Multiple Model Adaptive controller for Partially-Observed Boolean Dynamical Systems

2017 ◽  
Author(s):  
Mahdi Imani ◽  
Ulisses Braga-Neto
Keyword(s):  
Dynamical Systems ◽  
Adaptive Controller ◽  
Multiple Model ◽  
Partially Observed

Development and Simulation of an Adaptive Control System for the Teleoperation of Medical Robots

2013 ◽  
pp. 173-185 ◽  
Author(s):  
Vu Trieu Minh
Keyword(s):  
Neural Network ◽  
Control System ◽  
Remote Control ◽  
Low Cost ◽  
Adaptive Controller ◽  
Medical Doctor ◽  
Multiple Model ◽  
Adaptive Control System ◽  
The Stability ◽  
Master Device

This chapter presents the design and calculation procedure for a teleoperation and remote control of a medical robot that can help a doctor to use his hands/fingers to examine patients in remote areas. This teleoperation system is simple and low cost, connected to the global Internet system, and through the interaction with the master device, the medical doctor is able to communicate control signals for the slave device. This controller is robust to the time-variant delays and the environment uncertainties while assuring the stability and the high transparent performance. A novel theoretical framework and algorithms are developed with time forward observer-based adaptive controller and neural network-based multiple model. The system allows the medical doctor to feel the real sense of the remote environments.

scholarly journals Adaptive Control for a Class of Nonlinear System with Redistributed Models

2012 ◽  
Vol 2012 ◽  
pp. 1-6 ◽  
Author(s):  
Haisen Ke ◽  
Jiang Li
Keyword(s):  
Adaptive Control ◽  
Nonlinear System ◽  
Adaptive Controller ◽  
Multiple Models ◽  
Multiple Model ◽  
Transient Performance ◽  
Feedback Form ◽  
System A ◽  
Multiple Model Adaptive Control ◽  
The Stability

Multiple model adaptive control has been investigated extensively during the last ten years in which the “switching” or “switching and tuning” have emerged as the mainly approaches. It is the “switching” that can improve the transient performance to some extent and also make it difficult to analyze the stability of the system with multiple models adaptive controller. Towards this goal, this paper develops a novel multiple models adaptive controller for a class of nonlinear system in parameter-strict-feedback form which not only improves the transient performance significantly, but also guarantees the stability of all the states of the closed-loop system. A simulation example is proposed to illustrate the effectiveness of the developed multiple models adaptive controller.

Comparative experiments with a multiple model based adaptive controller for a SCARA type direct drive manipulator

Robotica ◽  
2005 ◽  
Vol 23 (6) ◽  
pp. 721-729 ◽  
Author(s):  
M. Kemal Ciliz ◽  
M. Ömer Tuncay
Keyword(s):  
Adaptive Control ◽  
Nonlinear Effects ◽  
Adaptive Controller ◽  
Control Algorithms ◽  
Robot Arm ◽  
Multiple Model ◽  
Direct Drive ◽  
Test Bed ◽  
Efficient Test ◽  
Control Scheme

In this paper, different adaptive control algorithms will be experimentally tested on a two axis SCARA type direct drive robot arm, and the performance of these algorithms will be compared. Being a direct drive system, the nonlinear effects, arising from the dynamics of the manipulator under high velocities, are directly reflected in the control of the manipulator. This makes the manipulator a more efficient test bed for testing the efficiency of the proposed adaptive schemes. In the experiments, we used fast trajectories rather than slow ones to observe how the proposed controllers compensate the dynamic nonlinear effects of manipulator dynamics. We will test some known adaptive control algorithms given in the literature along with our proposed adaptive control scheme which makes use of multiple models.

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