A simplified model-free self-evolving TS fuzzy controller for nonlinear systems with uncertainties
Date
2020
Authors
Al Mahturi, A.
Santoso, F.
Garratt, M.A.
Anavatti, S.G.
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Conference paper
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IEEE Conference on Evolving and Adaptive Intelligent Systems, 2020, iss.9122771, pp.1-6
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12th IEEE International Conference on Evolving and Adaptive Intelligent Systems, EAIS 2020 (27 May 2020 - 29 May 2020 : Bari, Italy)
Abstract
This paper proposes a self-evolving Takagi-Sugeno fuzzy controller for nonlinear systems with uncertainties. The self-evolving framework is used to add and prune fuzzy rules in an online manner. Our proposed nonlinear controller is model-free and does not depend on the plant dynamics. All adjustable fuzzy parameters are tuned using a sliding surface, which is derived from the gradient descent learning method to minimize the error between the desired and the actual signals. Unlike most of the existing self-evolving controllers, where a hybrid technique is required to determine the control action, our proposed algorithm is able to construct the final control signal, which can be fed directly to control a nonlinear system. The tracking performance of our proposed controller is validated and compared with an adaptive model-based fuzzy controller in the presence of external disturbances, where better tracking results are obtained from our proposed controller.
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Copyright 2020 IEEE