NDLI: Direct versus indirect neural control based on radial basis function networks

Content Provider	IEEE Xplore Digital Library
Author	Alexandridis, A. Stogiannos, M. Loukidis, A. Ninos, K. Zervas, E. Sarimveis, H.
Copyright Year	2014
Description	Author affiliation: Sch. of Chem. Eng., Nat. Tech. Univ. of Athens, Athens, Greece (Sarimveis, H.) \|\| Dept. of Electron. Eng., Technol. Educ. Inst. of Athens, Athens, Greece (Alexandridis, A.; Stogiannos, M.; Loukidis, A.; Ninos, K.; Zervas, E.)
Abstract	This work presents a comparison between direct and indirect neural control methods based on the radial basis function (RBF) architecture. As far as direct control schemes are concerned, a novel direct inverse neural RBF controller taking into account the applicability domain criterion (INCAD) is utilized. ? model predictive control (MPC) formulation based on RBF networks is tested as an example of indirect method. The performances of the two control schemes are evaluated and compared on a highly nonlinear control problem, namely control of a continuous stirred tank reactor (CSTR) with multiple stable and unstable steady states. Results show that the INCAD controller is able to provide satisfactory performance, while performing almost instant calculation of the control actions. MPC on the other hand, outperforms the INCAD in terms of speed of responses, due to the built-in optimization capability; however, the lengthy procedure of solving online the optimization problem impedes the practical use of MPC on systems with fast dynamics.
Sponsorship	Uni.Essex
Starting Page	91
Ending Page	96
File Size	976436
Page Count	6
File Format	PDF
ISBN	9781479966929
DOI	10.1109/CEEC.2014.6958561
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-09-25
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Radial basis function Model predictive control Direct control Radial basis function networks Artificial neural networks Indirect control Predictive models Vectors Chemical reactors Neurocontrol Optimization
Content Type	Text
Resource Type	Article

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2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Mr. Siddhartha Mukherjee siddhartha@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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