A Decentralized Adaptive Controller for Robot Manipulator Trajectory Control

1992 ◽  
pp. 109-116
Author(s):  
Premal Desai
Keyword(s):  
Robot Manipulator ◽  
Adaptive Controller ◽  
Trajectory Control

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.

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.

A New Neural Network Based Sliding Mode Adaptive Controller for Tracking Control of Robot Manipulator

2021 ◽  
Vol 11 (2) ◽  
pp. 12-16
Author(s):  
Monisha Pathak ◽  
◽  
Mrinal Buragohain ◽  
Keyword(s):  
Neural Network ◽  
Trajectory Tracking ◽  
Tracking Control ◽  
Sliding Mode ◽  
Robot Manipulator ◽  
Rbf Neural Network ◽  
Adaptive Controller ◽  
Robotic Manipulator ◽  
Trajectory Tracking Control ◽  
Chattering Effect

In this paper a New RBF Neural Network based Sliding Mode Adaptive Controller (NNNSMAC) for Robot Manipulator trajectory tracking in the presence of uncertainties and disturbances is introduced. The research offers a learning with minimal parameter (LMP) technique for robotic manipulator trajectory tracking. The technique decreases the online adaptive parameters number in the RBF Neural Network to only one, lowering computational costs and boosting real-time performance. The RBFNN analyses the system's hidden non-linearities, and its weight value parameters are updated online using adaptive laws to control the nonlinear system's output to track a specific trajectory. The RBF model is used to create a Lyapunov function-based adaptive control law. The effectiveness of the designed NNNSMAC is demonstrated by simulation results of trajectory tracking control of a 2 dof Robotic Manipulator. The chattering effect has been significantly reduced.

scholarly journals Comparative Study of Some New Hybrid Fuzzy Algorithms for Manipulator Control

2007 ◽  
Vol 2007 ◽  
pp. 1-10
Author(s):  
Sudeept Mohan ◽  
Surekha Bhanot
Keyword(s):  
Fuzzy Controller ◽  
Robot Manipulator ◽  
Adaptive Controller ◽  
Maximum Error ◽  
Control Algorithms ◽  
Adaptive Controllers ◽  
Fuzzy Algorithms ◽  
Actuation Signal ◽  
Manipulator Control ◽  
Better Than

The robot manipulator is a highly complex system, which is multi-input, multi-output, nonlinear, and time variant. Controlling such a system is a tedious and challenging task. In this paper, some new hybrid fuzzy control algorithms have been proposed for manipulator control. These hybrid fuzzy controllers consist of two parts: a fuzzy controller and a conventional or adaptive controller. The outputs of these controllers are superimposed to produce the final actuation signal based on current position and velocity errors. Simulation is used to test these controllers for different trajectories and for varying manipulator parameters. Various performance indices like the RMS error, steady state error, and maximum error are used for comparison. It is observed that the hybrid controllers perform better than only fuzzy or only conventional/adaptive controllers.

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