Online Feedback Control for Input-Saturated Robotic Systems on Lie Groups
This work addresses control challenges for robotic systems with input constraints, offering improved performance for applications like autonomous vehicles and drones, though it is incremental as it builds on existing SAC methods.
The paper tackles the problem of designing online feedback controllers for input-saturated robotic systems on Lie groups by extending Sequential Action Control (SAC), resulting in a closed-form control law that is faster to compute and has a larger basin of attraction compared to iLQG, as demonstrated on 2D and 3D robotic models.
In this paper, we propose an approach to designing online feedback controllers for input-saturated robotic systems evolving on Lie groups by extending the recently developed Sequential Action Control (SAC). In contrast to existing feedback controllers, our approach poses the nonconvex constrained nonlinear optimization problem as the tracking of a desired negative mode insertion gradient on the configuration space of a Lie group. This results in a closed-form feedback control law even with input saturation and thus is well suited for online application. In extending SAC to Lie groups, the associated mode insertion gradient is derived and the switching time optimization on Lie groups is studied. We demonstrate the efficacy and scalability of our approach in the 2D kinematic car on SE(2) and the 3D quadrotor on SE(3). We also implement iLQG on a quadrator model and compare to SAC, demonstrating that SAC is both faster to compute and has a larger basin of attraction.