Adaptive Control Using a Neural Network Estimator and Dynamic Inversion

Dynamic modeling and control of a 4 DOF robotic finger using adaptive-robust and adaptive-neural controllers

In this research, first, kinematic and dynamic equations of a 4-DOF 3-link robotic finger are derived using Denavit-Hartenberg convention and Lagrange’s formulation. To model the muscles, several springs and dampers are placed between the finger links. Then, two advanced controllers, namely adaptive-robust and adaptive-neural, which can control the robotic finger in presence of parametric uncer...

scour modeling piles of kambuzia industrial city bridge using hec-ras and artificial neural network

today, scouring is one of the important topics in the river and coastal engineering so that the most destruction in the bridges is occurred due to this phenomenon. whereas the bridges are assumed as the most important connecting structures in the communications roads in the country and their importance is doubled while floodwater, thus exact design and maintenance thereof is very crucial. f...

dynamic modeling and control of a 4 dof robotic finger using adaptive-robust and adaptive-neural controllers

in this research, first, kinematic and dynamic equations of a 4-dof 3-link robotic finger are derived using denavit-hartenberg convention and lagrange’s formulation. to model the muscles, several springs and dampers are placed between the finger links. then, two advanced controllers, namely adaptive-robust and adaptive-neural, which can control the robotic finger in presence of parametric uncer...

Modeling and Control of Gas Turbine Combustor with Dynamic and Adaptive Neural Networks (TECHNICAL NOTE)

Neural Feedback Linearization Adaptive Control for Affine Nonlinear Systems Based on Neural Network Estimator

In this work, we introduce an adaptive neural network controller for a class of nonlinear systems. The approach uses two Radial Basis Functions, RBF networks. The first RBF network is used to approximate the ideal control law which cannot be implemented since the dynamics of the system are unknown. The second RBF network is used for on-line estimating the control gain which is a nonlinear and u...