Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-540-87442-3_104
Title: An analytical adaptive single-neuron compensation control law for nonlinear process
Authors: Jia, L.
Tao, P.-Y.
Chen, G.-B.
Chiu, M.-S. 
Keywords: Analytical
Compensator
Composite model
Single-neuron
Issue Date: 2008
Source: Jia, L.,Tao, P.-Y.,Chen, G.-B.,Chiu, M.-S. (2008). An analytical adaptive single-neuron compensation control law for nonlinear process. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 5226 LNCS : 850-857. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-540-87442-3_104
Abstract: To circumvent the drawbacks in nonlinear controller designing of chemical processes, an analytical adaptive single-neuron compensation control scheme is proposed in this paper. A class of nonlinear processes with modest nonlinearities is approximated by a composite model consisting a linear ARX model and a fuzzy neural network-based linearization error model. Motivated by the conventional feedforward control design technique in process industries, the output of FNNM can be viewed as measurable disturbance and a compensator can be designed to eliminate the disturbance influence. Simulation results show that the adaptive single-neuron compensation control plays a major role in improving the control performance, and the proposed adaptive control possesses better performance. © 2008 Springer-Verlag Berlin Heidelberg.
Source Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
URI: http://scholarbank.nus.edu.sg/handle/10635/74479
ISBN: 3540874402
ISSN: 03029743
DOI: 10.1007/978-3-540-87442-3_104
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