View source for The regents of the university of california (20240256868). MACHINE-LEARNING BASED STABILIZATION CONTROLLER THAT CAN LEARN ON AN UNSTABLE SYSTEM simplified abstract

=MACHINE-LEARNING BASED STABILIZATION CONTROLLER THAT CAN LEARN ON AN UNSTABLE SYSTEM=


==Organization Name==

[[:Category:the regents of the university of california|the regents of the university of california]]


[[Category:the regents of the university of california]]

==Inventor(s)==

[[:Category:Dan Wang of Berkeley CA (US)|Dan Wang of Berkeley CA (US)]][[Category:Dan Wang of Berkeley CA (US)]]

[[:Category:Qiang Du of Pleasanton CA (US)|Qiang Du of Pleasanton CA (US)]][[Category:Qiang Du of Pleasanton CA (US)]]

[[:Category:Russell Wilcox of Berkeley CA (US)|Russell Wilcox of Berkeley CA (US)]][[Category:Russell Wilcox of Berkeley CA (US)]]

[[:Category:Tong Zhou of Albany CA (US)|Tong Zhou of Albany CA (US)]][[Category:Tong Zhou of Albany CA (US)]]

[[:Category:Christos Bakalis of Berkeley CA (US)|Christos Bakalis of Berkeley CA (US)]][[Category:Christos Bakalis of Berkeley CA (US)]]

[[:Category:Derun Li of Concord CA (US)|Derun Li of Concord CA (US)]][[Category:Derun Li of Concord CA (US)]]

==MACHINE-LEARNING BASED STABILIZATION CONTROLLER THAT CAN LEARN ON AN UNSTABLE SYSTEM - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240256868 titled 'MACHINE-LEARNING BASED STABILIZATION CONTROLLER THAT CAN LEARN ON AN UNSTABLE SYSTEM

'''Simplified Explanation''': 

The patent application describes a machine learning controller and method for stabilizing unstable systems based on measurements, allowing for training on uncontrolled systems and continuous learning during operation. The controller outperforms similar technologies on complex systems without the need for physics modeling.

'''Key Features and Innovation''': 
* Machine learning controller for stabilizing unstable systems
* Training on uncontrolled systems and continuous learning during operation
* Improved performance on complex systems without physics modeling

'''Potential Applications''': 
The technology can be applied in various industries such as:
* Aerospace
* Automotive
* Robotics
* Industrial automation

'''Problems Solved''': 
The technology addresses the following problems:
* Stabilizing unstable systems without the need for physics modeling
* Continuous learning and adaptation during operation

'''Benefits''': 
The benefits of this technology include:
* Improved performance on complex systems
* Adaptability to unanalyzed or partially analyzed systems
* Continuous learning and optimization during operation

'''Commercial Applications''': 
Potential commercial applications include:
* Control systems for aerospace and automotive industries
* Robotics applications in manufacturing
* Industrial automation for process control

'''Questions about the Technology''':
1. How does the machine learning controller adapt to unanalyzed systems?
2. What are the key advantages of continuous learning during operation?

'''Frequently Updated Research''': 
Stay updated on the latest research in machine learning controllers for stabilizing unstable systems to enhance performance and adaptability.


==Original Abstract Submitted==

a machine learning (ml) controller and method for systems that can learn to stabilize them based on measurements of an unstable system. this allows for training on a system not yet controlled and for continuous learning as the stabilizer operates. the controller has improved performance on unstable systems compared to similar technologies, especially complex ones with many inputs and outputs. furthermore, there is no need for modelling the physics, and the controller can adapt to un-analyzed or partially analyzed systems.



[[Category:G06N3/08]]
[[Category:CPC_G06N3/08]]