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- W2362259421 abstract "Stability and optimality analysis of neural network based nonlinear control has presented great difficulties. In order to overcome the conservation of the usual robust control schemes, for a class of uncertain nonlinear systems that can represent many engineering systems, linear parameter neural network was employed to approximate on line the unknown nonlinearity. The network's approximation error was treated as a disturbance and was suppressed by robust control. The control law and the adaptive law of neural network weights were designed based on Lyapunov method. It is shown that the proposed control is continuous, guarantees global stability, and ensures a finite upper bound on the attenuation performance index. That is, the proposed control is both robust and optimal. Simulation results for a two freedom degree mechnical manipulator illustrate that the robustness and excellent tracking performance of the proposed scheme." @default.
- W2362259421 created "2016-06-24" @default.
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- W2362259421 date "1999-01-01" @default.
- W2362259421 modified "2023-09-24" @default.
- W2362259421 title "Stable neural network based adaptive control for a class of uncertain nonlinear systems" @default.
- W2362259421 hasPublicationYear "1999" @default.
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