scholarly journals Inverse Kinematic Control of a Delta Robot Using Neural Networks in Real-Time

Robotics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 115
Author(s):  
Akram Gholami ◽  
Taymaz Homayouni ◽  
Reza Ehsani ◽  
Jian-Qiao Sun
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Trajectory Tracking ◽  
Tracking Error ◽  
Kinematic Model ◽  
Inverse Kinematic ◽  
Robot Kinematics ◽  
Kinematic Control ◽  
Modeling Uncertainties ◽  
Delta Robot

This paper presents an inverse kinematic controller using neural networks for trajectory controlling of a delta robot in real-time. The developed control scheme is purely data-driven and does not require prior knowledge of the delta robot kinematics. Moreover, it can adapt to the changes in the kinematics of the robot. For developing the controller, the kinematic model of the delta robot is estimated by using neural networks. Then, the trained neural networks are configured as a controller in the system. The parameters of the neural networks are updated while the robot follows a path to adaptively compensate for modeling uncertainties and external disturbances of the control system. One of the main contributions of this paper is to show that updating the parameters of neural networks offers a smaller tracking error in inverse kinematic control of a delta robot with consideration of joint backlash. Different simulations and experiments are conducted to verify the proposed controller. The results show that in the presence of external disturbance, the error in trajectory tracking is bounded, and the negative effect of joint backlash in trajectory tracking is reduced. The developed method provides a new approach to the inverse kinematic control of a delta robot.

scholarly journals An Autonomous Path Controller in a System on Chip for Shrimp Robot

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 441 ◽  
Author(s):  
Sergio Barrios-dV ◽  
Michel Lopez-Franco ◽  
Jorge D. Rios ◽  
Nancy Arana-Daniel ◽  
Carlos Lopez-Franco ◽  
...  
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Trajectory Tracking ◽  
Neural Control ◽  
Tracking System ◽  
Kinematic Model ◽  
System On Chip ◽  
Optimal Controller ◽  
Real Time System ◽  
On Chip

This paper presents a path planning and trajectory tracking system for a BlueBotics Shrimp III®, which is an articulate mobile robot for rough terrain navigation. The system includes a decentralized neural inverse optimal controller, an inverse kinematic model, and a path-planning algorithm. The motor control is obtained based on a discrete-time recurrent high order neural network trained with an extended Kalman filter, and an inverse optimal controller designed without solving the Hamilton Jacobi Bellman equation. To operate the whole system in a real-time application, a Xilinx Zynq® System on Chip (SoC) is used. This implementation allows for a good performance and fast calculations in real-time, in a way that the robot can explore and navigate autonomously in unstructured environments. Therefore, this paper presents the design and implementation of a real-time system for robot navigation that integrates, in a Xilinx Zynq® System on Chip, algorithms of neural control, image processing, path planning, and inverse kinematics and trajectory tracking.

scholarly journals Nonlinear PD plus sliding mode control with application to a parallel delta robot

2018 ◽  
Vol 69 (5) ◽  
pp. 329-336 ◽  
Author(s):  
Chems Eddine Boudjedir ◽  
Djamel Boukhetala ◽  
Mohamed Bouri
Keyword(s):  
Convergence Rate ◽  
Error Rate ◽  
Trajectory Tracking ◽  
Sliding Mode ◽  
Tracking Error ◽  
Sliding Mode Controller ◽  
External Disturbances ◽  
On Line ◽  
Delta Robot ◽  
The Stability

Abstract In this paper, a hybrid nonlinear proportional-derivative-sliding mode controller (NPD-SMC) is developed for the trajectory tracking of robot manipulators. The proposed controller combines the advantage of the easy implementation of NPD control and the robustness of SMC. The gains of PD control are tuned on-line in order to increase the convergence rate, whereas the SMC term is introduced to reject the external disturbances without requiring to know the system dynamics. The stability of the NPD-SMC is proved using Lyaponuv theorem, and it is demonstrated that the tracking error and the tracking error rate converge asymptotically to zero. Experiments are carried out on the parallel Delta robot to illustrate the effectiveness and robustness of the proposed approach. It is also shown the superiority of the NPD-SMC control over the NPD control and PD-SMC control.

Multi-dimensional Taylor network modelling and optimal control of SISO nonlinear systems for tracking by output feedback

2019 ◽  
Vol 37 (3) ◽  
pp. 699-717 ◽  
Author(s):  
Qi-Ming Sun ◽  
Hong-Sen Yan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Nonlinear Systems ◽  
Real Time ◽  
Output Feedback ◽  
Tracking Error ◽  
Real Time Control ◽  
Network Modelling ◽  
Single Input Single Output ◽  
Time Control

Abstract In this paper, a multi-dimensional Taylor network (MTN) output feedback tracking control of nonlinear single-input single-output (SISO) systems in discrete-time form is studied. To date, neural networks are generally used to identify unknown nonlinear systems. However, the neuron of neural networks includes the exponential function, which contributes to the complexity of calculation, making the neural network control unable to meet the real-time requirements. In order to identify the controlled object whose model is unknown, the MTN, which requires only addition and multiplication, is utilized for successful real-time control of the SISO nonlinear system based on only its output feedback. Lyapunov analysis proves that output signals in the closed-loop system remain bounded and the tracking error converges to an arbitrarily small neighbourhood around the origin. In contrast to the back propagation (BP) neural network self-adaption reconstitution controller, the edge of the scheme is that the MTN optimal controller promises desirable response speed, robustness and real-time control.

