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• W4385236098 abstract "In this paper, we propose a novel neural network based state constrained integral sliding mode (NN-SCISM) control algorithm for nonlinear system with partially unknown dynamics in presence of time-varying constraints. In particular, the drift term characterizing the system dynamics is estimated by using a two-layer neural network, whose weights are adjusted according to adaptation laws designed relying on stability analysis. Thanks to a sliding variable which varies depending on the minimum distance between the system state and the current closest constraint, the control algorithm is able to drive the system state to a desired target state, while avoiding the forbidden states contained in the time-varying set delimited by the constraints. The proposal has been theoretical analysed and assessed in simulation." @default.
• W4385236098 created "2023-07-26" @default.
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• W4385236098 date "2023-06-26" @default.
• W4385236098 modified "2023-09-25" @default.
• W4385236098 title "Neural network based integral sliding mode control of systems with time-varying state constraints" @default.
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• W4385236098 doi "https://doi.org/10.1109/med59994.2023.10185699" @default.
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