Al Mahturi, A.Santoso, F.Garratt, M.A.Anavatti, S.G.2025-12-182025-12-182020IEEE Conference on Evolving and Adaptive Intelligent Systems, 2020, iss.9122771, pp.1-69781728143842https://hdl.handle.net/11541.2/145249This 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.enCopyright 2020 IEEEself-evolving fuzzy controllermodel-free controlleruncertaintiesadaptive controlConference paper10.1109/EAIS48028.2020.91227712-s2.0-85088115347