Abstract

A method is presented for solving the discrete-time finite-horizon Linear Quadratic Regulator (LQR) problem sub- ject 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.

Paper Link

F. Laine and C. Tomlin, Efficient Computation of Feedback Control for Constrained Systems, ICRA 2019 (summitted)

PDF file

Code Dependancy

• Python 2.7.x
  • Numpy (for computation)
  • matplotlib (for drawing results)

How to Operate

• ipython LQR.py
  • My code is to caculate simple dynamics problem which is introduces in their paper.