Dynamic system identification via recurrent multilayer perceptrons
Information Sciences—Informatics and Computer Science: An International Journal
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Neural Networks - 2003 Special issue: Advances in neural networks research — IJCNN'03
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Neural Computation
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Applied Soft Computing
Adaptive Tracking Control for the Output PDFs Based on Dynamic Neural Networks
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IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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IEEE Transactions on Neural Networks
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SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
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Automatica (Journal of IFAC)
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Automatica (Journal of IFAC)
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Journal of Control Science and Engineering - Special issue on Dynamic Neural Networks for Model-Free Control and Identification
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In this paper the adaptive nonlinear identification and trajectory tracking are discussed via dynamic neural networks. By means of a Lyapunov-like analysis we determine stability conditions for the identification error. Then we analyze the trajectory tracking error by a local optimal controller. An algebraic Riccati equation and a differential one are used for the identification and the tracking error analysis. As our main original contributions, we establish two theorems: the first one gives a bound for the identification error, and the second one establishes a bound for the tracking error. We illustrate the effectiveness of these results by two examples: the second-order relay system with multiple isolated equilibrium points and the chaotic system given by Duffing equation