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Research on dynamics parameter identification and intelligent control for delta parallel robot

JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY 2025年第4期39卷 2099-2111页

作者： Zheng, Kunming Zeng, Shuisheng Jiangnan Univ Sch Intelligent Mfg Wuxi 214122 Peoples R China Jiangsu Key Lab Adv Food Mfg Equipment & Technol Wuxi 214122 Peoples R China Nanjing Univ Informat Sci Technol Sch Elect & Informat Engn Nanjing 210044 Peoples R China Jiangsu Jd Link Int Logist Co Ltd Wuxi 214028 Peoples R China Changzhou Gucoi Intelligent Equipment Technol Res Changzhou 213001 Peoples R China

The dynamics parameter identification of parallel robot is difficult and its identification accuracy is limited. Even under the action of advanced intelligent control methods, the problems of low pose accuracy and operation efficiency, poor stability still exist. Taking the delta parallel robot as the research object, this paper establishes the electromechanical coupling multi-energy domain dynamics model based on the parallel recursive dynamic modeling method, and carries out the detailed study on the dynamics parameter identification of delta robot. To improve the pose accuracy, operation efficiency and stability, based on the electromechanical coupling multi-energy domain dynamics model and dynamics parameter identification of delta robot, the identified dynamics parameters are applied to the control strategy, and the intelligent linear matrix inequalities (ILMI) control algorithm is designed. Finally, the detailed experimental analysis is carried out through the comparative experiments, the effectiveness of the dynamics parameter identification based on electromechanical coupling multi-energy domain dynamics model and the practicability of ILMI control algorithm are verified.

关键词： delta parallel robot Electromechanical coupling multi-energy domain dynamics model Dynamics parameter identification Intelligent control

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Numerical workspace optimization for a 3-DOF delta parallel robot on constrained geometric solid models

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2024年第18期238卷 9019-9031页

作者： Jiang, Zhenhua Liu, Dongdong Wang, Yu Donghua Univ Inst Artificial Intelligence 2999 North Renmin Rd Shanghai 201620 Peoples R China

For the workspace optimization of delta parallel robot (DPR), this paper employs Geometric Modeling Kernel (GMK) to construct DPR's solid workspace model constrained by two categories of angles with non-toroidal boundaries. Leveraging slicing and interpolating techniques, the maximal inscribed regular space within the DPR's workspace is obtained in conjunction with numerical optimization methods. With the objective of maximizing effective volume utilization (the volume ratio between the prescribed regular space to the DPR's workspace) and a prescribed regular space dimension, the Hook-Jeeves direct search method is coupled with the Interior-Point method to attain the optimal geometric DPR parameters on the constrained solid model of workspace. The effectiveness of the algorithm is validated through a design case. This numerical optimization methodology possesses generality, intuitiveness, precision, and high efficiency, theoretically applicable to the workspace optimization for other parallel mechanisms.

关键词： Workspace optimization constrained geometric solid model delta parallel robot numerical optimization geometric modeling kernel

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Robust PID optimal tuning of a delta parallel robot based on a hybrid optimization algorithm of particle swarm optimization and differential evolution

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robotICA 2023年第4期41卷 1159-1178页

作者： Pak, Yong-Ju Kong, Yong-Su Ri, Jin-Song Kim Il Sung Univ Fac Elect & Automat Pyongyang North Korea

In this paper, we propose an approach to tune optimal parameters of a robust PID controller by means of computed torque control (CTC) strategy for trajectory tracking of a delta parallel robot, using a hybrid optimization algorithm of Particle Swarm Optimization (PSO) and differential evolution (DE). It differs from previous works that they propose robust PID controller parameters tuning based on conventional gradient-based optimization algorithms and apply them to process control. First, we reduce the tuning problem of a robust PID controller with CTC strategy satisfying requirements including robustness and disturbance attenuation to an optimization problem with nonlinear constraints by considering the nonlinear dynamic model of a delta parallel robot. Second, we set up the design characteristics for the trajectory tracking of a delta parallel robot. Then, we propose a robust PID controller in a way of obtaining the global optimization solution of the nonlinear optimization problem by running a PSO-DE hybrid optimization algorithm of finding the global optimal solution by maintaining the diversity of swarm during evolution based on the evolution of cognitive experience. Simulation and experimental results demonstrate that the proposed controller outperforms previous works with respect to robust performance and active disturbance attenuation when it is applied to tracking control of a delta parallel robot.

