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• W3169738366 abstract "The adaptive learning and control are proposed for the full-state(FS) constrained NWMR system with external destabilization. First, the constrained state is reformulated as the unconstrained state. Then, approximating the unknown dynamics in the closed-loop (CL) system is conducted via radial basis function (RBF) NN. Also, a sliding term is designed to deal with the external destabilization and the neural network training error. The derived adaptive neural controller can realize the asymptotic stability of a robot system without violating FS constraints. Moreover, the neural weights are converged so that the unknown dynamics are expressed by the constant weights in the CL system. It is also applicable to other similar control tasks. Lastly, the proposed algorithm is simulated and validated." @default.
• W3169738366 created "2021-06-22" @default.
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• W3169738366 date "2022-06-01" @default.
• W3169738366 modified "2023-10-18" @default.
• W3169738366 title "Full-state constrained neural control and learning for the nonholonomic wheeled mobile robot with unknown dynamics" @default.
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• W3169738366 doi "https://doi.org/10.1016/j.isatra.2021.06.012" @default.
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