• Proceedings of the Korean Society of Marine Engineers Conference
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• 2002.05a
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• pp.239-244
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• 2002

Even though, tanks are used at the many industry plants, it is very difficult to control the tank level without any overflow and shortage; moreover, cause of its complication of dynamics and nonlinearity, it's impossible to realize the accurate control using the mathematical model which can be applied to the various operation modes. However, the sliding mode controller(SMC) is known as having the robust variable structures for the nonlinear control systems with the parametric perturbations and with the sudden disturbances, but the auto-tuning of parameters was a problem. Therefore, in this paper, a Genetic Algorithm based Sliding Mode Controller (GA-SMC) for the precise control of the coupled tank level was tried. GA optimized the SMCs switching parameters easily and rapidly. The simulation results are shown that the tank level could be satisfactorily controlled with less overshoot and steady-stale error by the proposed GA-SMC.

• Journal of Power System Engineering
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• v.18 no.5
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• pp.129-136
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• 2014

This paper proposes improvement on sensorless vector control performance of a permanent magnet synchronous motor (PMSM) with sliding mode observer. An adaptive observer gain and second order cascade low-pass filter (LPF) were used to improve the estimation accuracy of the rotor position and speed. The adaptive observer gain was applied to suppress the chattering intensity and obtained by using the Lyapunov's stability criterion. The second order cascade LPF was designed for the system to escalate the filtering performance of the back-emf estimation. Furthermore, genetic algorithm was used to optimize the system PI controller's performance. Simulation results showed the effectiveness of the suggested improvement strategy. Moreover, the strategy was useful for the sensorless vector control of PMSM to operate on the low-speed area.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.1
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• pp.69-76
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• 2009

This paper describes sensorless high-speed control for 45,000rpm/22kw type PMSM by using iterative adaptive sliding mode observer. The proposed algorithm is based on sensorless vector control by on-line estimating the speed of rotor in the wide speed operating range between the starting operation. In addition, it shows the enhanced performance of the iterative adaptive observer by lessening its chattering and getting stable response in limited PWM period. The simulation and experiment results show the reliable performance of the proposed algorithm through starting to high speed operating range.

• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.101-103
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• 2007

In this paper. we design a robust optimal controller for ultra-precision positioning system. Generally, it is hard to control the nanometric scale positioning system because of the parameter uncertainties and external disturbances. To solve this problem. we suggest a control algorithm based on the modified sliding-mode control and the LQ control in an augmented system. The augmented system is composed of additional state variables: state estimates and control input in the nominal system. Through comparison with LQ optimal control, it is verified that the proposed control algorithm is more robust to the unexpected parameter variations and external noises.

• Journal of the Korean Mathematical Society
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• v.57 no.2
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• pp.401-414
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• 2020

It is computationally expensive to compute principal components from scratch at every update or downdate when new data arrive and existing data are truncated from the data matrix frequently. To overcome this limitations, incremental principal component analysis is considered. Specifically, we present a sliding window based efficient incremental principal component computation from a covariance matrix which comprises of two procedures; simultaneous update and downdate of principal components, followed by the rank-one matrix update. Additionally we track the accurate decomposition error and the adaptive numerical rank. Experiments show that the proposed algorithm enables a faster execution speed and no-meaningful decomposition error differences compared to typical incremental principal component analysis algorithms, thereby maintaining a good approximation for the principal components.

• Proceedings of the KIPE Conference
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• 1998.07a
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• pp.361-365
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• 1998

In this paper, the sliding mode variable structure-fuzzy(SMVS-F) control algorithm is applied to speed controller for field oriented induction motor drive system. According to the principle of sliding mode variable structure-fuzzy adjustable speed control scheme, the proposed algorithm shows good performances which are reducing chattering, robustness against parameter variation in induction motor drive. The validity of the proposed control scheme is verified by computer simulation using SIMULINK.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.323-329
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• 1994

In the systems such as walking robots or high speed operating manipulators, the effect of nonlinear terms is important and can not be neglected. Therefore the application of linear control law to such systems is inadequate. Moreover, because of the mathematical modeling errors the systems may become unstable. In this study, we designed a nonlinear controller with sliding mode scheme, which is robust to the modeling errors and applied this control algorithm to the 5 DOF biped robot system. Throught the computer simulations, we examined walking characteris and walking stability of the 5 DOF biped robot system.

• Journal of Korea Society of Industrial Information Systems
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• v.16 no.4
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• pp.45-52
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• 2011

In the real-field of control cases for robot manipulators, there always exists a modeling error, which results the model has the uncertainties in its parameters and/or structure. In many modem applications, digital computers are extensively used to implement control algorithms to control such systems. The discretization of the nonlinear dynamic equations of such systems results in a complicated discrete dynamic equations. Therefore, it will be difficult to design a discrete-time controller to give good tracking performances in the presence of certain uncertainties. In this paper, a discrete-time sliding mode control algorithm for nonlinear and time varying robot manipulators with uncertainties is presented. Sufficient conditions for guaranteeing the convergence of the discrete-time SMC system are derived. As example simulations, the proposed SMC algorithm is applied to a two-link robotic manipulator with unknown dynamics. The results of the simulation indicate that the developed control scheme is effective in manipulators and electro-mechanical system control.

• Proceedings of the KIEE Conference
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• 1996.11a
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• pp.401-403
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• 1996

In this paper, a new control strategy is presented that combines sliding mode control theory with a neural network. Sliding mode control theory requires the complete knowledge of the dynamics of the controlled system. However, in practice, one often bas only a small number of state measurements. This could be a serious limitation on the practical usefulness of sliding mode control theory. A multilayer neural network is employed to solve this kind of problem. The neural network serves as a compensator without a prior knowledge about the system. The proposed control algorithm is applied to a class of uncertain nonlinear system. The robustness against parameter uncertainty, nonlinearity and external disturbances, and the effectiveness is verified by the simulation results.

• Journal of Korea Society of Digital Industry and Information Management
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• v.17 no.4
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• pp.85-94
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• 2021

The LPR system's trigger sensor makes problem occasionally due to the heave weight of vehicle or the obsolescence equipment. If we replace the hardware sensor to the deep-learning based software sensor in order to generate the trigger signal, LPR system maintenance would be a lot easier. In this paper we proposed the deep-learning sliding window based object detection and tracking algorithm for the LPR system's trigger signal generation. The gate passing vehicle's license plate recognition results are combined into the normal tracking algorithm to catch the position of the vehicle on the trigger line. The experimental results show that the deep learning sliding window based trigger signal generating performance was 100% for the gate passing vehicles including the 5.5% trigger signal position errors due to the minimum bounding box location errors in the vehicle detection process.