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RISS 인기검색어
- 검색키워드
- 저자 : Aminuddin Qureshi (검색결과 2 건)
- 무료
- 기관 내 무료
- 유료
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Decentralized Discrete-Time Quasi-Sliding Mode Control of Uncertain Linear Interconnected Systems
Aminuddin Qureshi,Mohamed Ali Abido 제어·로봇·시스템학회 2014 International Journal of Control, Automation, and Vol.12 No.2
In this paper, a new global decentralized discrete-time quasi-sliding mode control of linear interconnected systems is presented. The proposed controller is free of chattering problem and can be applied to a broader class of large-scale systems. Additionally, it is capable to deal with both known and unknown interconnections among the subsystems. Stability of the reduced-order interconnected systems is analyzed using Lyapunov approach. The proposed decentralized controller guarantees the reachability of the connective sliding manifold. The resultant dynamics are proved to be globally as-ymptotically stable. Furthermore, the controller is made robust to external disturbances by employing a disturbance estimation scheme. The simulations are performed on model of a two-area power generation system and the results show the efficacy of the proposed scheme.
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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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