Dynamic Modeling and Torque Feedforward based Optimal Fuzzy PD control of a High-Speed Parallel Manipulator

Lin, Zongli; Cui, Chuangchuang; Wu, Guanglei

doi:10.18196/26133

Cited by 9 publications

(11 citation statements)

References 30 publications

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“…Fuzzy system is mainly used for control, such as controlling robot movement [44], [45], speed [46], and other factors [47][48][49]. However, fuzzy system can basically be used for anything.…”

Section: Adaptive Neuro Fuzzy Inference System Methodsmentioning

confidence: 99%

Near Ground Pathloss Propagation Model Using Adaptive Neuro Fuzzy Inference System for Wireless Sensor Network Communication in Forest, Jungle and Open Dirt Road Environments

Hakim

Habaebi

Toha

et al. 2022

Sensors

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In Wireless Sensor Networks which are deployed in remote and isolated tropical areas; such as forest; jungle; and open dirt road environments; wireless communications usually suffer heavily because of the environmental effects on vegetation; terrain; low antenna height; and distance. Therefore; to solve this problem; the Wireless Sensor Network communication links must be designed for their best performance using the suitable electromagnetic wave behavior model in a given environment. This study introduces and analyzes the behavior of the LoRa pathloss propagation model for signals that propagate at near ground or that have low transmitter and receiver antenna heights from the ground (less than 30 cm antenna height). Using RMSE and MAE statistical analysis tools; we validate the developed model results. The developed Fuzzy ANFIS model achieves the lowest RMSE score of 0.88 at 433 MHz and the lowest MAE score of 1.61 at 433 MHz for both open dirt road environments. The Optimized FITU-R Near Ground model achieved the lowest RMSE score of 4.08 at 868 MHz for the forest environment and lowest MAE score of 14.84 at 868 MHz for the open dirt road environment. The Okumura-Hata model achieved the lowest RMSE score of 6.32 at 868 MHz and the lowest MAE score of 26.12 at 868 MHz for both forest environments. Finally; the ITU-R Maximum Attenuation Free Space model achieved the lowest RMSE score of 9.58 at 868 MHz for the forest environment and the lowest MAE score of 38.48 at 868 MHz for the jungle environment. These values indicate that the proposed Fuzzy ANFIS pathloss model has the best performance in near ground propagation for all environments compared to other benchmark models

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Section: Adaptive Neuro Fuzzy Inference System Methodsmentioning

confidence: 99%

Near Ground Pathloss Propagation Model Using Adaptive Neuro Fuzzy Inference System for Wireless Sensor Network Communication in Forest, Jungle and Open Dirt Road Environments

Hakim

Habaebi

Toha

et al. 2022

Sensors

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“…The robot dynamic model describes the relationship between the end-effector position, velocity and acceleration and the joint driving torque. The parallel robot dynamic model has been well documented in detail in previous work [11] , which is revisited by skipping the details. The robot dynamics model can be expressed as:…”

Section: Robot Dynamic Modelmentioning

confidence: 99%

Trajectory tracking control of SCARA parallel robot based on fuzzy adaptive iterative learning algorithm

Shen

2023

Third International Conference on Mechanical Design and Simulation (MDS 2023)

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Aiming to improve the anti-interference capability and trajectory tracking accuracy of the robot control system that undertakes repetitive motion tasks, this work presents the trajectory tracking control along with a four degree-of-freedom parallel robot. By means of the principle of virtual work, the dynamic model of the robot was built for the model-based control design. A fuzzy adaptive iterative learning control algorithm was proposed by combining fuzzy control theory and iterative learning theory. Finally, the proposed algorithm was simulated and verified in comparison with the traditional iterative learning control algorithm, of which the results show that the proposed algorithm has robustness and higher tracking accuracy than the traditional one.

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“…The paper proposes an experimental validation of an inverse dynamics control applied to a simple pick-and-place trajectory. Starting from the obtained results, the same authors propose a more advanced control system in [8]. In this case, a PD controller with the addition of an offline precomputed torque is presented.…”

Section: Introductionmentioning

confidence: 99%

An Experimental Investigation of the Dynamic Performances of a High Speed 4-DOF 5R Parallel Robot Using Inverse Dynamics Control

Righettini,

Strada,

Cortinovis

et al. 2024

Robotics

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High-speed pick-and-place industrial applications often use parallel kinematic robots due to their high stiffness and dynamic performance; furthermore, the latter not only depends on the mechanical characteristics of the robots but also on the control algorithm. The literature shows several theoretical contributions to such controllers, mainly tested at the simulation level or on simple proof-of-concept laboratory equipment that execute low-speed and simple trajectories. This paper presents an experimental investigation of the dynamic performance of an industrial high-speed 4-DOF 5R parallel robot designed for pick-and-place applications on moving objects. The inverse dynamics control in the task space is used as a control algorithm. The results show the contribution of all the components of the control algorithm to the motor torque, and the inverse dynamics controller performances are discussed also in comparison to those achievable with simpler PD or PID controllers in a joint space. Moreover, the paper shows the controller synthesis from a modern mechatronic point of view, and the effectiveness of the proposed solution for the tracking of complex high-speed trajectories in an industrial application.

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Dynamic Modeling and Torque Feedforward based Optimal Fuzzy PD control of a High-Speed Parallel Manipulator

Cited by 9 publications

References 30 publications

Near Ground Pathloss Propagation Model Using Adaptive Neuro Fuzzy Inference System for Wireless Sensor Network Communication in Forest, Jungle and Open Dirt Road Environments

Near Ground Pathloss Propagation Model Using Adaptive Neuro Fuzzy Inference System for Wireless Sensor Network Communication in Forest, Jungle and Open Dirt Road Environments

Trajectory tracking control of SCARA parallel robot based on fuzzy adaptive iterative learning algorithm

An Experimental Investigation of the Dynamic Performances of a High Speed 4-DOF 5R Parallel Robot Using Inverse Dynamics Control

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