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Control of Linear Servo Pneumatic Drive Based on Fuzzy Controller and Knowledge Base
Khaziev E.L.
pneumatic actuator based on fuzzy logic with the ultimate task of improving the quality of control
Automatic routing submarine robots using fuzzy logic
Alavi, S. E.
It is known that the fuzzy control rules for a control system is always built by designers
Fuzzy logic application approach in control of automatic spacecraft
Drozdov A.N., Drozdov N.A., Narozhnyy I.M.
with the desired properties, which is based on fuzzy controllers. The main method of which is fuzzy logic and soft
Model predictive control for Takagi–Sugeno fuzzy model-based Spacecraft combined energy and attitude control system
Aslam S., Chak Y.-C., Hussain Jaffery M., Varatharajoo R., Ahmad Ansari E.
to improve the attitude accuracy. In this maiden work, the Fuzzy–MPC controller is introduced to regulate
Development of automated test system for diesel engines based on fuzzy logic
Galiullin L.
© 2016 IEEE.To control a diesel engine during testing, the principles of fuzzy output, which
Genetic programming elements in fuzzy logic problems
Malyuk J.A.
The method of fuzzy control system parameters synthesis and adjustment is given. The method uses
Fussy Partial Differential Equation in the Task of Control
Mochalov I.A., Khrisat M.S., Shihab Eddin M.Y.
. In this connection, is formed and solved one-point boundary value problem on the synthesis of fuzzy optimal control
Fuzzy Differential Equations in Control. Part II
Mochalov I.A., Khrisat M.S., Shihab Eddin M.Y.
The initial value problem of Formulated fuzzy is obtained from a clear initial problem in which
Deep learning based fuzzy-MPC controller for satellite combined energy and attitude control system
Aslam S., Chak Y.-C., Jaffery M.H., Varatharajoo R., Razoumny Y.
-Learning capability of the fuzzy-Model Predictive Control (FMPC) controller for CEACS. The design approach
Method of reducing the computational complexity of fuzzy inference algorithms for implementation on a microcontroller with limited computational resources
Zhartybayeva, M. G., Tatur, M. M., Shaverdo, M. M., Iskakov, K. T., Татур, М. М.
In our work we suggest the most effective ways to reduce the complexity of computing fuzzy inference

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