Robotics: control, sensing, vision, and intelligence
Robotics: control, sensing, vision, and intelligence
Structure identification of fuzzy model
Fuzzy Sets and Systems
Neural networks for control
Numerical analysis and graphic visualization with MATLAB
Numerical analysis and graphic visualization with MATLAB
Soft computing for control of non-linear dynamical systems
Soft computing for control of non-linear dynamical systems
Neural Systems for Control
Adaptive Neural Network Control of Robotic Manipulators
Adaptive Neural Network Control of Robotic Manipulators
On-line system identification of complex systems using Chebyshev neural networks
Applied Soft Computing
Observer-based adaptive control of robot manipulators: Fuzzy systems approach
Applied Soft Computing
A direct adaptive neural command controller design for an unstable helicopter
Engineering Applications of Artificial Intelligence
Adaptive stick-slip friction and backlash compensation using dynamic fuzzy logic system
Applied Soft Computing
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We describe in this paper a new method for adaptive model-based control of robotic dynamic systems using a new hybrid fuzzy-neural approach. Intelligent control of robotic systems is a difficult problem because the dynamics of these systems is highly nonlinear. We describe an intelligent system for controlling robot manipulators to illustrate our fuzzy-neural hybrid approach for adaptive control. We use a new fuzzy inference system for reasoning with multiple differential equations for model selection based on the relevant parameters for the problem. In this case, the fractal dimension of a time series of measured values of the variables is used as a selection parameter. We use neural networks for identification and control of robotic dynamic systems. We also compare our hybrid fuzzy-neural approach with conventional fuzzy control to show the advantages of the proposed method for control.