Fuzzy adapting rate for a neural emulator of nonlinear systems: real application on a chemical process

Abstract

This paper deals with a new fuzzy adapting rate for a neural emulator of nonlinear systems with unknown dynamics. This method is based on an online intelligent adaptation by using a fuzzy supervisor. The satisfactory obtained simulation results are compared with those registered in the case of the classical choice of adapting rate and show very good emulation performances. An experimental validation of the proposed fuzzy adapting rate on a chemical reactor is also proposed to confirm the good performances in terms of speed of convergence and precision of representation.

Stability analysis of adaptive control using fuzzy adapting rate neural emulator: Experimental validation on a thermal process

“…So, to overcome this problem, the authors have proposed a method which is based on a fuzzy adapting rate for the neural emulator. 31 Then, a fuzzy supervisor is considered for the online adjustment of η e ( t ) (Figure 4).…”

“…In order to overcome the neural emulation problem, the authors proposed a fuzzy adapting rate for the neural emulator of nonlinear systems. 31 The basic advantage of this approach is to avoid the effort required for searching optimal choice of the neural emulator adapting rate. This type of fuzzy supervisor does not require any initialization parameter.…”

An improved indirect adaptive neural control performance based on MOPSO approach: An experimental validation via a transesterification reactor

“…However, to achieve good closed-loop performance, the NE and the NC starting parameters should be carefully selected which can remarkably increase the computation load. To overcome this problem, a fuzzy adapting rate NE of nonlinear systems has been proposed in the work by Rhili et al (2019). Using a fuzzy supervisor, the effort required for searching optimal choice of the NE adapting rate can be avoided.…”