Open AccessModified inverse neural controller using adaptive gain factor for DC motor
Author(s) -
Ali Reyadh Waheed,
Abbas H. Issa,
Mohammed Y. Hassan
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/881/1/012123
Subject(s) - control theory (sociology) , settling time , overshoot (microwave communication) , controller (irrigation) , pid controller , computer science , nonlinear system , open loop controller , signal (programming language) , tracking error , electronic speed control , dc motor , control engineering , engineering , step response , control (management) , artificial intelligence , temperature control , telecommunications , agronomy , physics , electrical engineering , closed loop , quantum mechanics , biology , programming language
Disturbances and input changes effects lead to condition of error in the action integrity of control systems especially when the system is nonlinear. Adaptive controllers are being used to solve this problem. However they may add cost and complexity to the system design, and may not give the required action. This paper presents a DC motor speed controller using Inverse Neural controller and adaptive gain factor. The adaptive gain factor designed to be a function of the error signal (will be used to cancel the error in the controller signal), also a positive feedback from the output (to overcome the error of the plant) will be added to the adaptive gain factor signal to create a new input to the plant. Simulation results proved that the proposed method excellently controls the DC motor speed and evicts the steady-state response error thus; makes the output correctly tracking the desired input with minimum rising time, reduced peak time and settling time, and minimum peak overshoot, with average enhancement of 50% for each versus the results of PID controller for the same plant and same inputs. MATLAB R2015b, SIMULINK simulation has been used to simulate the system and obtain the results.