• International Journal of Control, Automation, and Systems
• /
• v.3 no.2
• /
• pp.217-224
• /
• 2005

Generally, the underwater flight vehicle (UFV) depth control system operates with the following problems: it is a multi-input multi-output (MIMO) system because the UFV contains both pitch and depth angle variables as well as multiple control planes, it requires robustness because of the possibility that it may encounter uncertainties such as parameter variations and disturbances, it requires a continuous control input because the system that has reduced power consumption and acoustic noise is more practical, and further, it has the speed dependency of controller parameters because the control forces of control planes depend on the operating speed. To solve these problems, an adaptive fuzzy sliding mode controller (AFSMC), which is based on the decomposition method using expert knowledge in the UFV depth control and utilizes a fuzzy basis function expansion (FBFE) and a proportional integral augmented sliding signal, is proposed. To verify the performance of the AFSMC, UFV depth control is performed. Simulation results show that the AFSMC solves all problems experienced in the UFV depth control system online.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.18 no.4
• /
• pp.482-487
• /
• 2008

In case of flooding, the underwater flight vehicle (UFV) executes the blowing by blowing ballast tanks off using high pressure air (HPA), while it also uses control planes and a propulsion unit to reduce the overshoot depth caused by a flooding and blowing sequence. However, the conventional whole HPA blow-off method lets the body on the surface after blowing despite slight flooding. This results in the unnecessary mission failure or body exposure. Therefore, it is necessary to keep the body at the near surface by the blowing control while reducing the overshoot depth. To solve this problem, an adaptive blowing control algorithm, which is based on the decomposition method expanding the expert knowledge in depth control and the adaptive method using fuzzy basis function expansion (FBFE), is proposed. To verify the performance of the proposed algorithm, the blowing control of UFV is performed. Simulation results show that the proposed algorithm effectively solves the problems in the UFV blowing control system online.

• 제어로봇시스템학회:학술대회논문집
• /
• 1996.10b
• /
• pp.1253-1256
• /
• 1996

In this paper, a new nonlinear feedback linearization control scheme for induction motors is developed. The control scheme employs a fuzzy nonlinear identification scheme based on fuzzy basis function expansion to adoptively compensate the parameter variations, i.e. rotor resistance, mutual and self inductance etc. An important feature of the proposed control scheme is to incorporate the sliding mode controller into the scheme to speed up convergence rate. Simulation tests show the robust behavior of the proposed controller in the presence of the parameter uncertainties of the machine.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.11 no.4
• /
• pp.359-365
• /
• 2001

In this paper, we propose the design methodology of target tracking system using fuzzy basis function expansion(FBFE) based on virus evolutionary genetic algorithm (VEGA). In general, the objective of target tracking is to estimate the future trajectory of the target based on the past position of the target obtained from the sensor. In the conventional and mathematical nonlinear filtering method such as extended Kalman filter(EKF), the performance of the system may be deteriorated in highly nonlinear situation. To resolve these problems of nonlinear filtering technique, by appling artificial intelligent technique to the tracking control of moving targets, we combine the advantages of both traditional and intelligent control technique. In the proposed method, after composing training datum from the parameters of extended Kalman filter, by combining FDFE, which has the strong ability for the approximation, with VEGA, which prevent GA from converging prematurely in the case of lack of genetic diversity of population, and by idenLifying the parameters and rule numbers of fuzzy basis function simultaneously, we can reduce the tracking error of EKF. Finally, the proposed method is applied to three dimensional tracking problem, and the simulation results shows that the tracking performance is improved by the proposed method.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2001.05a
• /
• pp.126-130
• /
• 2001

In this paper, we propose the design methodology of target tracking system using fuzzy basis function expansion (FBFE) based on virus evolutionary genetic algorithm(VEGA). In general, the objective of target tracking is to estimate the future trajectory of the target based on the past position of the target obtained from the sensor. In the conventional and mathematical nonlinear filtering method such as extended Kalman filter (EKF), the performance of the system may be deteriorated in highly nonlinear situation. To resolve these problems of nonlinear filtering technique, by appling artificial intelligent technique to the tracking control of moving targets, we combine the advantages of both traditional and intelligent control technique. In the proposed method, after composing training datum from the parameters of extended Kalman filter, by combining FBFE, which has the strong ability for the approximation, with VEGA, which prevent GA from converging prematurely in the case of lack of genetic diversity of population, and by identifying the parameters and rule numbers of fuzzy basis function simultaneously, we can reduce the tracking error of EKF. Finally, the proposed method is applied to three dimensional tracking problem, and the simulation results shows that the tracking performance is improved by the proposed method.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2008.04a
• /
• pp.161-162
• /
• 2008

침수의 경우에, 수중 비행체(UFV : Underwater Flight Vehicle)는 발라스트 탱크들의 내부를 고압 공기로 비워 내어 부상을 수행한다. 그런데, 기존의 blow-off 방법은 가벼운 침수일지라도 부상 후에는 몸체를 수면에 드러나게 한다. 이는 불필요한 임무 실패 또는 몸체 노출의 결과를 가져온다. 따라서, 부상 제어에 의해 침수 및 부상에 의한 오버슈트 심도를 감소시킴과 동시에 몸체를 수면 근처에 유지시키는 것이 필요하다. 이 문제를 해결하기 위해서 전문가 지식 및 FBFE(Fuzzy Basis Function Expansion)를 사용하는 부상 제어 알고리즘이 제안되었다. 제안된 알고리즘의 성능 검증을 위한 시뮬레이션 결과는 제안된 알고리즘이 UFV 부상 제어 시스템에 존재하는 문제점들을 효과적으로 해결하고 있음을 보여준다.

• Proceedings of the KIEE Conference
• /
• 1999.07b
• /
• pp.525-527
• /
• 1999

In this paper, we propose the target tracking system using fuzzy basis function expansion (FBFN) based on genetic algorithm (GA). In general, the objective of target tracking is to predict the future trajectory of the target based on the past position of the target obtained from the sensor. In the conventional and mathematical method, the parameter uncertainty and the environmental noise may deteriorate the performance of the system. To resolve these problems, we apply artificial intelligent technique to the tracking control of moving targets. The proposed method combines the advantages of both traditional and intelligent technique. The result of numerical simulation shows the effectiveness of the proposed method.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.19 no.5
• /
• pp.635-640
• /
• 2009

In real system application, the obstacle avoidance system for the autonomous control of the underwater flight vehicle (UFV) operates with the following problems: it has local information because the sonar can only offer the obstacle information in a local detection area, it requires a continuous control input because the system that has reduced acoustic noise and power consumption is necessary, and further, it requires an easy design procedure in terms of its structures and parameters. To solve these problems, an intelligent obstacle avoidance algorithm using the evolution strategy (ES) and the fuzzy logic controller (FLC), is proposed. To verify the performance of the proposed algorithm, the obstacle avoidance of UFV is performed. Simulation results show that the proposed algorithm effectively solves the problems in the real system application.