Research on parallel nonlinear control system of PD and RBF neural network based on U model

Fei, Xu; Tang, Deqiang; Wang, Shanshan

doi:10.1080/00051144.2020.1731227

Cited by 11 publications

(4 citation statements)

References 17 publications

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“…Following the principles of position-based robotic arm end-effector impedance control, a PID algorithm is integrated into the end-effector of the robotic arm to handle end-effector forces (Xu 2020). The impedance control equation is formulated as:…”

Section: Outer Loop Designmentioning

confidence: 99%

End-Effector Impedance Control of Robotic Arm Based on Enhanced Neural Network RBF-PID-PSO

Wu,

Zhang,

Zhang

et al. 2023

Preprint

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In the current calibration system for power transformers, In the current calibration systems for electrical power transformers, errors in the insertion and extraction of wiring terminals occur due to the reliance solely on camera-based positioning of the terminal's location by the robotic arm. This approach falls short in achieving precise assembly of various transformer terminals. Therefore, it is imperative to introduce active compliant control between the robotic arm's end-effector and the wiring terminal to enable smooth terminal assembly. This paper proposes a mechanical arm end-effector impedance control algorithm based on RBF-PID-PSO for terminal assembly. The algorithm employs a particle swarm optimization technique to adjust the weight matrix and center parameters within the Radial Basis Function (RBF) neural network. Simultaneously, a PID algorithm is introduced to optimize impedance control for end-effector forces. The impedance parameters obtained from the RBF neural network and the PID parameters are incorporated into the robotic arm model. This allows for the conversion of end-effector forces into position errors for impedance control, followed by the transformation of these errors into six joint angles through the robot's inverse kinematics. This ultimately achieves impedance control of end-effector forces. Comparative experiments and simulations conducted in Matlab demonstrate that this controller effectively reduces errors in force and torque control. Furthermore, it exhibits superior resistance to disturbances in force feedback signals, significantly improving compliant control performance.

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Section: Outer Loop Designmentioning

confidence: 99%

End-Effector Impedance Control of Robotic Arm Based on Enhanced Neural Network RBF-PID-PSO

Wu,

Zhang,

Zhang

et al. 2023

Preprint

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“…Second, initialize the network parameter, assign the weight and initialization of the connection between the network access layer and the encryption process, and set the weight and initialization of the connection between the encryption process and the encryption process output by random number [22].…”

Section: Application Of Artificial Neural Network In Financialmentioning

confidence: 99%

Internal Control Model of Enterprise Financial Management Based on Market Economy Environment

2022

Advances in Multimedia

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In order to better solve the problems existing in the internal environment, risk assessment, and internal supervision of enterprise financial management, this paper proposes a method of enterprise financial analysis and control model based on artificial neural network algorithm. The results show that, through the internal control model and management optimization of financial management, a large number of calculations and subjective affairs in the process of financial work can be avoided to the greatest extent. For enterprises, a large number of human, material, and financial resources can be saved for data collection, which is more conducive to improving the enterprise financial management system, improving the quality of financial information, providing technical support for enterprise decision-making, and further reducing the risk in the process of enterprise operation.

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“…According to the references, we can find that the RBF neural network control algorithm basically uses the Lyapunov function to determine the stability conditions. In [47], based on the nonlinear U model, RBF neural network and PD parallel control algorithm are proposed. e Lyapunov function determines the conditions of system stability, and under this condition, the tracking effect has been improved.…”

Section: Introductionmentioning

confidence: 99%

Neural Network Supervision Control Strategy for Inverted Pendulum Tracking Control

Gao

et al. 2021

Discrete Dynamics in Nature and Society

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This paper presents several control methods and realizes the stable tracking for the inverted pendulum system. Based on the advantages of RBF and traditional PID, a novel PID controller based on the RBF neural network supervision control method (PID-RBF) is proposed. This method realizes the adaptive adjustment of the stable tracking signal of the system. Furthermore, an improved PID controller based on RBF neural network supervision control strategy (IPID-RBF) is presented. This control strategy adopts the supervision control method of feed-forward and feedback. The response speed of the system is further improved, and the overshoot of the tracking signal is further reduced. The tracking control simulation of the inverted pendulum system under three different signals is given to illustrate the effectiveness of the proposed method.

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Research on parallel nonlinear control system of PD and RBF neural network based on U model

Cited by 11 publications

References 17 publications

End-Effector Impedance Control of Robotic Arm Based on Enhanced Neural Network RBF-PID-PSO

End-Effector Impedance Control of Robotic Arm Based on Enhanced Neural Network RBF-PID-PSO

Internal Control Model of Enterprise Financial Management Based on Market Economy Environment

Neural Network Supervision Control Strategy for Inverted Pendulum Tracking Control

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