Variable Structure Control Strategy Research on Regenerative Braking for a Brushless DC Motor Driven Electric Bus Cruising Downhill

2014 ◽  
Vol 13 (04) ◽  
pp. 223-236 ◽  
Author(s):  
Guo-Zhu Zhao ◽  
Xing Peng
Keyword(s):  
Control System ◽  
Control Method ◽  
Variable Structure ◽  
Dc Motor ◽  
Constant Speed ◽  
Regenerative Braking ◽  
Brushless Dc Motor ◽  
Structure Control ◽  
Brushless Dc ◽  
Electric Bus

To use regenerative braking to act as an auxiliary brake to maintain the constant speed of a brushless DC motor driven electric bus (BDCMEB) on downhill based on the feature of double-loop control structure of the control method for electric vehicle traction motor and the variable structural characteristics of PWM Control System for brushless DC Motor. A double-manifold variable structure control method to control regenerative braking is proposed for the bus cruising downhill. The impact of lead-acid battery's charge acceptance ability over a long charging period on the regenerative braking force of a driving motor is analyzed. Dynamic model of the bus on long downhill is established. A double-manifold variable structure controller is designed for the DCMEB on long downhill. The simulation result shows that the control system maintains enough stability and strong robustness. It may be achieved for the bus to maintain a constant speed downhill only by regenerative braking on a smaller slope. But the dynamic process is very slow. When deceleration or a constant speed is desired on a larger slope, only by electro mechanical parallel braking can the bus track the target speed precisely and quickly.

Single Neuron PID Adaptive Control for BLDCM Based on Algebraic Algorithm

2012 ◽  
Vol 220-223 ◽  
pp. 851-854
Author(s):  
Yan Diao ◽  
Hong Ping Jia ◽  
Tian Jun Geng
Keyword(s):  
Adaptive Control ◽  
Motor Control ◽  
Control System ◽  
Pid Control ◽  
Control Method ◽  
Dc Motor ◽  
Brushless Dc Motor ◽  
Brushless Dc ◽  
Dc Motor Control ◽  
Motor Control System

The brushless DC motor control system often adopts the classic PID control, the advantages of which are as follows: simple to control, easy to adjust the parameter and a certain degree of control precision. But it relies on accurate mathematical model. The permanent magnet brushless DC motor control system is a multi-variable and nonlinear. As to the deficiencies of the classic PID control method, this thesis proposes a method called artificial neural network PID adaptive control method, which is based on algebraic algorithm and overcomes the shortcomings such as the slow convergence of BP algorithm, easy to trap in local minimum, and etc.

scholarly journals PID Control System on Brushless DC Motor for Quadcopter Balance

Jurnal INFORM ◽  
2021 ◽  
Vol 6 (2) ◽  
pp. 110-114
Author(s):  
Mochamad Mobed Bachtiar ◽  
Fernando Ardilla ◽  
Muhammad Faiz Hasbi ◽  
Iwan Kurnianto Wibowo
Keyword(s):  
Pid Control ◽  
Control Method ◽  
Dc Motor ◽  
Unmanned Aircraft ◽  
Integrated System ◽  
Brushless Dc Motor ◽  
Test Results ◽  
Brushless Dc ◽  
Aerial Vehicle ◽  
Aircraft System

Unmanned Aerial Vehicle (UAV) is an unmanned aircraft system that is no longer a special need but has become a general need for the community, and one example is used to capture everyday moments through photos or videos from the air. Among the models of UAV aircraft is the quadcopter, where there is a flight controller that functions to fly the quadcopter by adjusting the speed of each motor. The flight controller that is often used today is the Pixhawk manufacturer. The Pixhawk module is an integrated system that the factory has provided, so it cannot be modified in terms of control and I/O. This research focuses on making an independent flight controller that can be used to fly a quadcopter. The control method that is implanted is Proportional Integral Derivative or commonly known as PID. The flight controller uses the PID control method to adjust each Brushless DC Motor (BLDC) speed to maintain stability while flying. From the test results, the quadcopter can fly stably with KP parameters of 2.5, KI of 0.6, and KD of 1.0. The response time in processing feedback is 3s.

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