• Title/Summary/Keyword: FBFN (Fuzzy Basis Function Network)

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Robust Adaptive Fuzzy Tracking Control Using a FBFN for a Mobile Robot with Actuator Dynamics (구동기 동역학을 가지는 이동 로봇에 대한 FBFN을 이용한 강인 적응 퍼지 추종 제어)

  • Shin, Jin-Ho;Kim, Won-Ho;Lee, Moon-Noh
    • Journal of Institute of Control, Robotics and Systems
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    • v.16 no.4
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    • pp.319-328
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    • 2010
  • This paper proposes a robust adaptive fuzzy tracking control scheme for a nonholonomic mobile robot with external disturbances as well as parameter uncertainties in the robot kinematics, the robot dynamics, and the actuator dynamics. In modeling a mobile robot, the actuator dynamics is integrated with the robot kinematics and dynamics so that the actuator input voltages are the control inputs. The presented controller is designed based on a FBFN (Fuzzy Basis Function Network) to approximate an unknown nonlinear dynamic function with the uncertainties, and a robust adaptive input to overcome the uncertainties. When the controller is designed, the different parameters for two actuator models in the actuator dynamics are taken into account. The proposed control scheme does not require the kinematic and dynamic parameters of the robot and actuators accurately. It can also alleviate the input chattering and overcome the unknown friction force. The stability of the closed-loop control system including the kinematic control system is guaranteed by using the Lyapunov stability theory and the presented adaptive laws. The validity and robustness of the proposed control scheme are shown through a computer simulation.

A Study on an Adaptive Robust Fuzzy Controller with GAs for Path Tracking of a Wheeled Mobile Robot

  • Nguyen, Hoang-Giap;Kim, Won-Ho;Shin, Jin-Ho
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.10 no.1
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    • pp.12-18
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    • 2010
  • This paper proposes an adaptive robust fuzzy control scheme for path tracking of a wheeled mobile robot with uncertainties. The robot dynamics including the actuator dynamics is considered in this work. The presented controller is composed of a fuzzy basis function network (FBFN) to approximate an unknown nonlinear function of the robot complete dynamics, an adaptive robust input to overcome the uncertainties, and a stabilizing control input. Genetic algorithms are employed to optimize the fuzzy rules of FBFN. The stability and the convergence of the tracking errors are guaranteed using the Lyapunov stability theory. When the controller is designed, the different parameters for two actuator models in the dynamic equation are taken into account. The proposed control scheme does not require the accurate parameter values for the actuator parameters as well as the robot parameters. The validity and robustness of the proposed control scheme are demonstrated through computer simulations.