Trajectory Tracking Control of Robot Manipulators Based on U-Model

Ma, Xianghua; Zhao, Yang; Di, Yiqun

doi:10.1155/2020/8314202

Cited by 7 publications

(8 citation statements)

References 21 publications

(17 reference statements)

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“…[52][53][54] A new method of trajectory tracking control is proposed to improve tracking speed of the manipulator. 55 A finite-time trajectory controller is proposed. 56 A radial basis function NN is used to estimate an uncertain model of the spatial manipulator.…”

Section: Related Workmentioning

confidence: 99%

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Manipulator trajectory tracking based on adaptive sliding mode control

Zhao

Tao

et al. 2022

Concurrency and Computation

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A manipulator is a complex electromechanical system that is nonlinear, strongly coupled, and uncertain. Achieving its precise and high-quality trajectory control is difficult.Sliding mode control (SMC) is one of the common control methods for manipulators.However, discontinuities in SMC can cause jitter and vibration in the manipulator system, leading to a reduction in the performance of the control system. For the self-adaptive capability jitter vibration problem of SMC, the Dobot magician manipulator is treated as the research object in this article. The dynamics equations of the manipulator are established by Lagrange method, and a simplified model of the manipulator dynamics is constructed. The method of self-adaptive sliding mode control is proposed. Self-adaptive parameters are added to the SMC to achieve self-adaptive adjustment of the SMC parameters. In the MATLAB/Simulink simulation environment analysis show that the self-adaptive SMC method has better self-tuning ability and trajectory tracking ability than the traditional SMC, and weakens the jitter phenomenon existing in the traditional SMC.

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Section: Related Workmentioning

confidence: 99%

“…A new method of trajectory tracking control is proposed to improve tracking speed of the manipulator 55 . A finite‐time trajectory controller is proposed 56 .…”

Section: Related Workmentioning

confidence: 99%

Manipulator trajectory tracking based on adaptive sliding mode control

Zhao

Tao

et al. 2022

Concurrency and Computation

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“…Ma et al introduced the U-model method to relieve the requirement of dynamic mathematical model and simplify the design of the trajectory tracking controller of the manipulator. However, there are certain errors in this method, resulting in insufficient accuracy [2]. Peng et al proposed a controlled DAE-based optimal control (IOC) method.…”

Section: Related Workmentioning

confidence: 99%

“…The innovations of this paper are as follows: (1) It uses artificial intelligence technology to track the displacement trajectory of the agricultural picking robot, control the moving direction and speed of the picking robot, and improve the picking efficiency. (2) The change matrix is used to describe the pose, which simplifies the process of robot displacement planning. (3) Combined with the theory of iterative operation, the axis error is used to mediate and improve the tracking of the displacement trajectory.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence in Agricultural Picking Robot Displacement Trajectory Tracking Control Algorithm

Hua-ying

2022

Wireless Communications and Mobile Computing

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With the development and leap of artificial intelligence technology, more and more robots have penetrated into all walks of life. Today’s agriculture is changing in the direction of modernization and automation. On the one hand, because of rising labor costs, it cannot afford to consume a large amount of labor for agricultural operations. On the other hand, the population is growing rapidly, and traditional agricultural production, picking, and other links have been unable to keep pace with the development of the times. Therefore, it is very necessary to use artificial intelligence technology to transform traditional agriculture. The purpose of this paper is to use artificial intelligence technology to plan, track, and optimize the displacement trajectory of the agricultural picking robot, so as to improve the working efficiency of the picking robot. In this paper, the neural network, the D-H modeling method of the manipulator, and the forward and reverse motion of the manipulator are explained and analyzed, and based on the relevant algorithms of neural network, the manipulator is modeled, and then the, forward and reverse motion of the manipulator is analyzed in detail, and the digital model of the picking robot is constructed. Then, the angle and motion speed of each joint of the robot are analyzed to reduce the motion trajectory error caused by friction and other factors. Then, the simulation experiment of the displacement trajectory tracking control is carried out, and the linear trajectory motion and the arc trajectory motion are deeply analyzed, and the axis error is greatly reduced after 6 iterations. Finally, the displacement trajectory is optimized. The optimized total movement time is shortened by 6.84 seconds, which enables the picking robot to not only ensure work efficiency but also accurately complete the planned displacement trajectory. After continuous experiments on the algorithm model and the picking robot, the actual trajectory of the picking robot at 0.7 seconds can be expected. The trajectories are completely coincident, indicating that the neural network plays a very important role in the trajectory research of picking robots.

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“…The U model is applied to an electrically excited synchronous motor, and an anti-interference controller is designed on the basis of the U model [20]. In order to improve the robot's trajectory tracking speed, a U-model-based trajectory tracking control method is proposed, which also alleviates the requirement of dynamic mathematical model and simplifies the design of the robot trajectory tracking controller [21]. A new U-model-based sliding mode augmented control method for controlling a class of single input single output (SISO) dynamic systems with internal uncertain parameters and external system control noise/disturbance is proposed [22].…”

Section: Introductionmentioning

confidence: 99%

Research on parallel control of CMAC and PD based on U model

Fei

Zhang

et al. 2021

Automatika

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In this paper, the nonlinear U model with time-varying coefficients is investigated and the transformation of the nonlinear model is accomplished by the Newton iterative algorithm. Based on the nonlinear U model, a control algorithm with cerebellar model articulation controller and proportional derivative (PD) in parallel is proposed. The algorithm learns online through a neural network while optimizing the output of the PD, which ultimately enables the actual output of the system to track up to the desired output. Considering that the nonlinear object has the characteristic of rapid change with time, the article improves the PD algorithm to nonlinear PD control algorithm to complete the design of the system. The algorithm automatically adjusts the weights according to the error magnitude to complete the controller parameter adjustment, thus reducing the error of the system. The simulation results show that the nonlinear PD algorithm is better than the PD algorithm, meanwhile, the tracking speed and control precision of the system are improved.

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Trajectory Tracking Control of Robot Manipulators Based on U-Model

Cited by 7 publications

References 21 publications

Manipulator trajectory tracking based on adaptive sliding mode control

Manipulator trajectory tracking based on adaptive sliding mode control

Artificial Intelligence in Agricultural Picking Robot Displacement Trajectory Tracking Control Algorithm

Research on parallel control of CMAC and PD based on U model

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