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|Title:||An analytical adaptive single-neuron compensation control law for nonlinear process|
|Citation:||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)|
|Appears in Collections:||Staff Publications|
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