Workspace Generation for a 2 - DOF Parallel Mechanism Using Neural Networks

2012 ◽  
Vol 162 ◽  
pp. 121-130 ◽  
Author(s):  
Emilia Campean ◽  
Tiberiu Pavel Itul ◽  
Ionela Tanase ◽  
Adrian Pisla
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Parallel Mechanism ◽  
Degrees Of Freedom ◽  
Kinematic Model ◽  
Inverse Kinematic ◽  
Two Degrees Of Freedom ◽  
Initial Testing ◽  
Network Application ◽  
Satellite Dish

The main purpose of the paper is to develop a neural network application destined to the workspace generation of a parallel mechanism, as an performant alternative to the workspace representation based on inverse kinematic model. The paper describes both algorithms. The initial testing was made for a parallel mechanism with two degrees of freedom that could be applied for the orientation of different systems like a TV satellite dish antennas, sun trackers, telescopes, cameras, radars etc.

Iterative learning control for trajectory tracking of a parallel Delta robot

2019 ◽  
Vol 67 (2) ◽  
pp. 145-156
Author(s):  
Chems Eddine Boudjedir ◽  
Mohamed Bouri ◽  
Djamel Boukhetala
Keyword(s):  
Trajectory Tracking ◽  
Iterative Learning Control ◽  
Tracking Error ◽  
Iterative Learning ◽  
Pd Controller ◽  
Alignment Condition ◽  
Repetitive Tasks ◽  
Delta Robot ◽  
High Cadence ◽  
Number Of Iterations

Abstract This paper proposes an iterative learning controller (ILC) under the alignment condition for trajectory tracking of a parallel Delta robot, that performs various repetitive tasks for palletization. Motivated by the high cadence of our application that leads to significant coupling effects, where the traditional PD/PID fail to satisfy the requirements performances. A PD-type ILC is combined with a PD controller in order to enhance the performance through iterations during the whole operation interval. The traditional resetting condition is replaced by the practical alignment condition, then the convergence of the tracking error is derived based on the Lyapunov’s theory. We definitely point out that the position and velocity errors decrease as the number of iterations increases. Experiments are carried out to demonstrate the effectiveness of the proposed controller.

scholarly journals Kinematics of Serial Manipulators

2020 ◽  
Author(s):  
Ivan Virgala ◽  
Michal Kelemen ◽  
Erik Prada
Keyword(s):  
Rigid Body ◽  
Numerical Solutions ◽  
Kinematic Model ◽  
Inverse Kinematic ◽  
Body Motion ◽  
Robot Kinematics ◽  
Kinematic Modeling ◽  
Open Chain ◽  
Serial Manipulators ◽  
Serial Robot

This book chapter deals with kinematic modeling of serial robot manipulators (open-chain multibody systems) with focus on forward as well as inverse kinematic model. At first, the chapter describes basic important definitions in the area of manipulators kinematics. Subsequently, the rigid body motion is presented and basic mathematical apparatus is introduced. Based on rigid body conventions, the forward kinematic model is established including one of the most used approaches in robot kinematics, namely the Denavit-Hartenberg convention. The last section of the chapter analyzes inverse kinematic modeling including analytical, geometrical, and numerical solutions. The chapter offers several examples of serial manipulators with its mathematical solution.

scholarly journals SELF-LEARNING OF DELTA ROBOT USING INVERSE KINEMATICS AND ARTIFICIAL NEURAL NETWORKS

SINERGI ◽  
2021 ◽  
Vol 25 (3) ◽  
pp. 237
Author(s):  
Zendi Iklima ◽  
Muhammad Imam Muthahhar ◽  
Asif Khan ◽  
Arifiansyah Zody
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Inverse Kinematic ◽  
End Effector ◽  
Motion Data ◽  
Average Accuracy ◽  
Stepper Motors ◽  
Average Loss ◽  
Artificial Neural ◽  
Delta Robot

As known as Parallel-Link Robot, Delta Robot is a kind of Manipulator Robot that consists of three arms mounted in parallel. Delta Robot has a central joint constructed as an end-effector represented as a gripper. An Analysis of Inverse Kinematic (IK) used to convert the end-effector trajectory (X, Y) into rotations of stepper motors (ZA, ZB and ZC). The proposed method used Artificial Neural Networks (ANNs) to simplify the process of IK solver. The IK solver generated the datasets contain motion data of the Delta robot. There are 11 KB Datasets consist of 200 motion data used to be trained. The proposed method was trained in 58.78 seconds in 5000 iterations. Using a learning rate (α) 0.05 and produced the average accuracy was 97.48%, and the average loss was 0.43%. The proposed method was also tested to transfer motion data over Socket.IO with 115.58B in 6.68ms.

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