• Journal of Institute of Control, Robotics and Systems
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• v.21 no.12
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• pp.1160-1166
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• 2015

This paper proposes a decentralized robust adaptive control scheme for robot manipulators with input torque saturation in the presence of uncertainties. The control system should consider the practical problems that the controller gain coefficients of each joint may be nonlinear time-varying and the input torques applied at each joint are saturated. The proposed robot controller overcomes the various uncertainties and the input saturation problem. The proposed controller is comparatively simple and has no robot model parameters. The proposed controller is adjusted by the adaptation laws and the stability of the control system is guaranteed by the Lyapunov function analysis. Simulation results show the validity and robustness of the proposed control scheme.

• Proceedings of the KIEE Conference
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• 2007.11c
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• pp.160-164
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• 2007

MRAS observer, which is based on adaptive control theory, estimates speed and position by using optimal observer gains on the basis of Lyapunov stability theory. However, in case of MRAS theory, position estimation error is in existence because of non-linearity for inductance variation and limit cycles for position estimation. The adaptive sliding observer based on the variable structure control theory estimates the speed and position for zero of estimation error by using the sliding surface equal to the error between speed and position estimation. The binary observer estimates the rotor speed and rotor flux with alleviation of the high-frequency chattering, and retains the benefits achieved in the conventional sliding observer, such as robustness to parameter and disturbance variations. The speed and position sensorless control of SRM under the load and inductance variation is verified by the experimental results.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.512-517
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• 1994

The control system of a precise positioning mechanism with resonance and Coulomb friction has been designed using H$_{\infty}$ control theory, and the control performance has been verified by computer simulation and experimental analysis. The DGKF type H$_{\infty}$ control theory with scalar weighting factors was utilized for designing the control system. The followings have been confirmed from the present study: (1) The system with H$_{\infty}$ control presents better convergence and stability than the system with conventional control (PI-notch filter control). (2) The H$_{\infty}$ control system have good robustness properties for a wide range of operating conditions in the presence of external disturbances such as Coulomb friction and changing mechanical resonant frequency.ncy.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2190-2192
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• 2003

In this paper, a state observer based direct adaptive fuzzy controller for unknown nonlinear dynamical system is presented. The adaptive parameters of the direct adaptive fuzzy controller can be tuned by using a projection algorithm on-line based on the Lyapunov synthesis approach. A maximum control is used to guarantee the robustness of system. A stability analysis of the overall adaptive scheme is discussed based on the sense of Lyapunov. The inverted pendulum simulation example shows that proposed control algorithm can be used for the tracking problem of nonlinear system.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.6
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• pp.477-483
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• 2005

This article describes a robust state estimation method which enables to produce reliable estimates in spite of heavy perturbation including plant uncertainty and external disturbances. The main idea is to combine the standard state estimator with the perturbation observer in the estimator frame. The perturbation observer reflects equivalent quantity of plant uncertainty and external disturbances during the estimation process so that the state estimator dynamics gets as close as possible to the real plant dynamics. The robust state estimator proposed in this paper is given in a recursive discrete-time form which is very useful fur implementation purpose. In terms of the error dynamics derived for the robust state estimator, we discuss the stability issue and noise sensitivity. The effectiveness and practicality of the robust state estimator are verified through numerical examples and experimental results.

• Journal of Institute of Control, Robotics and Systems
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• v.2 no.1
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• pp.5-8
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• 1996

This paper proposea a new adaptive algorithm to improve the performance fo a robust adaptive control system. This adaptive algorithm counteracts the effects of disturbances and makes the time derivative of Lyapunov function non-positive, $V{\leq}0$. As a result, the output error approaches zero as $t\;\rightarrow\;\infty$ not only in the presence of bounded disturbances, but also in the ideal case. The effectiveness of this proposed algorithm is demonstrated by the stability analysis and simulation results.

• Proceedings of the Korean Vacuum Society Conference
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• 2014.02a
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• pp.287.2-287.2
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• 2014

PV (photovoltaic) has becoming an important industry to invest due to its high robustness and require very little maintenance which goes a long time. Solar cell fabrication involves a few critical processes such as doping to make the N-type and P-type silicon, contact metallization, surface texturization, and anti-reflection coatings. Anti-reflection coating is a kind of surface passivation which ensures the stability, and efficiency of the solar cell. Thus, I will focus on the changes happen to the solar cell due to the reflectance and anti-reflection coating by PC1D. By using the PC1D (solar cell simulation program), I would analysis the effect of reflectance on the N-type cell. At last I will conclude the result regarding what I learned throughout this experiment.

• Journal of Electrical Engineering and Technology
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• v.3 no.1
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• pp.135-142
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• 2008

This paper deals with the design of a robust PI controller for a vehicle suspension system. A method, which is related to computation of all stabilizing PI controllers, is applied to the vehicle suspension system in order to obtain optimum control between passenger comfort and driving performance. The PI controller parameters are calculated by plotting the stability boundary locus in the $(k_p,\;k_i)$-plane and illustrative results are presented. In reality, like all physical systems, the vehicle suspension system parameters contain uncertainty. Thus, the proposed method is also used to compute all the parameters of a PI controller that stabilize a vehicle suspension system with uncertain parameters.

• Journal of Institute of Control, Robotics and Systems
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• v.2 no.1
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• pp.60-66
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• 1996

Model Based Predictive Control(MBPC) has been widely used in predictive control since 80's. GPC[1] which is the superset of many MBPC strategies a popular method, but GPC has some weakness, such as insufficient stability analysis, non-applicability to internally unstable systems. However, CRHPC[2] proposed in 1991 overcomes the above limitations. So we chose RHPC based on CRHPC for electric furnace control. An electric furnace which has nonlinear properties and large time delay is difficult to control by linear controller because it needs nearly perfect modelling and optimal gain in case of PID. As a result, those controls are very time-consuming. In this paper, we applied RHPC with equality constraint to electric furnace. The reults of experiments also include the case of RHPC with monotonic weighting improving the transient response and including unmodelled dynamics. So, This paper proved the practical aspect of RHPC for real processes.

• Proceedings of the KIEE Conference
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• 2003.11b
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• pp.187-190
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• 2003

In this paper, we address the robust adaptive backstepping controller using fuzzy neural network (FHIN) for a class of uncertain output feedback nonlinear systems with disturbance. A new algorithm is proposed for estimation of unknown bounds and adaptive control of the uncertain nonlinear systems. The state estimation is solved using K-fillers. All unknown nonlinear functions are approximated by FNN. The FNN weight adaptation rule is derived from Lyapunov stability analysis and guarantees that the adapted weight error and tracking error are bounded. The compensated controller is designed to compensate the FNN approximation error and external disturbance. Finally, simulation results show that the proposed controller can achieve favorable tracking performance and robustness with regard to unknown function and external disturbance.