An Adaptive Controller with Guarantee of Better Conditioning of the Robot Manipulator Joint-Space Inertia Matrix

2019 ◽  
Author(s):  
Mariana de Paula Assis Fonseca ◽  
Bruno Vilhena Adorno ◽  
Philippe Fraisse
Keyword(s):  
Robot Manipulator ◽  
Adaptive Controller ◽  
Joint Space ◽  
Inertia Matrix

Pre-Impact Configuration Designing of a Robot Manipulator for Impact Minimization

2017 ◽  
Vol 9 (3) ◽  
Author(s):  
Jingchen Hu ◽  
Tianshu Wang
Keyword(s):  
Impact Force ◽  
Robot Manipulator ◽  
Analytical Formula ◽  
Joint Space ◽  
Collision Problem ◽  
Spatial Operator ◽  
Inertia Ellipsoid ◽  
The Impact ◽  
Maximum Minimum ◽  
Simple Analytical Formula

This paper studies the collision problem of a robot manipulator and presents a method to minimize the impact force by pre-impact configuration designing. First, a general dynamic model of a robot manipulator capturing a target is established by spatial operator algebra (SOA) and a simple analytical formula of the impact force is obtained. Compared with former models proposed in literatures, this model has simpler form, wider range of applications, O(n) computation complexity, and the system Jacobian matrix can be provided as a production of the configuration matrix and the joint matrix. Second, this work utilizes the impulse ellipsoid to analyze the influence of the pre-impact configuration and the impact direction on the impact force. To illustrate the inertia message of each body in the joint space, a new concept of inertia quasi-ellipsoid (IQE) is introduced. We find that the impulse ellipsoid is constituted of the inertia ellipsoids of the robot manipulator and the target, while each inertia ellipsoid is composed of a series of inertia quasi-ellipsoids. When all inertia quasi-ellipsoids exhibit maximum (minimum) coupling, the impulse ellipsoid should be the flattest (roundest). Finally, this paper provides the analytical expression of the impulse ellipsoid, and the eigenvalues and eigenvectors are used as measurements to illustrate the size and direction of the impulse ellipsoid. With this measurement, the desired pre-impact configuration and the impact direction with minimum impact force can be easily solved. The validity and efficiency of this method are verified by a PUMA robot and a spatial robot.

A FAT-based adaptive controller for robot manipulators without regressor matrix: theory and experiments

Robotica ◽  
2005 ◽  
Vol 24 (2) ◽  
pp. 205-210 ◽  
Author(s):  
An-Chyau Huang ◽  
Shi-Chang Wu ◽  
Wen-Fa Ting
Keyword(s):  
Function Approximation ◽  
Matrix Theory ◽  
Robot Manipulator ◽  
Nonlinear Differential Equations ◽  
Lyapunov Stability Theory ◽  
Adaptive Controller ◽  
Approximation Technique ◽  
Control Scheme ◽  
Approximation Errors ◽  
Theory And Experiments

In this paper, an adaptive control scheme is proposed for an n-link rigid robot manipulator without using the regressor. The robot is firstly modeled as a set of second-order nonlinear differential equations with the assumption that all of the matrices in that model are unavailable. Since these matrices are time-varying and their variation bounds are not given, traditional adaptive or robust designs do not apply. The function approximation technique (FAT) is used here to represent uncertainties in some finite linear combinations of orthonormal basis. The dynamics of the output tracking can thus be proved to be a stable first order filter driven by function approximation errors. Using the Lyapunov stability theory, a set of update laws is derived to give closed loop stability with proper tracking performance. Experiments are also performed on a 2-D robot to test the efficacy of the proposed scheme.

scholarly journals FRACTIONAL-ORDER PASSIVITY-BASED ADAPTIVE CONTROLLER FOR A ROBOT MANIPULATOR TYPE SCARA

Fractals ◽  
2020 ◽  
Vol 28 (08) ◽  
pp. 2040008
Author(s):  
J. E. LAVÍN-DELGADO ◽  
S. CHÁVEZ-VÁZQUEZ ◽  
J. F. GÓMEZ-AGUILAR ◽  
G. DELGADO-REYES ◽  
M. A. RUÍZ-JAIMES
Keyword(s):  
Fractional Order ◽  
Control Strategy ◽  
Control Method ◽  
Robot Manipulator ◽  
Adaptive Controller ◽  
Performance Comparison ◽  
External Disturbances ◽  
Scara Robot ◽  
Comparison Results ◽  
Lagrange Formalism

