Predictive Control of Pneumatic Artificial Muscle Antagonist Joint Angle Tracking Model Based on BP Neural Network Modeling

Qing-tao ZONG; Bin-rui WANG; Tai CHENG; Bin ZHANG; Dan LIU; Tao LIU; Jun-wei FANG; Kun ZHOU

doi:10.12404/j.issn.1671-1815.2505642

Science Technology and Engineering ›› 2026, Vol. 26 ›› Issue (23) : 10031-10040 DOI: 10.12404/j.issn.1671-1815.2505642

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Predictive Control of Pneumatic Artificial Muscle Antagonist Joint Angle Tracking Model Based on BP Neural Network Modeling

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Abstract

To improve the trajectory tracking accuracy of the pneumatic artificial muscle (PAM) antagonistic joint system, a predictive control method based on a backpropagation neural network (BPNN) was proposed. First, based on the dynamic analysis of the PAM antagonistic system, a neural network model integrating the error angle and error angular velocity was constructed using BPNN and discretized to establish a predictive model. Second, a BPNN-based model predictive controller (BPNNMPC) was designed based on this predictive model, by which high-precision joint angle control was achieved through rolling optimization. Finally, comparative simulations and physical experiments were conducted to validate the superiority of the proposed method. It is demonstrated by the results demonstrate that the trajectory tracking performance is effectively enhanced by the BPNNMPC.

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Keywords

pneumatic muscle / BP neural network model / model predictive control / trajectory tracking

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Qing-tao ZONG, Bin-rui WANG, Tai CHENG, Bin ZHANG, Dan LIU, Tao LIU, Jun-wei FANG, Kun ZHOU. Predictive Control of Pneumatic Artificial Muscle Antagonist Joint Angle Tracking Model Based on BP Neural Network Modeling. Science Technology and Engineering, 2026, 26(23): 10031-10040 DOI:10.12404/j.issn.1671-1815.2505642