• Proceedings of the KIEE Conference
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• 2004.05a
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• pp.44-46
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• 2004

본 논문에서는 이동로봇이 임의의 선행물체를 추종할 때 진행 경로상에 이동장애물이 진입하는 경우 이 장애물을 효과적으로 회피할 수 있는 방법을 제시한다 초음파 센서를 이용하여 이동로봇의 진행경로에 진입하는 이동장애물에 대한 거리 정보와 방향각(Heading Angle)을 구할 수 있다. 이동로봇의 본체 주위에 배치된 16개의 초음파 센서를 이용하여 이동로봇의 전면, 후면 및 측면의 데이터를 얻을 수 있으며 이 정보를 퍼지제어기의 입력으로 사용한다 퍼지제어기는 이러한 입력정보와 제안된 규칙 베이스를 이용하여 이동로봇의 진행방향과 속도를 결정한다. 본 논문에서 제안한 퍼지제어기를 이용한 시뮬레이션을 통해 이동장애물에 대한 효과적인 충돌회피가 수행됨을 보인다.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.4
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• pp.62-68
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• 1998

In this paper, a speed controller considering the effects of parameter variations and external disturbance for induction motor driver is designed. An proportional plus integral(P1) fuzzy controller is designed to match desired speed tracking specification. Then a robust controller using Fuzzy Weight matrix are designed that in order to reduce the effect of parameter variations caused by external disturbance. The desired speed tracking control performance of the driver is preserved under wide operating range, and also good speed performance is confirmed by the computer simulation.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.9
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• pp.788-792
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• 2001

Although the general sliding model control has the robust property, bounds on the disturbances and parameter variations should be known a prior to the designer of the control system. However, these bounds may not be easily obtained. Fuzzy logic provides an effective way to design a controller of the system with disturbances and parameter variations. Therefore, combination of the best feature of the fuzzy logic control and the sliding mode control is considered. In this paper, the adaptive fuzzy variable structure controller developed for variables of fuzzy logic. A variable length pendulum system is used to demonstrate the availability of the proposed algorithm.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.4
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• pp.104-109
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• 1997

A robust controller design to corrdinate a robot manipulator under unknown system parameters and bounded disturbance inputs is presented in this paper. Generally, robust controllers require high input torque so that they may face input saturation in actual application due to the power limitation of the actuator. To solve this problem, an improved robust controller with saturated input torque using a fuzzy logic control is proposed. Numerical examples are shown to validate the proposed controller using two degree-of-freedom planar arm.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.7 no.5
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• pp.823-829
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• 2006

In this paper presents approaches to the design of a hybrid fuzzy logic proportional plus conventional integral- derivative(fuzzy P+ID) controller in an incremental form. This controller is constructed by using an incremental fuzzy logic controller in place of the proportional term in a conventional PID controller. The PID type controller has been widely used in industrial application due to its simply control structure, easy of design, and inexpensive cost. However, control performance of the PID type controller suffers greatly from high uncertainty and nonlinearity of the system, large disturbances and so on. This paper presents a hybrid fuzzy logic proportional plus conventional integral derivative controller In comparison with a conventional PID controller, only one additional parameter has to be adjusted to tune the Fuzzy P+ID controller. In this case, the stability of a system remains unchanged after the PID controller is replaced by the Fuzzy P+ID controller without modifying the original controller parameters. Finally, the proposed hybrid Fuzzy P+ID controller is applied to BLDC motor drive. Simulation results demonstrated that the control performance of the proposed controller is better than that of the conventional controller.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.11
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• pp.91-99
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• 2003

When the driver suddenly depresses the brake pedal under critical conditions, the desired trajectory of the vehicle can be changed. In this study, the vehicle dynamics and fuzzy logic controller are used to control the vehicle trajectory. The dynamic vehicle model consists of the engine, the rotational wheel, chassis, tires and brakes. The engine model is derived from the engine experimental data. The engine torque makes the wheel rotate and generates the angular velocity and acceleration of the wheel. The dynamic equation of the vehicle model is derived from the top-view vehicle model using Newton's second law. The Pacejka tire model formulated from the experimental data is used. The fuzzy logic controller is developed to compensate for the trajectory error of the vehicle. This fuzzy logic controller individually acts on the front right, front left, rear right and rear left brakes and regulates each brake torque. The fuzzy logic controlling each brake works to compensate for the trajectory error on the split - $\mu$ road conditions follows the desired trajectory.

