Summary of Lyapunov Neural Ode State-feedback Control Policies, by Joshua Hang Sai Ip et al.
Lyapunov Neural ODE State-Feedback Control Policies
by Joshua Hang Sai Ip, Georgios Makrygiorgos, Ali Mesbah
First submitted to arxiv on: 31 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Systems and Control (eess.SY)
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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 The paper presents a novel approach called Lyapunov-NODE control (L-NODEC) for solving continuous-time optimal control problems (OCPs). This approach uses a neural ordinary differential equation (NODE) framework to learn a state-feedback neural control policy that stabilizes a known constrained nonlinear system around an equilibrium state. The proposed Lyapunov loss formulation incorporates an exponentially-stabilizing control Lyapunov function, ensuring exponential stability and adversarial robustness of the controlled system to perturbations. L-NODEC is evaluated in two problems, including a dose delivery problem in plasma medicine, where it effectively stabilizes the system and reduces inference time. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper uses deep neural networks to learn control policies for controlling systems. It focuses on solving continuous-time optimal control problems, which are important in many decision-making tasks. The approach is called Lyapunov-NODE control (L-NODEC) and it uses a special kind of neural network called a neural ordinary differential equation (NODE). This type of network can handle state and control constraints naturally. L-NODEC learns a state-feedback neural control policy that stabilizes the system around an equilibrium state. It also makes sure the system is stable even if there are small changes to the initial conditions. |
Keywords
» Artificial intelligence » Inference » Neural network
