Explicit MPC of higher-order linear processes via combinatorial multi-parametric quadratic programming

Parameter-dependent constrained optimization problems like they occur in the context of model predictive control (MPC) can be solved explicitly by means of multi-parametric quadratic programming (mpQP) techniques. We present a complexity analysis for a recently proposed combinatorial mpQP algorithm and discuss its advantages over existing geometric approaches concerning off-line explicit MPC computations for higher-order linear systems. The results are accompanied by numerical benchmark results for two suitable example problems from the area of process control.

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