• Journal of Biosystems Engineering
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• v.20 no.1
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• pp.87-94
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• 1995

When farmer loads and unloads farm products with a trailer, linked to a tractor, the tractor-trailer is backed up to the loading duck. However, travelling backward is not easy and takes a time for even skilled operators. Therefore, unmanned backing up is necessary to save the effort. A backward controller of tractor-trailer was simulated using fuzzy logic and genetic algorithms. Operators drive the tractor-trailer back and forth several times for backing up to the loading duck. As the operators did it, a backward controller was designed using fuzzy logic. And genetic algorithms was applied to improve the performance of the backward controller. With the strings coded with the fuzzy membership functions, genetic operations were carried out. After 30 generations, the best fitted fuzzy membership functions were found. Those membership functions were used in the fuzzy backward controller. The fuzzy controller combined with genetic algorithms showed the better results than the fuzzy controller did alone.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.1
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• pp.138-144
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• 2000

Robot manipulators, which are nonlinear structures and have uncertain system parameters, have complex in dynamics when are operated in unknown environment. To compensate for estimate errors of the uncertain system parameters and to accomplish the desired trajectory tracking, nonlinear robust controllers are appropriate. However, when estimation errors or tracking errors are large, they require large input torques, which may not be satisfied due to torque limits of actuators. As a result, their stability can not be guaranteed. In this paper, a new robust control scheme is presented to solve stability problem and to achieve fast trajectory tracking in the presence of torque limits. By using fuzzy logic, new desired trajectories which can be reduced are generated based on the initial desired trajectory, and torques of the robust controller are regulated to not exceed torque limits. Numerical examples are shown to validate the proposed controller using an uncertain two degree-of-freedom underwater robot manipulator.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.2
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• pp.152-160
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• 2000

This paper addresses the analysis and design of fuzzy-model-based controller for nonlinear systems using extended PDC and optimal pole-placement schemes. In the design procedure, we represent the nonlinear system using a Takagi-Sugeno fkzy model and formulate the controller rules by using the extended parallel distributed compensator (EPDC) and construct an overall fuzzy logic controller by blending all local state feedback controllers with an optimal pole-placement scheme. Unlike the commonly used parallel distributed compensation technique, by blending a newly extended parallel distributed compensator and the optimal poleplacement schemes, we can design not only a local stable k z y controller but also an overall stable fuzzy controller to perform the tacking control objective. Furthermore, a stability analysis is carried out not only for the fuzzy model but also for a real nonlinear system. Finally. the effectiveness and feasibility of the proposed fizzy model-based controller design method has been shown through a simulation example.

• Journal of the Korean Institute of Intelligent Systems
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• v.2 no.1
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• pp.17-32
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• 1992

The multi-layer neural network that has broadly been utilized in designing the controller of robot manipulator possesses the desirable characteristics of learning capacity, by which the uncertain variation of the dynamic parameters of robot can be handled adaptively, and parallel distributed processing that makes it possible to control on real-time. However the error back propagation algorithm that has been utilized popularly in the learning of the multi-layer neural network has the problem of its slow convergence speed. In this paper, an approach to improve the convergence speed is proposed using the fuzzy logic that can effectively handle the uncertain and fuzzy informations by linguistic level. The effectiveness of the proposed algorithm is demonstrated by computer simulation of PUMA 560 robot manupulator.