Summary of Neural Control: Concurrent System Identification and Control Learning with Neural Ode, by Cheng Chi
Neural Control: Concurrent System Identification and Control Learning with Neural ODE
by Cheng Chi
First submitted to arxiv on: 3 Jan 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research proposes a novel approach to controlling unknown dynamical systems, which is crucial for many real-world applications. The proposed method, Neural Control (NC), combines dynamics identification and optimal control learning using a coupled neural ODE structure. By concurrently learning system dynamics and optimal controls, NC can effectively guide the system towards target states. Experiments demonstrate its effectiveness in learning optimal control of unknown dynamical systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to get a robot to follow a specific path. You need to teach it how to move and what to do at each step. But, if your instructions are wrong, the robot won’t be able to achieve its goal. This paper presents a new way to control these kinds of systems using artificial intelligence. It’s called Neural Control (NC) and it learns both how the system works and what actions to take to reach a target state. The results show that this approach is very effective in controlling unknown systems. |
