• Journal of Biosystems Engineering
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• v.31 no.6 s.119
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• pp.489-499
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• 2006

Since the application of chemicals in confined spaces under the canopy of an orchard is hazardous work, it is needed to develop an autonomous guidance system for an orchard sprayer. The autonomous guidance system developed in this research could steer the vehicle by tracking an overhead guidance rail, which was installed on an existing frame structure. The autonomous guidance system consisted of an 80196 kc microprocessor, an inclinometer, two interface circuits of actuators for steering and ground speed control, and a fuzzy control algorithm. In addition, overhead guidance rails for both straight and curved paths were devised, and a trolley was designed to move smoothly along the overhead guidance rails. Evaluation tests showed that the experimental vehicle could travel along the desired path at a ground speed of 30 $\sim$ 50 cm/s with a RMS error of 5 cm and maximum deviation of less than 12 cm. Even when the vehicle started with an initial offset or a deflected heading angle, it could move quickly to track the desired path after traveling 2 $\sim$ 3 m. The vehicle could also complete turns with a curvature of 1 m. However, at a ground speed of 50 cm/s, the vehicle tended to over-steer, resulting in a zigzag motion along the straight path, and tended to turn outward from the projected line of the guidance rail.

• Proceedings of the KIPE Conference
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• 2011.07a
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• pp.105-106
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• 2011

본 논문에서는 퍼지제어기를 이용한 전기자동차용 영구자석 동기전동기의 영속도 제어 기법에 관하여 기술한다. 전기자동차가 언덕길 위에 정지한 상태에서 다시 출발할 경우에 차량이 뒤로 밀리는 현상이 발생한다. 따라서 탑승자의 안전과 편안함을 위해서 운전자의 브레이크 지령에 대한 제어기의 영속도 지령 수령 시, 차량은 움직이지 않고 계속 정지한 상태가 되어야 한다. 본 논문에서는 속도제어와 전류제어를 수행하기 위하여 기존의 PI제어기 대신 퍼지제어기를 적용해 영속도 제어를 수행하였다. 제어 성능은 시뮬레이션을 통하여 확인하였다.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.4
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• pp.449-455
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• 2014

This paper presents a method for developing a TM (Traversability Map) from a DTM (Digital Terrain Model) collected by remote sensors of autonomous mobile robots. Such a map can be used to plan traversable paths and estimate navigation speed quantitatively in real time for robots capable of performing autonomous tasks over rough terrain environments. The proposed method consists of three parts: a DTM partition module which divides the DTM into equally spaced patches, a terrain information module which extracts the slope and roughness of the partitioned patches using the curve fitting and the fractal-based triangular prism method, and a traversability analysis module which assesses traversability incorporating with extracted terrain information and fuzzy inference to construct a TM. The potential of the proposed method is validated via simulation works over a set of fractal DTMs.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.714-716
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• 1995

This Paper presents a novel and systematic approach to a self-learning controller. The proposed controller is built on a neural network consisting of a standard back propagation (BNN) and approxinate reasoning (AR). The fuzzy inference and knowledge representation are carried out by the neural network structure and computing, instead of logic inference. An architecture similar to that used by traditional model reference adaptive control system (MRAC) is employed.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.4
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• pp.271-276
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• 2006

To implement a generalization of value functions in Adaptive Search Element (ASE)-reinforcement learning, CMAC (Cerebellar Model Articulation Controller) is integrated into ASE controller. ASE-reinforcement learning scheme is briefly studied to discuss how CMAC is integrated into ASE controller. Neighbourhood Sequential Training for CMAC is utilized to establish the look-up table and to produce discrete control outputs. In computer simulation, an ASE controller and a couple of ASE-CMAC neural network are trained to balance the inverted pendulum on a cart. The number of trials until the controllers are established and the learning performance of the controllers are evaluated to find that generalization ability of the CMAC improves the speed of the ASE-reinforcement learning enough to realize the cartpole control system.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2132-2134
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• 2001

We propose an Tabu search changing neighborhood solution's range to be searched each iteration according to an objective function. It is applied for designing the scaling factors of Fuzzy Logic Controller (FLC) using the proposed Tabu search. We apply it to the speed control of AC Servomotor to evaluate the usefulness of the proposed method. As a result of the computer simulation, the FLC shows the better performance than PI controller in terms of overshoot and settling time.

• Proceedings of the KIEE Conference
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• 2006.04b
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• pp.155-157
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• 2006

The paper is proposed maximum torque control of SynRM drive using adaptive learning mechanism-fuzzy neural network(ALM-FNN) controller and artificial neural network(ANN). The control method is applicable over the entire speed range and considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal d-axis current $i_d$ for maximum torque operation is derived. The proposed control algorithm is applied to SynRM drive system controlled ALM-FNN and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper is proposed the analysis results to verify the effectiveness of the ALM-FNN and ANN controller.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.2
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• pp.108-114
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• 2009

This paper proposes the new hybrid adaptive controller for fast response time and precision control of automation system which exist deadzone or non-linearity of system. The proposed system, which provides the improvement in terms of the control region in high speed and precision control, first used the fuzzy control method for fast response time and when the error reaches the preset value, used the PLL method designing PFD improved jitter for precision control. The new designed PFD improves the jitter noise and response characteristic without generating deadzone. The theoretical and experimental studies have been carried out. The presented results from the above investigation show considerably improved performance in the position control of automation system.

• Proceedings of the KIEE Conference
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• 2005.10c
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• pp.198-201
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• 2005

The paper is proposed maximum torque control of IPMSM drive using adaptive learning mechanism-fuzzy neural network (ALM-FNN) controller and artificial neural network(ANN). The control method is applicable over the entire speed range and considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal d-axis current $^i_d$ for maximum torque operation is derived. The proposed control algorithm is applied to IPMSM drive system controlled ALM-FNN and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper is proposed the analysis results to verily the effectiveness of the ALM-FNN and ANN controller.

• Earthquakes and Structures
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• v.24 no.1
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• pp.1-9
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• 2023

A model for calculating structure interacted mechanics is proposed. A structural interaction model and controller design based on tuned mass damping (TMD) was developed to control the induced vibration. A key point is to introduce a new analytical model to evaluate the properties of the TMD that recognizes the motion-dependent nonlinear response observed in the simulations. Aiming at the problem of increased current harmonics and low efficiency of permanent magnet synchronous motors for electric vehicles due to dead time effect, a dead time compensation method based on neural network filter and current polarity detection is proposed. Firstly, the DC components and the higher harmonic components of the motor currents are obtained by virtue of what the neural network filters and the extracted harmonic currents are adjusted to the required compensation voltages by virtue of what the neural network filters. Then, the extracted DC components are used for current polarity dead time compensation control to avert the false compensation when currents approach zero. The neural network filter method extracts the required compensation voltages from the speed component and the current polarity detection compensation method obtains the required compensation voltages by discriminating the current polarity. The combination of the two methods can more precisely compensate the dead time effect of the control system to improve the control performance. Furthermore, based on the relaxed method, the intelligent approach of stability criterion can be regulated appropriately and the artificial TMD was found to be effective in reducing cross-wind vibrations.