Adaptive control of an overhead crane using dynamic feedback linearization and estimation design

2003 ◽  
Author(s):  
F. Boustany ◽  
B. d'Andrea-Novel
Keyword(s):  
Adaptive Control ◽  
Feedback Linearization ◽  
Overhead Crane ◽  
Dynamic Feedback

Object Manipulation Using Compliant Fingerpads: Modeling and Control

1993 ◽  
Vol 115 (4) ◽  
pp. 638-648 ◽  
Author(s):  
A. M. Annaswamy ◽  
D. Seto
Keyword(s):  
Adaptive Control ◽  
Feedback Linearization ◽  
Industrial Robots ◽  
Dynamic Interactions ◽  
Modeling And Control ◽  
Grasp Stability ◽  
Grasping And Manipulation ◽  
End Effectors ◽  
Geometric Techniques ◽  
The Stability

Current industrial robots are often required to perform tasks requiring mechanical interactions with their environment. For tasks that require grasping and manipulation of unknown objects, it is crucial for the robot end-effector to be compliant to increase grasp stability and manipulability. The dynamic interactions that occur between such compliant end-effectors and deformable objects that are being manipulated can be described by a class of nonlinear systems. In this paper, we determine algorithms for grasping and manipulation of these objects by using adaptive feedback techniques. Methods for control and adaptive control of the underlying nonlinear system are described. It is shown that although standard geometric techniques for exact feedback linearization techniques are inadequate, yet globally stable adaptive control algorithms can be determined by making use of the stability characteristics of the underlying nonlinear dynamics.

A Robust Approach to Dynamic Feedback Linearization for a Steerable Nips Mechanism

2008 ◽  
Author(s):  
Edgar I. Ergueta ◽  
Robert Seifried ◽  
Roberto Horowitz
Keyword(s):  
Control Strategy ◽  
Feedback Linearization ◽  
Position Control ◽  
Control Strategies ◽  
Successful Implementation ◽  
Experimental Setup ◽  
Dynamic Feedback ◽  
Robust Approach ◽  
Internal Loops ◽  
Steerable Nips

This paper presents two different control strategies for paper position control in printing devices. The first strategy is based on feedback linearization plus dynamic extension (dynamic feed-back linearization). Even though this controller is very simple to design, we show that it is not able to handle actuator multiplicative uncertainties, and therefore it fails when it is implemented on the experimental setup. The second strategy we present uses similar concepts, but it is more robust since feedback linearization is used only to linearize the kinematics of the system and internal loops are used to locally control the actuator’s positions and velocities. Not only do we prove the robustness of the second control strategy, but we also show its successful implementation.

Adaptive Feedback Linearization Control of SISO Nonlinear Processes Using a Self-Generating Neural Network-Based Approach

2011 ◽  
Vol 6 (1) ◽  
Author(s):  
Karim Salahshoor ◽  
Amin Sabet Kamalabady
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Adaptive Control ◽  
Feedback Linearization ◽  
Learning Algorithm ◽  
Rbf Neural Networks ◽  
Adaptive Feedback ◽  
Control Scheme ◽  
Feedback Linearization Control ◽  
Adaptive Control Scheme

This paper presents a new adaptive control scheme based on feedback linearization technique for single-input, single-output (SISO) processes with nonlinear time-varying dynamic characteristics. The proposed scheme utilizes a modified growing and pruning radial basis function (MGAP-RBF) neural network (NN) to adaptively identify two self-generating RBF neural networks for online realization of a well-known affine model structure. An extended Kalman filter (EKF) learning algorithm is developed for parameter adaptation of the MGAP-RBF neural networks. The MGAP-RBF growing and pruning criteria have been endeavored to enhance its performance for online dynamic model identification purposes. A stability analysis has been provided to ensure the asymptotic convergence of the proposed adaptive control scheme using Lyapunov criterion. Capabilities of the adaptive feedback linearization control scheme is evaluated on two nonlinear CSTR benchmark processes, demonstrating good performances for both set-point tracking and disturbance rejection objectives.

Sign in / Sign up

Export Citation Format

Share Document