Study on Dynamic Torque PID Control for Automobile Diaphragm Spring Clutch Based on Kalman Filter

In order to obtain the brake’s performance and related characteristics accurately and completely, a brake inertia dynamometer with the method of mechanical and electric inertia simulation was designed in this paper. By adjusting the output torque of the motor and inertia fly wheels, the equivalent inertia of the motorcycle was simulated on the inertia dynamometer. The relation between simulated inertia and output torque of the motor was presented. Due to the strong interference and high requirement of control accuracy, a PID control algorithm was proposed for the rotational speed contro1. Moreover, Kalman filter was designed to eliminate the random noise and industrial frequency disturbance. The experimental results demonstrated that the inertia dynamometer’s accuracy and efficiency were improved significantly. Moreover, the brake testing platform worked stably and reliably.

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Dynamometer Development of Motorcycle Engine Applied Kalman Filter

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.321-324.23 ◽

2013 ◽

Vol 321-324 ◽

pp. 23-26

Author(s):

Li Peng Zhang ◽

Guang Shun Wu ◽

Su Xiang Fang

Keyword(s):

Kalman Filter ◽

Pid Control ◽

System Reliability ◽

Control Algorithm ◽

Random Noise ◽

System Structure ◽

Motorcycle Engine ◽

Control Accuracy ◽

Control Management ◽

Industrial Frequency

In order to obtain the engine’s performance and related characteristics accurately and completely, engine dynamometer of motorcyclewas developed based on CAN bus in this paper. The system structure was decreased, the system reliability and level of control management were greatly improved. Due to the strong interference and high requirement of control accuracy, a PID control algorithm was proposed for the rotational speed contro1. Moreover, Kalman filter was designed to eliminate the random noise and industrial frequency disturbance. The experimental results demonstrated that the dynamometer’s accuracy and efficiency were improved significantly. Moreover, the testing platform worked stably and reliably.

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Developing a control system by integrating the Kalman Filter and the PID control loop

2017 IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI) ◽

10.1109/icpcsi.2017.8392311 ◽

2017 ◽

Author(s):

Suhas Etigunta ◽

Raj Malhotra ◽

Samuel Carlos

Keyword(s):

Kalman Filter ◽

Control System ◽

Pid Control ◽

Control Loop

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Kalman Filter to Improve Performance of PID Control Systems on DC Motors

IJITEE (International Journal of Information Technology and Electrical Engineering) ◽

10.22146/ijitee.64511 ◽

2021 ◽

Vol 5 (3) ◽

pp. 96

Author(s):

Seta Yuliawan ◽

Oyas Wahyunggoro ◽

Nurman Setiawan

Keyword(s):

Kalman Filter ◽

Control System ◽

Pid Control ◽

Pid Controller ◽

Absolute Error ◽

Pid Controllers ◽

Electronic Components ◽

Improve Performance ◽

Noise Sources ◽

Dc Motors

A proportional–integral–derivative (PID) controller is a type of control system that is most widely applied in industrial world. Various tuning models have been developed to obtain optimal performance in PID control. However, the methods are designed under ideal circumstances. This means that the control system which has been built will not work optimally when noise exists. Noise can come from electrical vibrations, inference of electronic components, or other noise sources. Thus, it is necessary to design PID control system that can work optimally without being disturbed by noise. In this research, Kalman filter was used to improve the performance of PID controllers. The application of Kalman filter was used to reduce the noise of the input signal so that it could generate output signal which is in accordance with the expected output. Simulation result showed that the PID performance with Kalman filter was more optimal than the ordinary one to minimize the existing noise. The resulting speed of DC motor with Kalman filter had a lower overshoot than PID control without Kalman filter. This method resulted lower integral of absolute error (IAE) than ordinary PID controls. The IAE value for the PID controller with the Kalman filter was 25.4, the PID controller with the observer was 31.0, while the IAE value in the ordinary controller was only 60.9.

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