Online Approximate Dynamic Programming within Linear Model Validity Regions Exploiting Impulsive Braking Inputs
The approach combines continuous-time approximate dynamic programming with an impulsive supervisory layer to learn local optimal controllers for nonlinear systems. Impulsive braking forces the system state into a safe region where its linear approximation is valid, ensuring desired exploration and parameter convergence while preventing large state deviations.