关键词： delta parallel robot particle swarm optimization differential evolution robust PID control computed torque control

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Robust Trajectory Tracking of delta parallel robot Using Fractional-Order Sliding Mode Control

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IEEE ACCESS 2023年 11卷 86397-86412页

作者： Alizadeh, Behnoush Hajipour, Ahmad Tavakoli, Hamidreza Nasrabadi, Abbas Hakim Sabzevari Univ Fac Elect & Comp Engn Sabzevar *** Iran

In this study, a Fractional-Order Sliding Mode Control scheme is proposed for trajectory tracking control of delta parallel robot. The proposed controller is compared with both integer-order Proportional-Derivative controller and integer-order Sliding Mode Controller with Computed Torque Control method. The forward kinematics, inverse kinematics and dynamic of delta parallel robot are described. A Solidworks/Matlab/SimScape/Multibody model of delta parallel robot is generated and used for dynamic parameter estimation and validation of the proposed method. Particle Swarm Optimization algorithm is utilized for dynamic parameter estimation of delta parallel robot. The validation of the proposed method is evaluated for three different trajectories. External disturbances, noise and also various payloads are considered in testing robustness of control techniques. The results of the robustness tests confirm higher performance of FOSMC than two other control schemes.

关键词： delta parallel robot fractional-order sliding mode control trajectory tracking parameter estimation

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Model-Free Dynamic Control of a 3-DoF delta parallel robot for Pick-and-Place Application based on Deep Reinforcement Learning 10

Model-Free Dynamic Control of a 3-DoF Delta Parallel Robot f...

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10th RSI International Conference on robotics and Mechatronics (ICRoM)

作者： Jalali, Hasan Samadi, Saba Kalhor, Ahmad Masouleh, Mehdi Tale Univ Tehran Sch Elect & Comp Engn Tehran Iran

ISBN: (纸本)9781665454520

This article investigates the performance of the delta robot using model-free controller. Since it is difficult to obtain an accurate dynamic model of the robot and identification methods are complex and challenging. Therefore, in this article, a new approach is presented which checks the performance of the delta robot in pick-and-place operation using model-free dynamic controllers. For the purposes of this paper, Deep Reinforcement Learning (DRL) and also the Deep Deterministic Policy Gradient (DDPG) network is used. The DDPG algorithm is implemented for the first time on the delta robot, and according to the obtained results, it reveals that it can provide a favorable approach for the pick-and-place operation. In this method, based on the science of DRL, a reward function based on the pick-and-place operation is also defined, which gives negative reward (punishment) or a positive reward according to the way the robot performs. It is noteworthy that according to the obtained results, it is possible to generalize other points for the pick-and-place operation. It should be noted that the robot was able to learn the target after a long episode and its graph converged after approximately 18,000 episodes.

关键词： delta parallel robot Deep Reinforcement Learning Dynamic Control Model-Free Control

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Trajectory tracking control of delta parallel robot based on disturbance observer

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING 2021年第7期235卷 1193-1203页

作者： Wu, Mingkun Mei, Jiangping Ni, Jinlu Hu, Weizhong Tianjin Univ Key Lab Mech Theory & Equipment Design Minist Educ Peiyang Pk Campus135 Yaguan Rd Tianjin 300350 Peoples R China

delta parallel robot is widely used in the manufacturing process of food, medicine, electronics and military industries, which is a highly nonlinear system with strongly uncertain dynamics. Therefore, there are many difficulties in the controller design of delta robot. Based on the simplified dynamic model, a nonlinear PD+ controller with nonlinear disturbance observer is proposed for delta parallel robot in this article, which can realize high-precision trajectory tracking in high-speed and high-acceleration motion. Then, the asymptotic stability of the closed-loop system's equilibrium point is proven by utilizing Lyapunov techniques and LaSalle's invariance theorem. It is obvious that the proposed controller is significantly less dependent on the accuracy of the dynamic model. Besides, a disturbance observer based on the generalized momentum is constructed, which can effectively observe and compensate the disturbances. What's more, the constructed disturbance observer avoids the calculation of the inverse of inertia matrix, which will greatly improve the response speed of the controller. The simulation results show that the proposed controller can assure better trajectory tracking accuracy in high-speed and high-acceleration motion. And the disturbance observer can effectively estimate the disturbance. The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article:This work was supported by the National Natural Science Foundation of China (grant number51474320).

关键词： delta parallel robot PD plus controller disturbance observer trajectory tracking

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Experimental Study on Neural Network-ARX and ARMAX Actuation Identification of a 3-DoF delta parallel robot for Accurate Motion Controller Design 9

Experimental Study on Neural Network-ARX and ARMAX Actuation...

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9th RSI International Conference on robotics and Mechatronics (ICRoM)

作者： Rahimi, Saeed Jalali, Hasan Yazdi, Mohammad Reza Hairi Kalhor, Ahmad Masouleh, Mehdi Tale Univ Tehran Human & Robot Interact Lab Sch Mech Engn Tehran Iran