In this paper, a novel fractional-order control strategy for the SCARA robot is developed. The proposed control is composed of [Formula: see text] and a fractional-order passivity-based adaptive controller, based on the Caputo–Fabrizio and Atangana–Baleanu derivatives, respectively; both controls are robust to external disturbances and change in the desired trajectory and effectively enhance the performance of robot manipulator. The fractional-order dynamic model of the robot manipulator is obtained by using the Euler–Lagrange formalism, as well as the model of the induction motors which are the actuators that drive their joints. Through simulations results, the effectiveness and robustness of the proposed control strategy have been demonstrated. The performance of the fractional-order proposed control method is compared with its integer-order counterpart, composed of the PI controller and the conventional passivity-based adaptive controller, reported in the literature. The performance comparison results demonstrate the superiority and effectiveness of the fractional-order proposed control strategy for a SCARA robot manipulator.

scholarly journals PD Control Compensation Based on a Cascade Neural Network Applied to a Robot Manipulator

2020 ◽  
Vol 14 ◽  
Author(s):  
Luis Arturo Soriano ◽  
Erik Zamora ◽  
J. M. Vazquez-Nicolas ◽  
Gerardo Hernández ◽  
José Antonio Barraza Madrigal ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Robot Manipulator ◽  
Industrial Robot ◽  
Identification Problem ◽  
Adaptive Controller ◽  
Pd Control ◽  
Non Linear ◽  
Cascade Neural Network ◽  
Integral Gain

A Proportional Integral Derivative (PID) controller is commonly used to carry out tasks like position tracking in the industrial robot manipulator controller; however, over time, the PID integral gain generates degradation within the controller, which then produces reduced stability and bandwidth. A proportional derivative (PD) controller has been proposed to deal with the increase in integral gain but is limited if gravity is not compensated for. In practice, the dynamic system non-linearities frequently are unknown or hard to obtain. Adaptive controllers are online schemes that are used to deal with systems that present non-linear and uncertainties dynamics. Adaptive controller use measured data of system trajectory in order to learn and compensate the uncertainties and external disturbances. However, these techniques can adopt more efficient learning methods in order to improve their performance. In this work, a nominal control law is used to achieve a sub-optimal performance, and a scheme based on a cascade neural network is implemented to act as a non-linear compensation whose task is to improve upon the performance of the nominal controller. The main contributions of this work are neural compensation based on a cascade neural networks and the function to update the weights of neural network used. The algorithm is implemented using radial basis function neural networks and a recompense function that leads longer traces for an identification problem. A two-degree-of-freedom robot manipulator is proposed to validate the proposed scheme and compare it with conventional PD control compensation.

Adaptive backstepping control for parallel robot with uncertainties in dynamics and kinematics

Robotica ◽  
2014 ◽  
Vol 32 (6) ◽  
Author(s):  
Jing Zou ◽  
John K. Schueller
Keyword(s):  
Measurement Errors ◽  
Robot Manipulator ◽  
Kinematic Model ◽  
Parallel Robot ◽  
Joint Space ◽  
Backstepping Control ◽  
Adaptive Backstepping ◽  
Robot Tracking ◽  
Robot Systems ◽  
Backstepping Controller

SUMMARYIt is common in robot tracking control that controllers are designed based on the exact kinematic model of the robot manipulator. However, because of measurement errors and changes of states in practice, the original kinematic model is often no longer accurate and will degrade the control result. An adaptive backstepping controller is designed here for parallel robot systems with kinematics and dynamics uncertainties. Backstepping control is used to manage the transformation between the errors in task space and joint space. Adaptive control is utilized to compensate for uncertainties in both dynamics and kinematics. The controller demonstrated good performance in simulation.

GA Based Adaptive Controller for 2DOF Robot Manipulator

2014 ◽  
Vol 47 (1) ◽  
pp. 670-675 ◽  
Author(s):  
M Vijay ◽  
Debashisha Jena ◽  
Member IEEE
Keyword(s):  
Robot Manipulator ◽  
Adaptive Controller

scholarly journals Neural Network-Based Adaptive Control of Robotic Manipulator: Application to a Three Links Cylindrical Robot

2017 ◽  
Vol 13 (1) ◽  
pp. 114-122
Author(s):  
Abdul-Basset AL-Hussein
Keyword(s):  
Neural Network ◽  
Sliding Mode ◽  
Robot Manipulator ◽  
Tracking Error ◽  
Adaptive Controller ◽  
Robotic Manipulator ◽  
Lyapunov Theorem ◽  
External Disturbances ◽  
The Stability ◽  
Learning Law

A composite PD and sliding mode neural network (NN)-based adaptive controller, for robotic manipulator trajectory tracking, is presented in this paper. The designed neural networks are exploited to approximate the robotics dynamics nonlinearities, and compensate its effect and this will enhance the performance of the filtered error based PD and sliding mode controller. Lyapunov theorem has been used to prove the stability of the system and the tracking error boundedness. The augmented Lyapunov function is used to derive the NN weights learning law. To reduce the effect of breaching the NN learning law excitation condition due to external disturbances and measurement noise; a modified learning law is suggested based on e-modification algorithm. The controller effectiveness is demonstrated through computer simulation of cylindrical robot manipulator.

Sign in / Sign up

Export Citation Format

Share Document