Paper Info
Title
Adaptive NN control for a class of chemical reactor systems
Abstract
An adaptive control algorithm is applied to controlling a class of SISO continuous stirred tank reactor (CSTR) system in discrete-time. The considered systems belong to pure-feedback form where the unknown dead-zone and it is first to control this class of systems. Radial basis function neural networks (RBFNN) are used to approximate the unknown functions and the mean value theorem is exploited in the design. Based on the Lyapunov analysis method, it is proven that all the signals of the resulting closed-loop system are guaranteed to be semi-global uniformly ultimately bounded (SGUUB) and the tracking error can be reduced to a small compact set. A simulation example is studied to verify the effectiveness of the approach.
Year
DOI
Venue
2013
10.1007/978-3-642-39068-5_20
ISNN (2)
Keywords
Field
DocType
mean value theorem,lyapunov analysis method,unknown dead-zone,small compact set,radial basis function neural,adaptive control algorithm,chemical reactor system,adaptive nn control,simulation example,unknown function,closed-loop system,tank reactor,nonlinear systems
Lyapunov function,Continuous stirred-tank reactor,Nonlinear system,Control theory,Computer science,Compact space,Chemical reactor,Mean value theorem,Bounded function,Tracking error
Conference
Citations 
PageRank 
References 
0
0.34
10
Authors
2
Name
Order
Citations
PageRank
Dong-Juan Li187326.00
Li Tang2858.78