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- W4386004754 abstract "Based on the cooperation of neural network and extended state observer (ESO), in this paper, an approach for reinforcement control will be presented for Planar robot. By using the sliding surface as a state variable, the nominal system in quadratic form will be converted to first-order where the total uncertain component is estimated and remove by ESO. Then, a reinforcement algorithm will be added in collaboration to determine the nearly optimal solution of Hamilton-Jacobi-Bellman (HJB) equation. During the determination of the control signal, only one neural network is applied to reduce the computational complexity while still achieving the desired requirements. The simulation results of the algorithm will be examined on the Planar Robot with two degrees of freedom, thereby confirming the effectiveness of proposed control strategy." @default.
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- W4386004754 date "2023-01-01" @default.
- W4386004754 modified "2023-10-12" @default.
- W4386004754 title "Reinforcement Control for Planar Robot Based on Neural Network and Extended State Observer" @default.
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- W4386004754 doi "https://doi.org/10.1007/978-981-99-4725-6_62" @default.
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