Suboptimal Explicit Receding Horizon Control via Approximate Multiparametric Quadratic Programming

作者: A. Bemporad , C. Filippi

DOI: 10.1023/A:1023696221899

关键词:

摘要: Algorithms for solving multiparametric quadratic programming (MPQP) were recently proposed in Refs. 1–2 computing explicit receding horizon control (RHC) laws linear systems subject to constraints on input and state variables. The reason this interest is that the solution MPQP a piecewise affine function of vector thus it easily implementable online. main drawback exactly that, whenever number involved optimization problem increases, polyhedral cells partition parameter space may increase exponentially. In paper, we address finding approximate solutions MPQP, where degree approximation arbitrary allows tradeoff between optimality smaller solution. We provide analytic formulas bounding errors optimal value optimizer, guaranteeing resulting suboptimal RHC law provides closed-loop stability constraint fulfillment.

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