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    題名: A discrete-time multivariable neuro-adaptive control for nonlinear unknown dynamic systems
    作者: 黃志良
    貢獻者: 淡江大學電機工程學系
    關鍵詞: Nonlinear control systems;Control systems;Nonlinear dynamical systems;Stability;Gain;Trajectory;Recurrent neural networks;Upper bound;Uncertainty;Function approximation
    日期: 2000-12
    上傳時間: 2011-10-15 00:51:03 (UTC+8)
    摘要: First, we assume that the controlled systems contain a nonlinear matrix gain before a linear discrete-time multivariable dynamic system. Then, a forward control based on a nominal system is employed to cancel the system nonlinear matrix gain and track the desired trajectory. A novel recurrent-neural-network (RNN) with a compensation of upper bound of its residue is applied to model the remained uncertainties in a compact subset /spl Omega/. The linearly parameterized connection weight for the function approximation error of the proposed network is also derived. An e-modification updating law with projection for weight matrix is employed to guarantee its boundedness and the stability of network without the requirement of persistent excitation. Then a discrete-time multivariable neuro-adaptive variable structure control is designed to improve the system performances. The semi-global (i.e., for a compact subset /spl Omega/) stability of the overall system is then verified by the Lyapunov stability theory. Finally, simulations are given to demonstrate the usefulness of the proposed controller.
    關聯: IEEE Transactions on Systems, Man and Cybern, Part B- Cybernetics 30(6), pp.865-877
    DOI: 10.1109/3477.891148
    顯示於類別:[電機工程學系暨研究所] 期刊論文

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