ISBN: (纸本)9781665420945

This paper investigates the problem of actuator identification of a 3-DoF delta parallel robot, by means of linear AutoRegressive Moving Average with eXogenous input (ARMAX) and nonlinear dynamic Neural Network AutoRegressive with eXogenous input (NN-ARX) methods. To this end, the ARMAX and NN-ARX approaches are used to develop a scheme which is capable of identifying a model for each actuator. Based on the ARMAX structure, an accurate model of the actuation system is derived. The model is then trained and tested using the data collected from a real robotic setup. Using a dynamic neural network capabilities, an identification and prediction scheme is designed for modeling the nonlinear dynamic behavior of the system. The NN-ARX is trained based on the collected data from the system, and the new trajectory data is used to validate both methods. By considering the results of experimental implementations, three servo motors are demonstrated to have different dynamical behavior which was expected to happen from the outset, due to uncertainty in fabrication of motors component and gearbox. In the identification and prediction stages, the Root Mean Square Errors (RMSE) index is used to validate and analyze the performance of each method using the validation data from new trajectories. In terms of predicting the output of the system, NN-ARX performed better than ARMAX with RMSE of 0.001441, compared to ARMAX with RMSE of 0.0886. Due to the high accuracy of the obtained models. thus they can be used in the design of motion controllers and modelling disturbances in the system.

关键词： delta parallel robot Actuation Identification ARMAX Dynamic Neural Networks NN-ARX

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Novel Nonlinear Hypothesis for the delta parallel robot Modeling

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IEEE ACCESS 2020年 8卷 46324-46334页

作者： Aquino, Gustavo Rubio, Jose De Jesus Pacheco, Jaime Gutierrez, Guadalupe Juliana Ochoa, Genaro Balcazar, Ricardo Cruz, David Ricardo Garcia, Enrique Novoa, Juan Francisco Zacarias, Alejandro Inst Politecn Nacl ESIME Azcapotzalco Secc Estudios Posgrad & Invest Mexico City 02250 DF Mexico

In previous investigations, the nonlinear hypothesis use the linear bounded maps. Nonlinear hypothesis are described as the combination of the first order terms, and after of the mentioned combination, one bounded map is applied to alter the result. This document proposes two nonlinear hypothesis which use different structures instead of using the linear bounded maps. They are termed as novel nonlinear hypothesis and second order nonlinear hypothesis and their goal is to improve the second order processes modeling. The proposed nonlinear hypothesis are described as the combination of the first order and second order terms. Since the delta parallel robot is a second order process, it is an excellent platform to prove the effectiveness of the two proposed hypothesis.

关键词： Novel nonlinear hypothesis second order nonlinear hypothesis nonlinear hypothesis delta parallel robot

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Robust Control for Nonlinear delta parallel robot With Uncertainty: An Online Estimation Approach

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IEEE ACCESS 2020年 8卷 97604-97617页

作者： Zhao, Ruiying Wu, Linlin Chen, Ye-Hwa Changan Univ Natl Engn Lab Highway Maintenance Equipment Xian 710065 Peoples R China Georgia Inst Technol George W Woodruff Sch Mech Engn Atlanta GA 30332 USA

A series of fractional robust trajectory tracking controls are proposed for the delta parallel robot with uncertainty. For the high speed and heavy load, the delta parallel robot could not ignore the influences of the high nonlinearity (by the dynamics of the multiple closed-loops mechanism and the nonlinear joints friction) and the various kinds of uncertainties (i.e., the unknown dynamic parameters and external disturbances caused by the residual vibration). By formulating the motion equation of the delta parallel robot, the nonlinearity is settled by a norm model based control design. The uncertainty considered in the paper is time-varying but unknown. An online estimation with an exponential type leakage term and dead-zone is construct to investigate the realtime information of the uncertainty. In virtue of the estimated information, two fractional robust trajectory tracking controls with the joints friction compensation are designed. Under the proposed controls, the system performance of the delta parallel robot can be deterministically guaranteed (which includes uniform boundedness and uniform ultimate boundedness).

关键词： parallel robots Friction Uncertainty Kinematics Estimation Manipulators Robust control delta parallel robot fractional robust control trajectory tracking online estimation

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Safety analysis via forward kinematics of delta parallel robot using machine learning

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SAFETY SCIENCE 2019年第7期117卷 243-249页

作者： Liu, Cheng Cao, Guohua Qu, Yongyin Changchun Univ Sci & Technol Coll Mech & Elect Engn Changchun Jilin Peoples R China Beihua Univ Coll Elect & Informat Engn Jilin Jilin Peoples R China

Aiming at solving the problems of complex forward kinematics of delta parallel robot and multiple solutions, and further improving celerity and accuracy of positioning for the spatial pose of the manipulator end effecter, in this paper, we present a method to kinematics solution of delta parallel robot based on BP neural network. Taking the three-degree-of-freedom delta parallel robot as the research object, according to analysis of its the kinematics principle, the basic BP neural network model and the optimized BP neural network model for kinematics solution of delta parallel robot are simulated by using MATLAB, respectively. The results indicate that using BP neural network model improved by Genetic Algorithms to address the forward kinematics problems of delta parallel robot is feasible, which can achieve the requirement of higher celerity and accuracy of delta parallel robot control and avoid the shortcomings of traditional methods to a certain extent, furthermore, ensures the reliability of production safety.

关键词： Forward kinematics delta parallel robot BP neural network Genetic algorithms

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