Bemporad, Alberto and Muñoz de la Peña, David Multiobjective model predictive control. Automatica, 45 (12). pp. 2823-2830. (2009)Full text not available from this repository.
This paper proposes a novel model predictive control (MPC) scheme based on multiobjective optimization. At each sampling time, the MPC control action is chosen among the set of Pareto optimal solutions based on a time-varying, state-dependent decision criterion. Compared to standard single-objective MPC formulations, such a criterion allows one to take into account several, often irreconcilable, control specifications, such as high bandwidth (closed-loop promptness) when the state vector is far away from the equilibrium and low bandwidth (good noise rejection properties) near the equilibrium. After recasting the optimization problem associated with the multiobjective MPC controller as a multiparametric multiobjective linear or quadratic program, we show that it is possible to compute each Pareto optimal solution as an explicit piecewise affine function of the state vector and of the vector of weights to be assigned to the different objectives in order to get that particular Pareto optimal solution. Furthermore, we provide conditions for selecting Pareto optimal solutions so that the MPC control loop is asymptotically stable, and show the effectiveness of the approach in simulation examples.
|Uncontrolled Keywords:||Model predictive control; Multiobjective optimization; Multiparametric programming|
|Subjects:||Q Science > QA Mathematics
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
|Research Area:||Computer Science and Applications|
|Depositing User:||Professor Alberto Bemporad|
|Date Deposited:||27 Jul 2011 08:34|
|Last Modified:||04 Aug 2011 07:29|
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