Abstract
This paper presents an approach of cooperative control that is based on the concept of combining neural networks and the methodology of fuzzy sliding mode control (SMC). The aim of this study is to overcome some of the difficulties of conventional control methods such as controllers requires system dynamics in detailed. In the proposed control system, a neural network (NN) is developed to mimic the equivalent control law in the SMC. The structure of the NN that estimates the equivalent control is a standard two layer feed-forward NN with the backprobagation algorithm. The weights of the NN are updated such that the corrective control term of the SMC goes to zero.
Keywords
Control theory (sociology)
Sliding mode control
Artificial neural network
Computer science
Controller (irrigation)
Fuzzy control system
Fuzzy logic
Variable structure control
Mode (computer interface)
Robot
Control (management)
Control system
Control engineering
Neuro-fuzzy
Artificial intelligence
Engineering
Nonlinear system
Subject Areas
Adaptive Control of Nonlinear Systems
·Control and Systems Engineering ·Physical Sciences
Fuzzy Logic and Control Systems
·Artificial Intelligence ·Physical Sciences
Advanced Control Systems Design
·Control and Systems Engineering ·Physical Sciences
Citations by Year
2014: 1
2015: 1
2016: 1
2017: 1
2021: 1
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