system.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.10
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• pp.457-463
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• 2001

This paper presents a simple and new digital redesign algorithm for fuzzy-model-based controllers. In the first stage, a continuous-time TS fuzzy model is constructed for a given continuous-time nonlinear system and a corresponding continuous-time fuzzy-model-based controller is established based on the existing controller synthesis algorithms. In the second stage, the continuous-time fuzzy-model-based controller is converted to equivalent discrete-time fuzzy-model-based controller, aiming at maintaining the property of the analogue controlled system, which are called intelligent digital redesign. Finally, the proposed method is applied to the digital control of inverted pendulum system to shows the effectiveness and the effectiveness and the feasibility of the method.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2061-2063
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• 2003

This paper presents a method to compensate the drift of solid-state inertial sensors for control applications. A solid-state gyroscope is evaluated via both theoretical and experimental analyses. From the analytical results, a heuristic compensation method for the drift of the gyroscope is proposed. Experimental results on inverted pendulum control show that the proposed method is feasible since compensated signals from the gyroscope are successfully used in the feedback loop to control the inverted pendulum system.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.705-707
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• 2018

Rotary Inverted Pendulum 은 제어분야에서 비선형 제어 시스템을 설명하기 위해 자주 사용되어왔다. 본 논문은 강화학습 에이전트의 환경으로써 Rotary Inverted Pendulum 을 도입하였다. 이를 통해서 강화학습이 실제 세계에서의 복합적인 문제를 해결할 수 있음을 보인다. 강화학습 에이전트의 가상 환경과 실제 환경을 맵핑시키기 위해서 Ethernet 연결 위에 MQTT 프로토콜을 사용하였으며 이를 통해서 경량화된 IoT 분야에서의 강화학습의 활용도를 조명한다.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2759-2761
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• 2001

Sliding mode is a robust control method and can be applied in the presence of model uncertainties and parameter disturbances. But there ane problems in sliding mode controller. Hard in modeling system parameters, chattering, etc. In this paper, new sliding controller design method is proposed for solving the above problems using fuzzy sliding mode contros(FSMC) scheme are considered. we propose that fuzzy logic system are used to approximate unknown system functions in desinging the SMC of Inverted Pendulum. In the method, a fuzzy logic system is utilized to approximate the unknown function f of the nonlinear system. As a simulation result of applying the inverted pendulum, the sliding controller shows good robust characteristics.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.456-459
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• 2003

This paper deal with the biped walking stability by inverted pendulum type balancing joints. This model is hard to interpretation for the nonlinearity caused by upper direction movement then conventional model which have roll and prismatic joints. We can interpret this model by a linear approximation or interpolation method. This paper use a linear approximation method that can decide a movement of upper direction. Inverted pendulum type balancing joints have a advantage of less movement for keep stability and similar with human than conventional model and this model can be used for humanoid robot. We can see a stability of biped by ZMP(Zero Moment Point). Genetic algorithm is used for trajectory planning that is important for stable walking of biped.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.425-428
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• 2003

In this paper, the stabilization of an inverted pendulum system is studied. Here, the PID control method is adopted to make the system stable. In order to adjust the PID gains, a three-layer neural network, which is based on the back propagation method, is used. Meanwhile, the time for training the neural network depends on the initial values of PID gains and connection weights. Hence, the genetic algorithm Is considered to shorten the time to find the desired values. Simulation results show the effectiveness of the proposed approach.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2001.05a
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• pp.243-248
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• 2001

The stabilization control of Inverted Pendulum(IP) system is difficult because of its nonlinearity and structural unstability. Thus, in this paper, an Evolving Neural Network Controller(ENNC) without Error Back Propagation(EBP) is presented. An ENNC is described simply by genetic representation using an encoding strategy for types and slope values of each active functions, biases, weights and so on. By an evolutionary programming which has three genetic operation; selection, crossover and mutation, the predetermine controller is optimally evolved by updating simultaneously the connection patterns and weights of the neural networks. The performances of the proposed ENNC(PENNC) are compared with the ones of conventional optimal controller and the conventional evolving neural network controller(CENNC) through the simulation and experimental results. And we showed that the finally optimized PENNC was very useful in the stabilization control of an IP system.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.227-233
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• 1996

The Inverted Pendulum has been one of most popular nonlinear dynamic systems for the exploration of control techniques. This paper presents a new linear optimal control techniques and nonlinear neural network learning methods. The multiayered neural networks are used to add nonlinear effects on the linear optimal regulator(LQR). The new regulator can compensate nonlinear system uncertainties that are not considered in the LQR design, and can tolerated a wider range of uncertainties than the LQR alone. The new regulator has two neural networks for modeling and control. The neural network for modeling is used to obtain a more accurate model than the given mathematical equations. The neural network for control is used to overcome deficiencies by adding corrections to the linear coefficients of the LQR and by adding nonlinear effects on the LQR. Computer simulations are performed to show the applicability and a more robust regulator than the LQR alone.