Learning Local Optimal Controller for a Class of Nonlinear Systems via Impulse-Supervised Exploration
Summary
arXiv:2606.03107v1 Announce Type: new Abstract: This paper develops an impulse-supervised confined exploration framework for learning local optimal controller for a class of nonlinear systems. The proposed approach combines continuous-time approximate dynamic programming (ADP) with an impulsive supervisory layer, where impulsive braking confines the state within a prescribed region in which a local linear approximation of the nonlinear system is valid. This enables desired persistent excitation required for parameter convergence while preventing large state deviations that invalidate local optimality.
Why It Matters
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Key Facts
- SectorAdvanced Manufacturing
- Market—
- ImpactLow (42/100)
- SignalResearch