RLS model identification-based robust control for gimbal axis of control moment gyroscope

2017 ◽  
Author(s):  
Sangdeok Lee ◽  
Seul Jung
Keyword(s):  
Robust Control ◽  
Model Identification ◽  
Control Moment Gyroscope ◽  
Control Moment

Oscillating Control Moment Gyroscope Experimental Results

2019 ◽  
Author(s):  
Burak Akbulut ◽  
Ozan Tekinalp ◽  
Ferhat Arberkli ◽  
Kivanc Azgin
Keyword(s):  
Experimental Results ◽  
Control Moment Gyroscope ◽  
Control Moment

Towards Automated Bicycles: Achieving Self-Balance Using Steering Control

2018 ◽  
Author(s):  
Wenhao Deng ◽  
Skyler Moore ◽  
Jonathan Bush ◽  
Miles Mabey ◽  
Wenlong Zhang
Keyword(s):  
Autonomous Vehicles ◽  
Controller Design ◽  
Smart Cities ◽  
Autonomous Driving ◽  
Human Robot Interaction ◽  
Steering Control ◽  
Control Moment Gyroscope ◽  
Control Moment ◽  
Potential Applications ◽  
Electric Bicycle

In recent years, researchers from both academia and industry have worked on connected and automated vehicles and they have made great progress toward bringing them into reality. Compared to automated cars, bicycles are more affordable to daily commuters, as well as more environmentally friendly. When comparing the risk posed by autonomous vehicles to pedestrians and motorists, automated bicycles are much safer than autonomous cars, which also allows potential applications in smart cities, rehabilitation, and exercise. The biggest challenge in automating bicycles is the inherent problem of staying balanced. This paper presents a modified electric bicycle to allow real-time monitoring of the roll angles and motor-assisted steering. Stable and robust steering controllers for bicycle are designed and implemented to achieve self-balance at different forward speeds. Tests at different speeds have been conducted to verify the effectiveness of hardware development and controller design. The preliminary design using a control moment gyroscope (CMG) to achieve self-balancing at lower speeds are also presented in this work. This work can serve as a solid foundation for future study of human-robot interaction and autonomous driving.

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