arXiv (All)
open access
Action-Increment-Constrained Policy Optimization for Smooth Online Adaptive Critic Flight Control with Actuator Rate Constraints and Low Computational Complexity (extended version with Ultimate Uniform Boundedness analysis)
Approximate Dynamic Programming (ADP) enables online control-law adaptation through adaptive critic methods. A safety challenge in applying ADP to angle-of-attack (AoA) tracking control is that an overly sensitive neural network (NN) controller may generate oscillatory or saturated control actions u
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Continuous-Time Critic as a Lyapunov Function: Projected Adaptation and Cart-Pole Stabilization#1234355
- An integrated interpretable control effectiveness learning and nonlinear control allocation methodology for overactuated aircraft#1389318
- Improving Action Smoothness for a Cascaded Online Learning Flight Control System#149308
- MPC for underactuated spacecraft control with a Lyapunov supervised physics-informed neural network correction layer#630039
- MPC for underactuated spacecraft control with a Lyapunov supervised physics-informed neural network correction layer#628177
Record · ID 1468108
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive