Abstract
For problems of complex algorithm, multi-parameter and labor-intensive and time-consuming adjustment in the active disturbance rejection controller(ADRC), on the basis of optimizing the structure of ADRC, a compound ADRC based on radical basis function(RBF) neural network was designed. The controller obtained the online adjustment information of ADRC parameters by using RBF neural network to track the controlled object online, so that the parameters of the ADRC can be automatically adjusted. The method was applied in the three-motor synchronous control system which combined with S7-300 PLC to build an experimental platform and adopted PLC programming language for algorithm implementation in order to perform experiments of the speed control. The results show that the method can achieve the self-adjusting function of partial parameters and can decrease overshoot to the lowest and even realize speed regulation without overshoot. It can also improve the dynamic performance and steady-state accuracy of the system. The experimental results show that the method has practical application.
Original language | English |
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Pages (from-to) | 108-116 |
Number of pages | 9 |
Journal | Dianji yu Kongzhi Xuebao/Electric Machines and Control |
Volume | 22 |
Issue number | 2 |
DOIs | |
Publication status | Published - 1 Feb 2018 |
Externally published | Yes |
Keywords
- Active disturbance rejection controller
- Neural network
- Parameters
- Radical basis function
- Self-adjusting