scholarly journals Robust Hybrid Control Using Recurrent Wavelet- Neural-Network Sliding-Mode Controller for Two- Axis Motion Control System

2021 ◽  
Vol 8 ◽  
pp. 49-59
Author(s):  
Fayez F. M. El-Sousy
Keyword(s):  
Control System ◽  
Motion Control ◽  
Motion Tracking ◽  
Hybrid Control ◽  
Sliding Mode ◽  
Torque Control ◽  
Numerical Control ◽  
Tracking Performance ◽  
Sliding Mode Controller ◽  
Motion Control System

In this paper, a robust hybrid control system (RHCS) for achieving high precision motion tracking performance of a two-axis motion control system is proposed. The proposed AHCS incorporating a recurrent wavelet-neuralnetwork controller (RWNNC) and a sliding-mode controller (SMC) to construct a RRWNNSMC. The two-axis motion control system is an x-y table of a computer numerical control machine that is driven by two field-oriented controlled permanent-magnet synchronous motors (PMSMs) servo drives. The RWNNC is used as the main motion tracking controller to mimic a perfect computed torque control law and the SMC controller is designed with adaptive bound estimation algorithm to compensate for the approximation error between the RWNNC and the ideal controller. The on-line learning algorithms of the connective weights, translations and dilations of the RWNNC are derived using Lyapunov stability analysis. A computer simulation and an experimental are developed to validate the effectiveness of the proposed RHCS. All control algorithms are implemented in a TMS320C31 DSP-based control computer. The simulation and experimental results using star and four leaves contours are provided to show the effectiveness of the RHCS. The motion tracking performance is significantly improved using the proposed RHCS and robustness to parameter variations, external disturbances, cross-coupled interference and frictional torque can be obtained as well for the two-axis motion control system.

Motion Control System and Control Algorithm of 3-Axes Platform Based on Controller Area Network

2014 ◽  
Vol 532 ◽  
pp. 196-199
Author(s):  
Cheng Long Zhou ◽  
Xing Song Wang ◽  
Yu Liang Mao
Keyword(s):  
Control System ◽  
Motion Control ◽  
Control Algorithm ◽  
Controller Area Network ◽  
Numerical Control ◽  
Area Network ◽  
Motion Controller ◽  
Motion Control System ◽  
And Control ◽  
Control Machine

3-axes motion is broadly used in industry numerical control machine. Many motion controller we use are usually limited by the number of interface it consist. But the kind of motion controller based on CAN (Controller Area Network ) can solve this problem properly. We can add a controller of one axe conveniently. I use a Advantech CAN motion controller to build the motion control system.

Probability of Collision between Autonomous Mobile Robot with an Obstacle

2021 ◽  
Vol 22 (3) ◽  
pp. 125-133
Author(s):  
D. S. Iakovlev ◽  
A. A. Tachkov
Keyword(s):  
Control System ◽  
Mobile Robot ◽  
Motion Control ◽  
Motion Tracking ◽  
Probability Estimation ◽  
Angular Deviation ◽  
Autonomous Mobile Robot ◽  
Motion Control System ◽  
Grid Map ◽  
Occupancy Grids

The probability estimation problem of a collision between path tracking for an autonomous mobile robot with an obstacle is considered. We reviewed and analyzed methods for solving this problem. We show that reviewed methods use periodically updated grid maps (occupancy grids). The new method of probability estimation of the collision between the mobile robot with an obstacle is presented. This method based on the use of probabilistic grid map. Each cell of this map stores the estimated probability that the obstacle is located within. In addition, this map stores the conditional probability of occupying of the map cells by a robot, taking into account the possible lateral and angular deviation from the planned trajectory. This deviation caused by error connected with dynamic characteristics of the tracking system. To build the probabilistic occupancy grid, the dynamically updated multilayer grid map was used. Each layer of this map, except for the resulting output, has been filled with the data obtained from classifiers which process information incoming from sensory of the robot. This layer is the result of Bayesian inference from the layers laying below. The motion control system provides construction of the multilayered grid maps, probabilistic occupancy grids, coordinate estimations, path planning, motion tracking and the probability estimation for collision with obstacles. The method such estimation is implemented as an embedded module compatible with ROS (Robot Operating System). The description of experiments with the mobile robot in-nature (on the field) is given in the case when a motile obstacle appears intercepting the planned path. The estimated changes of probability for a collision between the mobile robot with obstacle are presented, interpretation of the obtained results is also given. Here we demonstrated the necessity of collision probability estimation for assessment of the risk as the main safety indicator of the given motion control system. Results of this work are considered and evaluated as a solution to the problem of ensuring the safety of motion tracking for autonomous mobile robots.

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