【摘 要】
：
Pneumatic artificial muscles(PAMs)usually exhibit strong hysteresis nonlinearity and time-varying features that bring PAMs modeling and control difficulties.To characterize the hysteresis relation between PAMs\'displacement and fluid pressure,a long sho
【机 构】
：
School of Artificial Intelligence,University of Chinese Academy of Sciences,Beijing 100049,China;Sta
论文部分内容阅读
Pneumatic artificial muscles(PAMs)usually exhibit strong hysteresis nonlinearity and time-varying features that bring PAMs modeling and control difficulties.To characterize the hysteresis relation between PAMs\'displacement and fluid pressure,a long short term memory(LSTM)neural network model and an adaptive Takagi-Sugeno(T-S)fuzzy model are proposed.Experiments show that both models perform well under the load free conditions,and the adaptive T-S Fuzzy model can furtherly adapt to the change of load with the online adaptation ability.With the concise expression and satisfactory performance of the adaptive T-S Fuzzy model,a model predictive controller is designed and tested.Experiments show that the model predictive controller has a good performance on tracking the given references.
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