DC motor controller with neuro-adaptive fuzzy control logic

The development of motor drive controllers is an urgent task in industrial and robotic manipulators. DC motor controllers (DCT) can be used in various tasks, as their use provides flexible opportunities for the development of control algorithms. Many well-known controllers that use feedback cannot maintain system performance at an acceptable level. The paper proposes the development of a new DCT controller, which allows to increase the efficiency of its control by reducing tn (rise time), σ (overshoot) and tp (regulation time) when compared with other known controllers (proportional-integral-differential (PID) controller and fuzzy controller (NL) logic). The article discusses the development of a controller based on an adaptive system with neuro-fuzzy logic (ASNL) to effectively control the speed of the DCT with the load. The mathematical model of the developed controller in the environment of Matlab-Simulink is implemented. Comparison of the proposed controller with other known controllers is performed according to the following criteria: tn, tp and σ. To confirm the effectiveness of the proposed ASNL controller, it was compared with known controllers. The simulation results showed that the developed ASNL controller reduces tn compared to the PID controller by 6% and compared to the NL controller by 17%, tp compared to the PID controller by 37% and compared to the NL controller by 17 %, and σ compared to the PID controller by 6% with the load.

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