Evaluation of Three Nonlinear Control Methods to Reject the Constant Bounded Disturbance for Robotic Manipulators
Performance Analysis of a Neuro-PID Controller Applied to a Robot Manipulator
Designing of Adaptive Model-Free Controller Based on Output Error and Feedback Linearization: Abstract and Applied Analysis
A new model-free adaptive controller versus non-linear H∞ controller for levitation of an electromagnetic system
In this paper we aim to survey the performance of nonlinear H∞ method and a new proposed controller on a Magnetic Levitation model (Maglev). The proposed controller is a new Model Free Adaptive (MFA) designing approach using Adaptive-Fuzzy procedure based on Feedback Linearization. The main idea of new controller comprises two steps: first, by means of Feedback Linearization method, measured signal is taken to a specific level with error less than a defined value and second, proposed rules are applied to the system to keep error near zero. The major advantage of new controller is that there is no need for identification of the system dynamic and only output error is required.
A New Approach for Designing of Adaptive Model Free Controllers
In this paper a new approach on designing of Model Free Adaptive Controller
(MFAC) using adaptive-fuzzy procedure as a feedback linearization and some rules
depended on output error is introduced. Basic idea in this controller is based on transferring
control signal to an appropriate surface and then by using of some rules, depends on the
output error of system, control signal changes around of this surface. This controller is
applied on three systems that each of them has some different nonlinear factors.