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송재복,변경석,Song, Jae-Bok,Byeon, Kyeong-Seok 대한기계학회 1997 大韓機械學會論文集A Vol.21 No.12
Accurate positioning of the throttle valve of a gasoline engine is required to implement various systems such as traction control system(TCS), cruise control system and drive-by-wire system. In this research, position control system has been developed for the throttle actuator system that uses one throttle actuation for small volume and DC servo motor for fast response. In order to drive the DC motor, PWM signal generator and PWM amplifier were built and interfaced to the motor and controller. Also, time delay control(TDC) law has been used as a basic control algorithm. A method of varying the reference model of the TDC according to the size of change in target throttle angle is proposed here. The simulation and experimental results show that both overshoot prevention and fast response are achieved by the TDC technique with this variable reference model.
엔진맵에 기초한 엔진제어 TCS 슬립제어 알고리듬의 개발
송재복,김병철,Song, Jae-Bok,Kim, Byeong-Cheol 대한기계학회 1998 大韓機械學會論文集A Vol.22 No.2
A TCS slip control system improves acceleration capability and steerability on slippery roads through engine torgue and/or brake torque control. This research mainly deals with the engine control algorithm via the adjustment of the engine throttle angle. The following new control strategy is proposed and investigated ; the TCS slip controller whose input is the difference between the desired driving wheel speed corresponding to the optimum slip ratio and the actual speed yields the target engine torque and then estimates the throttle angle based on the engine performance curve. Various simulation and hardware-in-the-loop simulation have been carried out. The results show the proposed strategy may compensate for the inherent nonlinearity between variation of the throttle angle and variation of the engine torque and produce better performance than the previous strategies without the engine map, especially in the high speed region.
송재복(Jae-Bok Song),황서연(Seo-Yeon Hwang) 제어로봇시스템학회 2014 제어·로봇·시스템학회 논문지 Vol.20 No.3
This paper surveys past and state-of-the-art SLAM technologies. The standard methods for solving the SLAM problem are the Kalman filter, particle filter, graph, and bundle adjustment-based methods. Kalman filters such as EKF (Extended Kalman Filter) and UKF (Unscented Kalman Filter) have provided successful results for estimating the state of nonlinear systems and integrating various sensor information. However, traditional EKF-based methods suffer from the increase of computation burden as the number of features increases. To cope with this problem, particle filter-based SLAM approaches such as FastSLAM have been widely used. While particle filter-based methods can deal with a large number of features, the computation time still increases as the map grows. Graph-based SLAM methods have recently received considerable attention, and they can provide successful real-time SLAM results in large urban environments.