A new and simple algorithm for sliding mode trajectory control of the robot arm

1990 ◽  
Vol 35 (7) ◽  
pp. 828-829 ◽  
Author(s):  
Y.-F. Chen ◽  
T. Mita ◽  
S. Wakui
Keyword(s):  
Sliding Mode ◽  
Simple Algorithm ◽  
Trajectory Control ◽  
Robot Arm

scholarly journals Trajectory control of robot arm using sliding mode control and experimented results.

1989 ◽  
Vol 7 (6) ◽  
pp. 706-711 ◽  
Author(s):  
Yi-Seng CHEN ◽  
Hiroyuki IKEDA ◽  
Tsutomu MITA ◽  
Shinji WAKUI
Keyword(s):  
Sliding Mode Control ◽  
Sliding Mode ◽  
Trajectory Control ◽  
Robot Arm ◽  
Mode Control

Trajectory control of a robot arm using sliding mode control

1990 ◽  
Vol 5 (4) ◽  
pp. 385-395
Author(s):  
Yi-Feng Chen ◽  
Hiroyuki Ikeda ◽  
Tsutomu Mita ◽  
Shiji Wakui
Keyword(s):  
Sliding Mode Control ◽  
Sliding Mode ◽  
Trajectory Control ◽  
Robot Arm ◽  
Mode Control

scholarly journals Adaptive Robust Controller Design-Based RBF Neural Network for Aerial Robot Arm Model

Electronics ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 831
Author(s):  
Izzat Al-Darraji ◽  
Dimitrios Piromalis ◽  
Ayad A. Kakei ◽  
Fazal Qudus Khan ◽  
Milos Stojemnovic ◽  
...  
Keyword(s):  
Neural Network ◽  
Controller Design ◽  
Sliding Mode ◽  
Rbf Neural Network ◽  
Robot Arm ◽  
Robust Controller ◽  
Practical Applications ◽  
Control Loops ◽  
Modeling Errors ◽  
Aerial Robot

Aerial Robot Arms (ARAs) enable aerial drones to interact and influence objects in various environments. Traditional ARA controllers need the availability of a high-precision model to avoid high control chattering. Furthermore, in practical applications of aerial object manipulation, the payloads that ARAs can handle vary, depending on the nature of the task. The high uncertainties due to modeling errors and an unknown payload are inversely proportional to the stability of ARAs. To address the issue of stability, a new adaptive robust controller, based on the Radial Basis Function (RBF) neural network, is proposed. A three-tier approach is also followed. Firstly, a detailed new model for the ARA is derived using the Lagrange–d'Alembert principle. Secondly, an adaptive robust controller, based on a sliding mode, is designed to manipulate the problem of uncertainties, including modeling errors. Last, a higher stability controller, based on the RBF neural network, is implemented with the adaptive robust controller to stabilize the ARAs, avoiding modeling errors and unknown payload issues. The novelty of the proposed design is that it takes into account high nonlinearities, coupling control loops, high modeling errors, and disturbances due to payloads and environmental conditions. The model was evaluated by the simulation of a case study that includes the two proposed controllers and ARA trajectory tracking. The simulation results show the validation and notability of the presented control algorithm.

scholarly journals Sliding mode control-based system for the two-link robot arm

2019 ◽  
Vol 9 (4) ◽  
pp. 2771
Author(s):  
Trong-Thang Nguyen
Keyword(s):  
Control System ◽  
Sliding Mode Control ◽  
Sliding Mode ◽  
Dynamic Equations ◽  
Sliding Mode Controller ◽  
Robot Arm ◽  
System Quality ◽  
Static Error ◽  
Very High

In this research, the author presents the model of the two-link robot arm and its dynamic equations. Based on these dynamic equations, the author builds the sliding mode controller for each joint of the robot. The tasks of the controllers are controlling the Torque in each Joint of the robot in order that the angle coordinates of each link coincide with the desired values. The proposed algorithm and robot model are built on Matlab-Simulink to investigate the system quality. The results show that the quality of the control system is very high: the response angles of each link quickly reach the desired values, and the static error equal to zero.

scholarly journals Sliding Mode Robot Control with Friction and Payload Estimation

2004 ◽  
Vol 8 (5) ◽  
pp. 553-561 ◽  
Author(s):  
Lörinc Márton ◽  
◽  
Béla Lantos ◽  
Keyword(s):  
Robot Control ◽  
Control Algorithm ◽  
Lyapunov Method ◽  
Sliding Mode ◽  
Robot Arm ◽  
Tracking Accuracy ◽  
Parameter Adaptation ◽  
Robust Parameter ◽  
Payload Mass ◽  
Friction Parameters

The paper deals with robust motion control of robotic systems with unknown friction parameters and payload mass. The parameters of the robot arm were considered known with a given precision. To solve the control of the robot with unknown payload mass and friction parameters, sliding mode control algorithm was proposed combined with robust parameter adaptation techniques. Using Lyapunov method it was shown that the resulting controller achieves a guaranteed final tracking accuracy. Simulation results are presented to illustrate the effectiveness and achievable control performance of the proposed scheme.

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