Polyhedron in the form {x in R^n | A x <= b, C x = d}.

Discrete-time affine systems in the form x(t+1) = A x(t) + B u(t) + c.

Discrete-time linear systems in the form x(t+1) = A x(t) + B u(t).

Discrete-time piecewise-affine systems in the form x(t+1) = A_i x(t) + B_i u(t) + c_i if (x(t), u(t)) in D_i := {(x,u) | F_i x + G_i u <= h_i}.

Model predictive controller for linear systems, it solves the optimal control problem V*(x(0)) := min_{x(.), u(.)} 1/2 sum_{t=0}^{N-1} x'(t)

mpQP in the form |u|' |Huu Hux| |u| |fu|' |u| V(x) := min_u 1/2 |x| |Hux' Hxx| |x| + |fx| |x| + g s.t

Critical Region (CR) of a multi-parametric quadratic program. A CR is the region of space where a fixed active set is optimal.

Explicit solution of a multiparametric quadratic program.

Multiparametric Mixed Integer Quadratic Program (mpMIQP) in the form that comes out from the MPC problem for a piecewise affine system, i.e.

Wrapper for the HybridModelPredictiveController class from pympc.

These are just some very basic tests. The class MultiParametricQuadraticProgram is further tested with the ModelPredictiveController class.

This tests only if the solvers are called correctly,. More tests on the correctness of the results can be fuond in test_solvers.py.