• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.1901-1905
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• 2004

A controller designed by CDM for a servo type system which is an augmented system constructed from a rotational inverted pendulum with an integrator added to its arm, is presented in this paper. In order to be able to apply the CDM concept, the augmented system must be linearized and converted into controllable canonical form. Then, the controller consisting of the state feedback gain matrix and an integral gain in the sense of CDM can be obtained. This shows that design procedure for the proposed controller is easy. The experimental results obtained from the rotational inverted pendulum controlled by the proposed controller show that the system response has no steady-state error, however, the oscillation amplitude of the arm angle is still significant. Therefore, in this paper, the friction compensation using Coulomb friction with stiction is also added to the controller. The oscillation amplitude of the arm angle that can be reduced remarkably is also shown in the experimental results.

• 한국기계가공학회지
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• 제15권2호
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• pp.125-130
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• 2016

In this paper, we deal with the dynamic walking of a humanoid robot. In our method, the inverted pendulum model is used as a dynamic model for a humanoid robot in which the Zero Moment Point (ZMP) and COG constraints of the robot are analyzed by considering the motion of the robot as that of an inverted pendulum. The motion of a humanoid robot should be generated by considering the dynamics of the robot, which commonly requires a large amount of computation. If a robot walks from one position to another while keeping the ZMP in the stable region, then the robot remains dynamically stable. The linear inverted pendulum model regards the whole robot as a point mass. It is simple, and relatively less computation is needed; however, it cannot model the whole dynamics of a humanoid robot. We propose a method for modeling a humanoid robot as an inverted pendulum system having 14 point masses. We also show that the dynamic stability of a humanoid robot can be determined more precisely by our method.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 제37회 하계학술대회 논문집 D
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• pp.1775-1776
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• 2006

This paper deals with the problem of friction measurement of an inverted pendulum system using the regression analysis and proposes a solution. The approach taken in this study is getting the friction from a regression relational expression between the motor voltage and the cart velocity of an inverted pendulum system. The result to compensate LQR (linear Quadratic Regulator) controller with the friction which is measured in system, improved the performance of the system. Above all, the study has found that the proposed compensation of the friction reduces the oscillation of the cart position. In conclusion, the proposed method is useful when parameters in the given system model are not known.

• 대한기계학회논문집A
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• 제24권4호
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• pp.1065-1074
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• 2000

A robust nonlinear $H_2$/$H_{\infty}$ control method for a parallel inverted pendulum with structured perturbation and dry friction is proposed. By the random input describing function techniques, the nonlinear dry friction is approximated into the quasi-linear system. Introducing the quadratic robustness theorem, the robust $H_2$/$H_{\infty}$ control system is constructed for the quasi-linear perturbed system. But it is difficult to design a controller due to the nonlinear correction term in Riccati equation. With some transformations on the Riccati equation containing nonlinear correction term, the design of the robust nonlinear controller can be done easily. Hence when the stiffness and mass of the parallel inverted pendulum vary in certain ranges, the proposed control scheme has the robustness for both the structured perturbation and dry friction. The results of computer simulation show the effectiveness of our proposed control method.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
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• pp.691-695
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• 1998

Most of systems has nonlinearity . And also accurate modelings of these uncertain nonlinear systems are very difficult. In this paper, a fuzzy modeling technique for the stabilization control of an IP(inverted pendulum) system with nonlinearity was proposed. The fuzzy modeling was acquired on the basis of ANFIS(Adaptive Neuro Fuzzy Infernce System) which could learn using a series of input-output data pairs. Simulation results showed its superiority to the PID controller. We believe that its applicability can be extended to the other nonlinear systems.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 하계학술대회 논문집 D
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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.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제7권4호
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• pp.267-273
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• 2007

This paper presents the implementation of an educational kit for intelligent system control education. Neural network control algorithms are presented and control hardware is embedded to control the inverted pendulum system. The RBF network and the MLP network are implemented and embedded on the DSP 2812 chip and other necessary functions are embedded on an FPGA chip. Experimental studies are conducted to compare performances of two neural control methods. The intelligent control educational kit(ICEK) is implemented with the inverted pendulum system whose movements of the cart is limited by space. Experimental results show that the neural controllers can manage to control both the angle and the position of the inverted pendulum systems within a limited distance. Performances of the RCT and the FEL control method are compared as well.

• 한국기계가공학회지
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• 제17권1호
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• pp.116-121
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• 2018

In this paper, we propose the SIIM fuzzy Quasi-sliding mode controller for the system of a double inverted pendulum on a cart. Since it is difficult to handle this 6th-order system, we decoupled the entire system into three $2^{nd}$ order subsystem, and we designed the SIIM fuzzy Quasi-sliding mode controller for each subsystem, which was easy and did not require the derivation of the equivalent control. The stability of the entire system is guaranteed using Lyapunov function. The validity and robustness of the proposed controller are demonstrated through the computer simulation, and the results are compared with the results of former studies.

• 한국조명전기설비학회:학술대회논문집
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• 한국조명전기설비학회 2004년도 춘계학술대회 논문집
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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.

• 한국마린엔지니어링학회:학술대회논문집
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• 한국마린엔지니어링학회 2001년도 춘계학술대회 논문집
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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.