Design and Evaluation of Adaptive and Sliding Mode Control for a 3-DOF Delta Parallel Robot

Azad, Faraz Abed; Rahimi, Saeed; Yazdi, Mohammad Reza Hairi; Masouleh, Mehdi Tale

doi:10.1109/icee50131.2020.9261040

Cited by 8 publications

(3 citation statements)

References 17 publications

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“…The experiment was carried out in simulation to investigate the effectiveness of the proposed controller. On the other hand, Azad et al [8] performed the backstepping design to Control a linear delta robot in simulation. In [9], they analyzed and simulated the solid work to get the exact motion process and then verified it with FANUC Robot M-2iA and ROBOGUIDE in a real-time application.…”

Section: Introductionmentioning

confidence: 99%

The Graphical User Interface for Controlling Delta Robot Movement through G-Code

Analia,

Gozali,

Sipahutar

et al. 2023

Proceedings of the 5th International Conference on Applied Engineering, ICAE 2022, 5 October 2022, Batam, Indonesia

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This research aims to develop a Graphical User Interface (GUI) to control the delta robot using the G-Code command in real time. The proposed mechanical design of the delta robot adopts a parallel arm mechanism. As the primary controller, the Arduino Uno has been chosen as a bridge to translate the joint command from the computer to the robot stepper motor. Furthermore, we proposed the G-Code command to control the delta robot end effector. The system interface was carried out using the C# programming language and the.NET frameworks. As G-Code translation, we proposed an inverse kinematics equation derived from a trigonometric to decode command into joint movement. Finally, the experiment was carried out in real-time to verify our interface. As an experimental result, the proposed system successfully translates the G-Code command into end effector movement.

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Section: Introductionmentioning

confidence: 99%

The Graphical User Interface for Controlling Delta Robot Movement through G-Code

Analia,

Gozali,

Sipahutar

et al. 2023

Proceedings of the 5th International Conference on Applied Engineering, ICAE 2022, 5 October 2022, Batam, Indonesia

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show abstract

“…A 3-DOF Delta parallel robot's simulation, kinematics analysis, and control have been investigated in [46]where the ability of the controllers to reject white noise disturbance applied to the system's input signals was demonstrated using simulation results [46]- [60].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Inverse Dynamic Deep Neural Network Tracking Control for Delta Robot Based on a COVID-19 Optimization

Shamseldin

2023

Journal of Robotics and Control

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This paper presents a new technique to design an inverse dynamic model for a delta robot experimental setup to obtain an accurate trajectory. The input/output data were collected using an NI DAQ card where the input is the random angles profile for the three-axis and the output is the corresponding measured torques. The inverse dynamic model was developed based on the deep neural network (NN) and the new COVID-19 optimization to find the optimal initial weights and bias values of the NN model. Due to the system uncertainty and nonlinearity, the inverse dynamic model is not enough to track accurately the preselected profile. So, the PD compensator is used to absorb the error deviation of the end effector. The experimental results show that the proposed inverse dynamic deep NN with PD compensator achieves good performance and high tracking accuracy. The suggested control was examined using two different methods. The spiral path is the first, with a root mean square error of 0.00258 m, while the parabola path is the second, with a root mean square error of 0.00152 m.

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“…The PID control has been commonly adopted for various robots since it was proposed, thanks to the simple control scheme and eased parameter tuning. On the other hand, the uncertainties cannot be readily handled in the use of this type of control law, and therefore, the PID control is usually designed in combination with other control algorithms to improve the tracking accuracy [4][5][6][7][8][9][10], e.g., feedforward [11], adaptive [12,13], robust [14], fuzzy control [15], etc. Besides, a number of different control algorithms have been proposed and applied to the parallel pick-and-place (PnP) robots, such as computed-torque control (CTC) [16] and sliding mode control (SMC) [17,18], but these heavily depends on the accurate dynamic model of the robots, which is a challenging task due to the highly nonlinear coupled system.…”

Section: Introductionmentioning

confidence: 99%

Trajectory Tracking Control of Fast Parallel SCARA Robots with Fuzzy Adaptive Iterative Learning Control for Repetitive Pick-and-Place Operations

Wu,

Niu,

2023

Electronics

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Aiming at enhanced suppression of external disturbances and high-precision trajectory tracking of parallel SCARA robot dedicating to fast pick-and-place operations, this work presents the integrated control design of iterative learning algorithm, adaptive control and fuzzy rules, namely, fuzzy adaptive iterative learning control, for such type of robots. A step-design approach is adopted to ensure the adaptability of the designed control law, which is reflected in two aspects: ① the feedback gain of the controller is adjusted by the fuzzy rules; ② the adaptive unknown parameters are obtained by means of iterative learning estimation to suppress the uncertainties and external disturbances. The stability of the designed controller is analyzed and proved by the Lyapunov theory, and the effectiveness is verified by observing the tracking errors in joint space along with the testing pick path, in comparison with different iterative learning based algorithms. After the first-iteration learning, the motion errors of the four actuated joints can be reduced by 56.5%, 45.8%, 46.4% and 39.8%, respectively, and after 15 iterations of learning control, the final angular errors by the designed control law converge to 0.7×10−4 degree maximally. The varying maximum, root-mean-squared and mean angular displacement errors of the actuation joints can converge to zero values with the increasing iterations rapidly, which shows the robustness, effectiveness and advantages of the designed control law. The designed control law can be generalized to high-speed parallel pick-and-place robot to ensure high-precision trajectory tracking for high-quality material handling tasks.

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Design and Evaluation of Adaptive and Sliding Mode Control for a 3-DOF Delta Parallel Robot

Cited by 8 publications

References 17 publications

The Graphical User Interface for Controlling Delta Robot Movement through G-Code

The Graphical User Interface for Controlling Delta Robot Movement through G-Code

Real-Time Inverse Dynamic Deep Neural Network Tracking Control for Delta Robot Based on a COVID-19 Optimization

Trajectory Tracking Control of Fast Parallel SCARA Robots with Fuzzy Adaptive Iterative Learning Control for Repetitive Pick-and-Place Operations

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