Abstract
The main problem of sliding mode controllers is that a whole knowledge system parameters is required to compute the equivalent control. Neural networks are used to compute the equivalent control. Standard two layer feedforward neural network training with the backpropagation algorithm and Radial Basis Function Neural Networks (RBFNN) are the most popular methods that used on robot control. This paper applies these structures to Fuzzy Sliding Mode Control (FSMC). Methods are tested for robot trajectory tracking with computer simulations. Computer simulations of three link robot manipulator show that RBFNN is more efficient on FSMC for trajectory control applications.
Keywords
Trajectory
Backpropagation
Artificial neural network
Control theory (sociology)
Computer science
Feed forward
Controller (irrigation)
Sliding mode control
Robot
Fuzzy logic
Fuzzy control system
Feedforward neural network
Tracking (education)
Radial basis function
Artificial intelligence
Control engineering
Control (management)
Engineering
Nonlinear system
Subject Areas
Adaptive Control of Nonlinear Systems
·Control and Systems Engineering ·Physical Sciences
Hydraulic and Pneumatic Systems
·Mechanical Engineering ·Physical Sciences
Advanced Control Systems Design
·Control and Systems Engineering ·Physical Sciences
Citations by Year
2017: 1
2023: 1
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