Synthesis of an adaptive synergistic fuzzy discrete proceedings controller for nonlinear systems

The article presents a method for developing a synergistic adaptive regulator for dynamic systems with indefinite nonlinearity and time-varying characteristics. The main task is to create a controller capable of adjusting its parameters in response to changes in an object’s properties under a changing environment. The controller synthesis combines a synergistic control approach with fuzzy logic, providing asymptotic stability and the required dynamic control characteristics, with the ability to adapt to varying object parameters. The synergistic control guarantees the stability and asymptotic reliability of the control system and enables the use of both typical and atypical control laws. To overcome the difficulties associated with the uncertainties in the states of objects, a Mamdani-type neural network model is proposed. A sigmoidal function is used as the membership function, which is distinguished by the simplicity of implementation and the possibility of differentiating input variables. The resulting control law has an analytical dependence, which significantly increases its implementation possibilities on microcontrollers. The imitation achievement by example demonstrated the effectiveness of the proposed method relative to known methods.

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