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RISS 인기검색어
- 검색키워드
- 저자 : Sami El Ferik (검색결과 2 건)
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Neuro-based Canonical Transformation of Port Controlled Hamiltonian Systems
Aminuddin Qureshi,Sami El Ferik,Frank L. Lewis 제어·로봇·시스템학회 2020 International Journal of Control, Automation, and Vol.18 No.12
In the literature of control theory, tracking control of port controlled Hamiltonian systems is generally achieved using canonical transformation. Closed form evaluation of state-feedback for the canonical transformation requires the solution of certain partial differential equations which becomes very difficult for nonlinear systems. This paper presents the application of neural networks for the canonical transformation of port controlled Hamiltonian systems. Instead of solving the partial differential equations, neural networks are used to approximate the closedform state-feedback required for canonical transformation. Ultimate boundedness of the tracking and neural network weight errors is guaranteed. The proposed approach is structure preserving. The application of neural networks is direct and off-line processing of neural networks is not needed. Efficacy of the proposed approach is demonstrated with the examples of a mass-spring system, a two-link robot arm and an Autonomous Underwater Vehicle (AUV).
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Adaptive Control of Autonomous Bicycle Kinematics
Omar Al-Buraiki,Sami El Ferik 제어로봇시스템학회 2013 제어로봇시스템학회 국제학술대회 논문집 Vol.2013 No.10
In this paper , an adaptive Self-Tuning Regulator is proposed to control an autonomous bicycle kinematics. The model used for describing the dynamics of the bicycle is Whipple’s bicycle model. The roll (lean) and the steer angle of the bicycle the two outputs of the model and the torques across the roll and steer angle as the two control variables. The identification technique used in here is recursive least square identification and the proposed control strategy is state feed back control with the controller gain calculated using linear quadratic regulator. The autonomous bicycle was tested for varying velocities and it was observed that the adaptive controller gives a good control action of the bicycle model not just within the stable velocity region but outside it as well.
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