Robust Adaptive Finite-Time Synergetic Tracking Control of Delta Robot Based on Radial Basis Function Neural Networks

Pham, Phu-Cuong; Kuo, Yong-Lin

doi:10.3390/app122110861

Cited by 8 publications

(5 citation statements)

References 46 publications

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“…One of the first strategies described for using neural networks to control unknown nonlinear systems was training the network to serve as the system's inverse and using that as a controller. Assuming that the system to be controlled can be described by (4).…”

Section: 𝑒 = 𝑞 𝑑 − 𝑞mentioning

confidence: 99%

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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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Section: 𝑒 = 𝑞 𝑑 − 𝑞mentioning

confidence: 99%

“…As technology advanced, parallel robots became more desirable in industrial applications where high speed, high accuracy, and high acceleration are required [1]- [4]. When compared to serial robots, parallel robots have clear advantages, such as their high speed and rigidity [5]- [9].…”

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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“…Sliding mode control is one of the most popular nonlinear control algorithms [4,5]. This algorithm has been extensively applied to the trajectory tracking of the Delta robot combined with the fuzzy neural network [6], nonlinear proportional-derivative (PD) control [7], and synergetic control [8]. Additionally, Radial Basis Function Neural Networks (RBFNNs) combined with sliding mode control were adopted in [8,9] to compensate for the unknown disturbances.…”

Section: Introductionmentioning

confidence: 99%

“…This algorithm has been extensively applied to the trajectory tracking of the Delta robot combined with the fuzzy neural network [6], nonlinear proportional-derivative (PD) control [7], and synergetic control [8]. Additionally, Radial Basis Function Neural Networks (RBFNNs) combined with sliding mode control were adopted in [8,9] to compensate for the unknown disturbances. The work reported in [10] proposed an online estimation approach to compensate for various uncertainties in the Delta robot and to improve the tracking performance.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Neural Network Effectiveness on Sliding Mode Control of Delta Robot for Trajectory Tracking

Zhao,

Toudeshki,

Ehsani

et al. 2024

Algorithms

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The Delta robot is an over-actuated parallel robot with highly nonlinear kinematics and dynamics. Designing the control for a Delta robot to carry out various operations is a challenging task. Various advanced control algorithms, such as adaptive control, sliding mode control, and model predictive control, have been investigated for trajectory tracking of the Delta robot. However, these control algorithms require a reliable input–output model of the Delta robot. To address this issue, we have created a control-affine neural network model of the Delta robot with stepper motors. This is a completely data-driven model intended for control design consideration and is not derivable from Newton’s law or Lagrange’s equation. The neural networks are trained with randomly sampled data in a sufficiently large workspace. The sliding mode control for trajectory tracking is then designed with the help of the neural network model. Extensive numerical results are obtained to show that the neural network model together with the sliding mode control exhibits outstanding performance, achieving a trajectory tracking error below 5 cm on average for the Delta robot. Future work will include experimental validation of the proposed neural network input–output model for control design for the Delta robot. Furthermore, transfer learnings can be conducted to further refine the neural network input–output model and the sliding mode control when new experimental data become available.

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“…Owning to flexibility of the delta-robot structure [19], a vibration suppression could be optimized in the trajectory planning by minimizing an elastic deformation of its moving platform with constrains on three-motor torques [20]. The fractional order PID controller, based on an inverse dynamic method to linearized and to uncoupled the delta-robot dynamic model [21], and a robust PD adaptive nonsingular finite-time synergetic control [22] were applied for trajectory tracking and pick-and-place tasks. Furthermore, a model-free iterative learning control strategy combining with Lyapunov approach [23] could precisely control the delta robot for nonrepetitive-trajectory applications.…”

Section: Introductionmentioning

confidence: 99%

Visual Servo Kinematic Control of Delta Robot using YOLOv5 Algorithm

Yamtuan,

Radomngam,

Prempraneerach

2023

Journal of Robotics and Control

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Industrial delta robots with motion-and vision-base controllers, providing high-precision and fast/flexible motion, normally come with high cost and complex software. Low-cost delta robots with vision solution could be beneficial to vast industries to increase productivity and to reduce labor cost. A delta robot using a low-cost motion controller and an open-source vision system is developed to accomplish real-time visual servoing with high motion accuracy. In the low-cost motion controller, three parallel links’ upper-arm angles, computed from inverse kinematics for a given desired target position by a high-level computer, are used as reference position commands for three AC-motor drives. A low-level Arduino microcontroller is employed to convert these links’ angles to high-frequency pulses and on-off signals for synchronously controlling three motor angles and direction. Experimental results of a point-to-point motion tracking exhibit high-precision repeatability. Synchronous pulse generation from Arduino microcontroller and structural misalignments of parallel links are major challenges for achieving high motion accuracy. For the vision-based system, the YOLOv5 algorithm is implemented along with a Python GUI Application. Then, the visual-servo performance is evaluated on localization accuracy and recognition rate of 3-color objects. However, a partial object occlusion can reduce the visual classification rate. A sorting task of 4-category medicine boxes demonstrate a high-speed pick-and-place operation using the low-cost visual-servo system of this delta robot. Therefore, integration of low-cost visual servoing with this delta robot can revolutionize various industries, like automobile, pharmaceutical, and food sectors, in separating, sorting and packing applications.

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Robust Adaptive Finite-Time Synergetic Tracking Control of Delta Robot Based on Radial Basis Function Neural Networks

Cited by 8 publications

References 46 publications

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

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

Evaluation of Neural Network Effectiveness on Sliding Mode Control of Delta Robot for Trajectory Tracking

Visual Servo Kinematic Control of Delta Robot using YOLOv5 Algorithm

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