Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/30827
Title: Enhancing the estimation of plant Jacobian for adaptive neural inverse control
Authors: Wang, D
Bao, P
Keywords: Adaptive inverse control
Neural networks
Nonlinear systems
On-line specialized learning
Plant Jacobian
Issue Date: 2000
Publisher: Elsevier
Source: Neurocomputing, 2000, v. 34, p. 99-115 How to cite?
Journal: Neurocomputing 
Abstract: To implement the specialized learning of the inverse dynamic neuro-controller for controlling nonlinear plants with noise, it is strongly desirable that an on-line trained neural plant emluator may provide a reasonably good estimation of the plant Jacobian under noise environments. This paper presents an approach for enhancing the estimation of the plant Jacobian which is on-line used in direct adaptive neural inverse control schemes. The estimated teaching signals are obtained by using the input-output data available at each time step, and then they are used to train the neural plant emluator by a new cost function introduced in this work for training the plant emluator. Convergence theorem for the adaptive back-propagation algorithm and stability of the closed-loop control system are established by using the Lyapunov theory. Simulations are conducted for demonstrating the effectiveness of the proposed strategy.
URI: http://hdl.handle.net/10397/30827
ISSN: 0925-2312
EISSN: 1872-8286
DOI: 10.1016/S0925-2312(00)00319-2
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