Adaptive Takagi-Sugeno fuzzy model and model predictive control of pneumatic artificial muscles
Source abstract
Abstract
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.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Prosthetics and Rehabilitation Robotics · Hydraulic and Pneumatic Systems · Muscle activation and electromyography studies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (44)
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