Efficient Computation of Feedback Control for Constrained Systems
This work provides a more efficient solution for the constrained LQR problem, which is common in robotic trajectory optimization, potentially enabling faster real-time control.
The paper presents a method for efficiently solving the discrete-time finite-horizon LQR problem with linear equality constraints, such as fixed end-point constraints, by deriving an affine feedback control policy. The method shows improved computation time compared to existing approaches.
A method is presented for solving the discrete-time finite-horizon Linear Quadratic Regulator (LQR) problem subject to auxiliary linear equality constraints, such as fixed end-point constraints. The method explicitly determines an affine relationship between the control and state variables, as in standard Riccati recursion, giving rise to feedback control policies that account for constraints. Since the linearly-constrained LQR problem arises commonly in robotic trajectory optimization, having a method that can efficiently compute these solutions is important. We demonstrate some of the useful properties and interpretations of said control policies, and we compare the computation time of our method against existing methods.