Research on a hybrid controller combining RBF neural network supervisory control and expert PID in motor load system control

Gao, Hongliang; Xiong, Lang

doi:10.1177/16878132221109994

Cited by 7 publications

(4 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The PID controller (Liu et al, 2020;Liu and Sui, 2021;Gao and Xiong, 2022) is inherently a single-input singleoutput system. However, in the case of a robotic arm with six The motion trajectory of the six-axis robotic arm.…”

Section: When |E (K)| ⩽ ε(Precision)mentioning

confidence: 99%

Control and physical verification of 6-DOF manipulator for power inspection robots based on expert PID algorithm

Jiang,

Wang,

Liu

2024

Front. Energy Res.

View full text Add to dashboard Cite

To enhance the performance of power inspection robots in intricate nuclear power stations, it is necessary to improve their response speed and accuracy. This paper uses the manipulator of the power inspection robot as the primary research object, and unlike previous control algorithm research, which only remained in the software simulation stage, we constructed a set of physical verification platforms based on CAN communication and physically verified the robotic arm’s control algorithm. First, the forward motion model is established based on the geometric structure of the manipulator and D-H parameter method, and the kinematic equation of the manipulator is solved by combining geometric method and algebraic method. Secondly, in order to conduct comparison tests, we designed PID controllers and expert PID controllers by utilising the expertise of experts. The results show that compared with the traditional PID algorithm, the expert PID algorithm has a faster response speed in the control process of the manipulator. It converges quickly in 0.75 s and has a smaller overshoot, with a maximum of only 6.9%. This confirms the expert PID algorithm’s good control effect on the robotic arm, allowing the six-degree-of-freedom robotic arm to travel more accurately and swiftly along the trajectory of the target point.

show abstract

Section: When |E (K)| ⩽ ε(Precision)mentioning

confidence: 99%

Control and physical verification of 6-DOF manipulator for power inspection robots based on expert PID algorithm

Jiang,

Wang,

Liu

2024

Front. Energy Res.

View full text Add to dashboard Cite

show abstract

“…Neural networks are the most popular artificial intelligence technology. Because they can adjust themselves according to input/output mapping, neural network models have outstanding advantages in solving complex nonlinear problems [30]. Therefore, an adaptive PID controller was proposed to improve the dynamic performance of the PMLSM servo system by introducing a BP neural network model into the parameter optimization process of the PID controller.…”

Section: Design Of the Adaptive Pid Controllermentioning

confidence: 99%

Adaptive PID controller of permanent magnet linear synchronous motor based on particle swarm neural network

Yang

Fan

2023

IEICE Electron. Express

View full text Add to dashboard Cite

To improve the dynamic response performance and robustness of a permanent magnet linear synchronous motor (PMLSM)-based servo system, an adaptive proportional-integralderivative (PID) controller based on a particle swarm optimization neural network is proposed. First, according to the mechanical dynamics equation of the PMLSM, a mathematical model of the PMLSM was established. Second, an adaptive PID speed controller is designed to realize real-time control of the PMLSM. To improve the dynamic performance and stability of the controller, a particle swarm optimization neural network is used to dynamically tune the parameters. Finally, the effectiveness of the proposed controller was verified on the MATLAB/Simulink simulation platform. Compared to the traditional PID controller, the adaptive PID controller can improve the dynamic performance of the system more effectively.

show abstract

“…Simulation results show the superiority of the proposed method. Gao and Xiong 16 designed a control strategy combining RBF neural network supervisory control and expert PID control to ensure the stability of the motor load system and improve the performance of the system.…”

Section: Introductionmentioning

confidence: 99%

Parameter optimization of nonlinear PID controller using RBF neural network for continuous stirred tank reactor

Shi

Zhao

Zheng

2023

Measurement and Control

View full text Add to dashboard Cite

The temperature system of the Continuous Stirred Tank Reactor (CSTR) has the characteristics of strong nonlinearity and uncertain parameters. The linear PID controller makes it difficult to meet CSTR’s control requirements. Nonlinear PID (NPID) can improve the control effect of nonlinear controlled objects, but due to the influence of nonlinear function selection and manual parameter setting, when parameters are uncertain or subject to external interference, the control performance of the system will decrease. To improve the adaptive capability of the NPID controller, the RBF-NPID control algorithm is proposed. The learning ability of RBF neural network is used to adjust NPID parameters online to improve the control performance of the system. In order to verify the effectiveness of the proposed algorithm, a CSTR model was established in MATLAB and algorithm comparison research was carried out. Simulation results show the effectiveness and superiority of the proposed algorithm.

show abstract

Research on a hybrid controller combining RBF neural network supervisory control and expert PID in motor load system control

Cited by 7 publications

References 41 publications

Control and physical verification of 6-DOF manipulator for power inspection robots based on expert PID algorithm

Control and physical verification of 6-DOF manipulator for power inspection robots based on expert PID algorithm

Adaptive PID controller of permanent magnet linear synchronous motor based on particle swarm neural network

Parameter optimization of nonlinear PID controller using RBF neural network for continuous stirred tank reactor

Contact Info

Product

Resources

About