Wonchang Lee

A fuzzy model-based controller of an underwater robotic vehicle under the influence of thruster dynamics

Underwater robotic vehicles (URVs) have become an important tool for various underwater tasks because they have greater speed, endurance, depth capability, and safety than human divers. Many URVs powered by electric rotors driving propellers. The thruster system is known to be nonlinear and time-varying. The system dynamics of URVs can be greatly influenced by the thruster of dynamics at low speed or station keeping. Good control of a vehicle at low speed is also an important design problem which must be solved to permit important operations like automatic docking and combined vehicle-manipulator control. The conventional linear controller based on the simplified vehicle dynamics may not be able to handle these properties and result in poor performance. This paper describes a fuzzy model-based controller of an underwater robotic vehicle with the influence of the thruster dynamics. The fuzzy controller presented in this paper is based on a Tagaki-Sugeno-Kang (TSK) fuzzy model and guarantees the stability of overall fuzzy control system. Its superiority to the conventional linear controller is investigated by computer simulation.

A tactics for robot soccer with fuzzy logic mediator

This paper presents a tactics using fuzzy logic mediator that selects proper robot action depending on the positions and the roles of adjacent two robots. Conventional Q-learning algorithm, where the number of states increases exponentially with the number of robots, is not suitable for a robot soccer system, because it needs so much calculation that processing cannot be accomplished in real time. A modular Q-learning algorithm reduces a number of states by partitioning the concerned area, where mediator algorithm for cooperation of robots is used additionally. The proposed scheme not only reduces a number of a calculation but also combines a robot action selection with robot cooperation by means of fuzzy logic system.

Design of fuzzy state controllers and observers

This paper suggests methods for designing fuzzy state controllers and fuzzy state observers based on Takagi-Sukeno-Kang (TSK) fuzzy models (Takagi and Sukeno, 1985; Sukeno and Kang, 1988) which approximate complicated nonlinear systems to any degree of accuracy. First, a designing method of fuzzy state controllers, guaranteeing that the poles of the fuzzy control systems with fuzzy state controllers are to be desired ones, is suggested. Next, a designing method of fuzzy state observers, guaranteeing that the dynamics of state error vector have the desired poles, is suggested. Finally, fuzzy control systems with fuzzy state controllers and observers are constructed and it is showed that the so-called separation property of the observer-controller design procedure is satisfied.<<ETX>>

Transformation of TSK fuzzy system into fuzzy system with singleton consequents and its application

A TSK fuzzy system can represent the behavior of a complex nonlinear system with a low number of rules with the desired accuracy and guarantee the stability of the closed loop system, while the interpretation of the rules is difficult due to the functional nature of the consequents. On the contrary, the fuzzy controller with singleton consequents is understandable intuitively and adjustable easily due to linguistic expression of the rules. The paper suggests a method transforming TSK fuzzy systems into fuzzy systems with singleton consequents, and shows its application designing a fuzzy controller with singleton consequents by using the TSK fuzzy system when the behavior of a nonlinear system is described with a singleton fuzzy model by a human expert.

Navigation of an Autonomous Mobile Robot with Vision and IR Sensors Using Fuzzy Rules

Algorithms of path planning and obstacle avoidance are essential to autonomous mobile robots that are working in unknown environments in the real time. This paper presents a new navigation algorithm for an autonomous mobile robot with vision and IR sensors using fuzzy rules. Temporary targets are set up by distance variation method and then the algorithms of trajectory planning and obstacle avoidance are designed using fuzzy rules. In this approach, several digital image processing technique is employed to detect edge of obstacles and the distances between the mobile robot and the obstacles are measured. An autonomous mobile robot with single vision and IR sensors is built up for experiments. We also show that the autonomous mobile robot with the proposed algorithm is navigating very well in complex unknown environments.

Adaptive Fuzzy Control of Helicopter

This paper presents an adaptive fuzzy control scheme for nonlinear helicopter system which has uncertainty or unknown variations in parameters. The proposed adaptive fuzzy controller is a model reference adaptive controller. The parameters of fuzzy controller are adjusted so that the plant output tracks the reference model output. It is shown that the adaptive law guarantees the stability of the closed-loop system by using Lyapunov function. Several experiments with a small model helicopter having parameter variations are performed to show the usefulness of the proposed adaptive fuzzy controller.

Adaptive PID Controller for Nonlinear Systems using Fuzzy Model

This paper presents an adaptive PID control scheme for nonlinear system. TSK(Takagi-Sugeno-Kang) fuzzy model is used to estimate the error of control input, and the parameters of PID controller are adapted using the error. The parameters of TSK fuzzy model also adapted to plant. The proposed algorithm allows designing adaptive PID controller which Is adapted to the uncertainty of nonlinear plant and the change of parameters. The usefulness of the proposed algorithm is also certificated by the several simulations.

Clustering Algorithm for Efficient Use of Energy in Wireless Sensor Network

In oder to operate sensor networks effectively it is very important to use the energy in the individual nodes efficiently and so increase their lifetime. Cluster-based routing algorithms such as LEACH and HEED obtain the efficiency of energy using data transfer between cluster heads and its members. In this paper we analyze the typical cluster-based routing algorithms and suggest a new energy efficient method of electing the cluster heads with the maximum delay of dead nodes occurrence. The efficiency of the proposed algorithm is verified through MATLAB simulation.

Odor Source Tracking of Mobile Robot with Vision and Odor Sensors

본 논문에서는 비전 시스템과 후각 센서를 이용하여 자율주행 이동로봇에 냄새 발생지 추적을 위한 기능을 구현하였다. 초기에 로봇에 부착된 후각 센서가 냄새를 탐지하지 못한 경우에는 비전 시스템을 이용하여 특정 지역 내를 운행을 하다가 시계 내의 한 물체에 접근하여 냄새를 방출하고 있는 지를 검사하게 된다. 만일 냄새를 방출하고 있다면 신경회로망을 이용한 냄새 구별 알고리즘을 이용하여 그 냄새가 찾고자 하는 것인지를 확인하게 된다. 실험을 위해 AMOR(Autonomous Mobile Olfactory Robot) 로봇을 구현하여 사용하였으며, 실험결과는 제안된 알고리즘이 냄새 발생지를 찾고 냄새를 구별해 내는데 효율성이 있음을 보여준다.

A Robot Soccer Strategy and Tactic Using Fuzzy Logic

This paper presents a strategy and tactic for robot soccer using furry logic mediator that determines robot action depending on the positions and the roles of adjacent two robots. Conventional Q-learning algorithm, where the number of states increases exponentially with the number of robots, is not suitable for a robot soccer system, because it needs so much calculation that processing cannot be accomplished in real time. A modular Q-teaming algorithm reduces a number of states by partitioning the concerned area, where mediator algorithm for cooperation of robots is used additionally. The proposed scheme implements the mediator algorithm among robots by fuzzy logic system, where simple fuzzy rules make the calculation easy and hence proper for robot soccer system. The simulation of MiroSot shows the feasibility of the proposed scheme.