Velocity and Force Observers for the Control of Robot Manipulators

2013 ◽  
Vol 135 (6) ◽  
Author(s):  
Marco A. Arteaga–Pérez ◽  
Juan C. Rivera–Dueñas ◽  
Alejandro Gutiérrez–Giles
Keyword(s):  
Dynamic Model ◽  
Tracking Control ◽  
Inverse Kinematics ◽  
Robot Manipulator ◽  
Robot Manipulators ◽  
Asymptotical Stability ◽  
Joint Angles ◽  
Force Tracking

In this paper, position/force tracking control for rigid robot manipulators interacting with its environment is considered. It is assumed that only joint angles are available for feedback, so that velocity and force observers are designed. The principle of orthogonalization is employed for this particular purpose and some of its main properties are fully exploited to guarantee local asymptotical stability. Only the force observer requires the dynamic model of the robot manipulator for implementation, and the scheme is developed directly in workspace coordinates, so that no inverse kinematics is required. The proposed approach is tested experimentally and compared with a well–known algorithm.

Trajectory tracking control of robot manipulators using a neural-network-based torque compensator

1998 ◽  
Vol 212 (5) ◽  
pp. 361-372 ◽  
Author(s):  
Q Li ◽  
S K Tso ◽  
W J Zhang
Keyword(s):  
Neural Network ◽  
Trajectory Tracking ◽  
Tracking Control ◽  
High Performance ◽  
Robot Manipulator ◽  
Robot Manipulators ◽  
Lyapunov Stability Theory ◽  
System Stability ◽  
Trajectory Tracking Control ◽  
Modelling Uncertainties

In this paper, an adaptive neural-network-based torque compensator is developed for the trajectory-tracking control of robot manipulators. The overall control structure employs a classical non-linear decoupling controller for actuating torque computation based on an approximated robot dynamic model. To suppress the effects of uncertainties associated with the estimated model, a supplementary neural network algorithm is developed to generate compensation torques. The weight adaptation rule for this neuro-compensator is derived on the basis of the Lyapunov stability theory. Both global system stability and the error convergence can then be guaranteed. Simulation studies on a two-link robot manipulator demonstrate that high performance of the proposed control algorithm could be achieved under severe modelling uncertainties.

Control Design of Robotic Manipulator Based on Quantum Neural Network

2017 ◽  
Vol 140 (6) ◽  
Author(s):  
Hayder Mahdi Abdulridha ◽  
Zainab Abdullah Hassoun
Keyword(s):  
Neural Network ◽  
Dynamic Model ◽  
Pid Controller ◽  
Inverse Kinematics ◽  
Robot Manipulator ◽  
System Response ◽  
Proportional Integral Derivative ◽  
Dynamics Model ◽  
Lagrange Method ◽  
Quantum Neural Network

In this study, a control system was designed to control the robot's movement (The Mitsubishi RM-501 robot manipulator) based on the quantum neural network (QNN). A proposed method was used to solve the inverse kinematics in order to determine the angles values for the arm's joints when it follows through any path. The suggested method is the QNN algorithm. The forward kinematics was derived according to Devavit–Hartenberg representation. The dynamics model for the arm was modeled based on Lagrange method. The dynamic model is considered to be a very important step in the world of robots. In this study, two methods were used to improve the system response. In the first method, the dynamic model was used with the traditional proportional–integral–derivative (PID) controller to find its parameters (Kp, Ki, Kd) by using Ziegler Nichols method. In the second method, the PID parameters were selected depending on QNN without the need to a mathematical model of the robot manipulator. The results show a better response to the system when replacing the traditional PID controller with the suggested controller.

scholarly journals DYNAMIC MODEL WITH A NEW FORMULATION OF CORIOLIS/CENTRIFUGAL MATRIX FOR ROBOT MANIPULATORS

2020 ◽  
Vol 36 (1) ◽  
pp. 89-104
Author(s):  
Le Ngoc Truc ◽  
Nguyen Van Quyen ◽  
Nguyen Phung Quang
Keyword(s):  
Dynamic Model ◽  
Robot Manipulator ◽  
Robot Manipulators ◽  
Dynamic Equations ◽  
Lagrange Equations ◽  
Matrix Formulation ◽  
The Matrix ◽  
Angular Velocities ◽  
Set Up ◽  
New Formulation

The paper presents a complete generalized procedure based on the Euler-Lagrange equations to build the matrix form of dynamic equations, called dynamic model, for robot manipulators. In addition, a new formulation of the Coriolis/centrifugal matrix is proposed. The link linear and angular velocities are formulated explicitly. Therefore, the translational and rotational Jacobian matrices can be derived straightforward from definition, which makes the calculation of the generalized inertia matrix more convenient. By using Kronecker product, a new Coriolis/centrifugal matrix formulation is set up directly in matrix-based manner and guarantees the skew symmetry property of robot dynamic equations. This important property is usually exploited for developing many control methodologies. The validation of the proposal formulation is confirmed through the symbolic solution and simulation of a typical robot manipulator.

scholarly journals Modified Linear Active Disturbance Rejection Control for Uncertain Robot Manipulator Trajectory Tracking

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Hongjun Hu ◽  
Shungen Xiao ◽  
Haikuo Shen
Keyword(s):  
Trajectory Tracking ◽  
Disturbance Rejection ◽  
Tracking Control ◽  
Feedforward Control ◽  
Robot Manipulator ◽  
Robot Manipulators ◽  
External Disturbance ◽  
Angular Acceleration ◽  
Trajectory Tracking Control ◽  
Active Disturbance Rejection

To solve the problems of model uncertainties, dynamic coupling, and external disturbances, a modified linear active disturbance rejection controller (MLADRC) is proposed for the trajectory tracking control of robot manipulators. In the computer simulation, MLADRC is compared to the proportional-derivative (PD) controller and the regular linear active disturbance rejection controller (LADRC) for performance tests. Multiple uncertain factors such as friction, parameter perturbation, and external disturbance are sequentially added to the system to simulate an actual robot manipulator system. Besides, a two-degree-of-freedom (2-DOF) manipulator is constructed to verify the control performance of the MLADRC. Compared with the regular LADRC, MLADRC is significantly characterized by the addition of feedforward control of reference angular acceleration, which helps robot manipulators keep up with target trajectories more accurately. The simulation and experimental results demonstrate the superiority of the MLADRC over the regular LADRC for the trajectory tracking control.

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