Double Closed-Loop Controller Design of Brushless DC Torque Motor Based on RBF Neural Network

Abstract

For the problem of that the traditional brushless DC torque motor controller position servo system is difficult to track accurately the complex and varied location information, an improved double closed-loop controller is designed in this paper. A self-adaptive PID algorithm based on RBF neural network is employed to design position loop controller and a feed-forward control algorithm is used in the speed loop controller. The improved double closed-loop controller validated the feasibility of the new method for the design of brushless DC torque motor position servo system

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

“…The advantage of this system is that the torque of the motor can be controlled in real-time based on the difference between the given speed and the actual speed. When the difference between given and actual speeds is relatively large, the motor torque increases and the speed changes faster to drive the motor speed toward the given value as quickly as possible, which makes for rapid speed regulation 7,8,9 . Conversely, when the speed is relatively close to the given value, it can automatically reduce the torque of the motor to avoid excessive speed, allowing the speed to achieve the given value relatively quickly with no error 6,10 .…”