Adaptive PID Control and Its Application Based on a Double-Layer BP Neural Network
Ming-Li Zhang,Yi-Jie Zhang,Xiao-Long He,Zhengjie Gao +3 more
- Vol. 9, Iss: 8, pp 1475
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TLDR
The results showed that the proposed method can provide a theoretical and experimental basis for the selection of control parameters, and can be extended to similar controllers, therefore possessing engineering application value.Abstract:
In this paper, focusing on the inconvenience of variable value PID based on manual parameter adjustment for the hydraulic drive unit (HDU) of a legged robot, a method employing double-layer back propagation (BP) neural networks for learning the law of PID control parameters is proposed. The first layer is used to learn the relationship between different control parameters and the control performance of the system under various working conditions. The second layer is used to study the relationship between the parameters of the working conditions and the optimizing control parameters under various working conditions. The effectiveness of the proposed control method was verified by simulation and experiment. The results showed that the proposed method can provide a theoretical and experimental basis for the selection of control parameters, and can be extended to similar controllers, therefore possessing engineering application value.read more
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TL;DR: In this paper, the authors presented the design of the hydraulically actuated quadruped robot HyQ2Max, which is an evolution of the 80 kg agile and versatile robot HQ. Compared to HQ, the new robot needs to be more rugged, more powerful and extend the existing locomotion skills with self-righting capability.
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Quantification of interfacial energies associated with membrane fouling in a membrane bioreactor by using BP and GRNN artificial neural networks
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