The paper considers the issues of synthesizing an adaptive fuzzy synergetic controller with discrete time for nonstationary nonlinear dynamic objects. The proposed approach is based on the integrated use of synergetic control principles and fuzzy logic methods, which ensure the formation of a control law for nonlinear dynamic objects that provides asymptotic stability of the control system. Such a hybrid combination makes it possible to guarantee the asymptotic stability of the closed-loop system and to shape the required dynamic behavior of the object over a wide range of operating modes. In addition, this approach provides the ability to adapt to uncertainties and changes in external influences due to the self-organization properties of the method and the real-time adjustment of parameters, while maintaining the required quality of control under variations in the characteristics of the object and its environment. Thus, the proposed hybrid approach forms a universal methodological basis for building adaptive control systems that are robust to disturbances and possess a high degree of flexibility under conditions of information uncertainty. To compensate for uncertainties in the object states and disturbances, the use of a Mamdani-type neural network model is proposed, which is characterized by simplicity and convenience in practical implementation. The synthesized control law has an analytical form, which significantly expands the possibilities of its implementation on microcontrollers. A comparative analysis of the obtained results with known methods was carried out, demonstrating their effectiveness.
Sidikov et al. (Tue,) studied this question.