2
technique
example
previous work
uncertain systems
2017-04-19
213-225
classical BP
uncertain nonlinear systems
robust neural control
control strategy
single-output discrete-time nonlinear systems
parametric uncertainties
BP
learning
https://scigraph.springernature.com/explorer/license/
neural control
first step
discrete-time nonlinear systems
combination
2017-04-19
system
work
important properties
articles
discrete-time uncertain nonlinear systems
controller
scheme
nonlinear systems
properties
model
effectiveness
specialized learning technique
algorithm
false
simulation example
neural model
robustness
Robust Neural Control of Discrete Time Uncertain Nonlinear Systems Using Sliding Mode Backpropagation Training Algorithm
backpropagation training algorithm
robust control
inverse neural models
backpropagation algorithm
https://doi.org/10.1007/s11633-017-1062-2
article
behavior
step
uncertainty
speedy learning
best configuration
2022-10-01T06:42
class
configuration
training algorithm
neural control strategy
strategies
control scheme
learning techniques
This work deals with robust inverse neural control strategy for a class of single-input single-output (SISO) discrete-time nonlinear system affected by parametric uncertainties. According to the control scheme, in the first step, a direct neural model (DNM) is used to learn the behavior of the system, then, an inverse neural model (INM) is synthesized using a specialized learning technique and cascaded to the uncertain system as a controller. In previous works, the neural models are trained classically by backpropagation (BP) algorithm. In this work, the sliding mode-backpropagation (SM-BP) algorithm, presenting some important properties such as robustness and speedy learning, is investigated. Moreover, four combinations using classical BP and SM-BP are tested to determine the best configuration for the robust control of uncertain nonlinear systems. Two simulation examples are treated to illustrate the effectiveness of the proposed control strategy.
control
Control & Energy Management Laboratory, National School of Sfax Engineers, University of Sfax, 3038, Sfax, Tunisia
Control & Energy Management Laboratory, National School of Sfax Engineers, University of Sfax, 3038, Sfax, Tunisia
pub.1084933419
dimensions_id
Applied Mathematics
2731-538X
2731-5398
Springer Nature
Machine Intelligence Research
Djemel
Mohamed
Zaidi
Imen
Mohamed
Chtourou
doi
10.1007/s11633-017-1062-2
Information and Computing Sciences
Springer Nature - SN SciGraph project
16
Artificial Intelligence and Image Processing
Mathematical Sciences