Adaptive Takagi-Sugeno fuzzy model and model predictive control of pneumatic artificial muscles

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.