![]() It also does not appear to support discrete time systems. The downside is that it requires Slycot library, which is annoying to get running on Windows. This offers a Matlab-like syntax for a variety of control methods. K = np.matrix((B.T*X*B+R)*(B.T*X*A))Īn alternative method is to use the Python Control toolbox. """Solve the discrete time lqr controller. X = np.matrix(_continuous_are(A, B, Q, R)) #first, try to solve the ricatti equation """Solve the continuous time lqr controller. I’ve done some basic sanity checks, and it seems to work.įrom _future_ import division, print_function The equations come from Bertsekas “Dynamic Programming and Optimal Control”. Similarly, one can compute steady state Kalman filters.īelow are my wrapper functions for continuous and discrete time LQR controllers. Since solving the Ricatti equation is the hard part of solving for an LQR gain, this implies that one can compute infinite horizon LQR controllers straight-forwardly using only SciPy.linalg. I’ve learnt that SciPy offers a Ricatti equation solver (`_continuous_are` and `_discrete_are`). One area where I struggled was to compute steady state LQR controllers. ![]() I have been using Python exclusively for my scientific computing for about half a year (having been frustrated by Matlab’s awkward syntax for complex programs, and annoying licensing). UPDATE: Please see here for an update: Python control library: controlpy
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