Stable adaptive probabilistic Takagi-Sugeno-Kang fuzzy controller for dynamic systems with uncertainties

In this study, an adaptive probabilistic Takagi-Sugeno-Kang fuzzy PID (APTSKF-PID) scheme is developed to control nonlinear systems. The proposed controller merges the features of the TSK fuzzy logic system, which possess a superior performance in system size and learning accuracy than the Mamdani-type fuzzy systems and the probabilistic processing method in nonlinear control, which handles the system uncertainties. To achieve controlled system stability, Lyapunov function is used for tuning the controller parameters. Tuning the probability parameters provides an extra degree of flexibility in controller design and improves the control performance. Furthermore, to ensure the effectiveness of the developed scheme for engineering applications, the proposed control technology is introduced to control nonlinear dynamical plants and its performance is compared with existing schemes. Simulation tasks indicate that the efficiency of APTSKF-PID scheme has high superiority over the other controller for external disturbances, random noise and a large scope of system uncertainties. (C) 2019 ISA. Published by Elsevier Ltd. All rights reserved.