• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.12
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• pp.2678-2686
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• 2002

A novel approach is developed for avoidance of obstacles in unknown environment. This paper proposes a new way of intelligent autonomous wheelchairs for the handicapped to move safely and comfortably. It is the objective of this paper to develop delayed time fuzzy control algorithms to deal with various obstacles. This new algorithm gives the benefit of the collision free movement in real time and optimal path to the moving target. The computer simulations and the experiments are demonstrated to the effect of the suggested control method.

• Journal of Electrical Engineering and Technology
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• v.6 no.1
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• pp.67-75
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• 2011

The development of electric vehicle power electronics system control, composed of DC-AC inverters and DC-DC converters, attract much research interest in the modern industry. A DC-AC inverter supplies the high-power motor torques of the propulsion system and utility loads of electric vehicles, whereas a DC-DC converter supplies the conventional low-power and low-voltage loads. However, the need for high-power bidirectional DC-DC converters in future electric vehicles has led to the development of many new topologies of DC-DC converters. The nonlinear control of power converters is an active research area in the field of power electronics. This paper focuses on the use of the fuzzy sliding mode strategy as a control strategy for buck-boost DC-DC converter power supplies in electric vehicles. The proposed fuzzy controller specifies changes in control signals based on the surface and knowledge on surface changes to satisfy the sliding mode stability and attraction conditions. The performance of the proposed fuzzy sliding controller is compared to that of the classical sliding mode controller. The satisfactory simulation results show the efficiency of the proposed control law, which reduces the chattering phenomenon. Moreover, the obtained results prove the robustness of the proposed control law against variations in load resistance and input voltage in the studied converter.

• Journal of Electrical Engineering and Technology
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• v.13 no.1
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• pp.451-459
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• 2018

This work presents an approach for modeling of electric vehicle considering the vehicle dynamics, drive train, rotational wheel and load dynamics. The system is composed of IPMSM (Interior Permanent Magnet Synchronous Motor) coupled with the wheels through a drive train. Generally, IPMSM is controlled by ordinary PID controllers. Performance of the ordinary PID controller is not satisfactory owing to the difficulties of optimal gain selections. To overcome this problem, a new type of fuzzy logic gain tuner for PID controllers of IPMSM is required. Therefore, in this paper fuzzy logic based gain tuning method for PID controller is proposed and compared with some previous control techniques for the better performance of electric vehicle with an optimal balance of acceleration, speed, travelling range, improved controller quality and response. The model was developed in MATLAB/Simulink, simulations were carried out and results were observed. The simulation results have proved that the proposed control system works well to remove the transient oscillations and assure better system response in all conditions.

• Proceedings of the IEEK Conference
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• 2003.07c
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• pp.2863-2866
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• 2003

In nonlinear control fields, for irregular nonlinear systems, control form which consists of approximate tracking control law and exact tracking control law and which switches between two laws has been proposed recently. In this thesis, we design new switching control law which connect approximate linearization control law and exact linearization control law by fuzzy rules for irregular nonlinear system, ball and beam system. Fuzzy switching controller designed by fuzzy concept is proved that designed scheme overcomes singularities of irregular system, improves unstability problem of switching procedure, and has more efficient control value through simulation. Stability of fuzzy control system proved by Lyapunov's stability theorems.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.619-624
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• 1998

This paper presents a new clustering algorithm called FCM algorithm for the design of fuzzy controller. FCM is an extended version of FCM(Fuzzy c-Means) algorithm and can estimate the number of clusters automatically and give membership grades $u_{ik}$ suitable for making fuzzy control rules. This paper also shows an example of its application to the line pursuit control of a car.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.39.2-39
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• 2001

This paper describes the design of an output-feedback controller based on an adaptive fuzzy observer for uncertain single-input single-output nonlinear dynamical systems. Especially, we have focused on the realization of minimal dynamic order of the adaptive fuzzy observer. For the purpose, we propose a new method in which no strictly positive real(SPR) condition is needed and combine dynamic rule activation scheme with on-line estimation of fuzzy parameters. By using proposed scheme, we can reduce computation time, storage space, and dynamic order of the adaptive fuzzy observer ...

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.127-130
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• 1990

The fuzzy logic controller which can be applied to various industrial processes is quite often dependent on the heuristics of the experienced operator. The operator's knowledge is often uncertain. Therefore an incorrect control rule on the basis of the operator's information is a cause of bad performance of the system. This paper proposes a new self-learning fuzzy control method by the fuzzy system identification using the data pairs of input and output and arbitrary initial relation matrix. The position control of a DC servo motor model is simulated to verify the effectiveness of the proposed algorithm.

• Proceedings of the IEEK Conference
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• 2002.07c
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• pp.1483-1486
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• 2002

In this paper, a new scheme of iterative loaming control of a robot manipulator is presented. The proposed method uses a fuzzy sliding mode controller(FSMC), which is designed based on the similarity between the fuzzy logic control(FLC) and the sliding mode control(SMC), for the feedback. With this, the proposed method makes possible fDr fast iteration and has advantages that no linear approximation is used for the derivation of the learning law or in the stability proof Full proof of the convergence of the fuzzy sliding base learning scheme Is given.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.5
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• pp.46-50
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• 1997

In fuzzy logic control, static fuzzy rules cannot cope with significant changes of parameters of plants or environment. To solve this prohlem, self-organizing fuzzy control. neural-network-hased fuzzy logic control and so on have heen introduced so far. However, dynamically changed fuzzy rules of these schemes may make a fuzzy logic controller Fall into dangerous situations because the changed fuzzy rules may he incomplete or inconsistent. This paper proposes a new adaptive filzzy logic control scheme using a predictivc neural network. Although some parameters of a controlled plant or environment are changed, proposed fuzzy logic controller changes its decision outputs adaptively and robustly using unchanged initial fuzzy rules and the predictive errors generated hy the predictive neural network by on-line learning. Experimental results with a D<' servo-motor position control problem show that propnsed cnntrol scheme is very useful in the viewpoint of adaptability.

• 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.