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• W2148070153 abstract "In the face of large-scale parametric uncertainties, the single-model (SM)-based sliding mode control (SMC) approach demands high gains for the observer, controller, and adaptation to achieve satisfactory tracking performance. The main practical problem of having high-gain-based design is that it amplifies the input and output disturbance as well as excites hidden unmodeled dynamics, causing poor tracking performance. In this paper, a multiple model/control-based SMC technique is proposed to reduce the level of parametric uncertainty to reduce observer-controller gains. To this end, we split uniformly the compact set of unknown parameters into a finite number of smaller compact subsets. Then, we design a candidate SMC corresponding to each of these smaller subsets. The derivative of the Lyapunov function candidate is used as a resetting criterion to identify a candidate model that approximates closely the plant at each instant of time. The key idea is to allow the parameter estimate of conventional adaptive sliding mode control design to be reset into a model that best estimates the plant among a finite set of candidate models. The proposed method is evaluated on a 2-DOF robot manipulator to demonstrate the effectiveness of the theoretical development." @default.
• W2148070153 created "2016-06-24" @default.
• W2148070153 creator A5029712431 @default.
• W2148070153 creator A5057742267 @default.
• W2148070153 date "2011-06-01" @default.
• W2148070153 modified "2023-10-06" @default.
• W2148070153 title "Robust Sliding Mode Control for Robot Manipulators" @default.
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• W2148070153 doi "https://doi.org/10.1109/tie.2010.2062472" @default.
• W2148070153 hasPublicationYear "2011" @default.
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