Multiobjective model predictive control
Multiobjective model predictive control
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DOI：
10.1016/j.automatica.2009.09.032
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发表时间：
2009-12-01
期刊：
影响因子：
6.4
通讯作者：
Munoz de la Pena, David
中科院分区：
文献类型：
--
作者：
Bemporad, Alberto;Munoz de la Pena, David
提出了一种基于多目标优化的模型预测控制方法。在每个采样时间，MIPC控制动作是根据一个随时间变化的，状态相关的决策标准的帕累托最优解的集合中选择的。与标准的单目标MPC公式相比，这样的标准允许人们考虑几个通常不可调和的控制规范，例如当状态向量远离平衡时的高带宽（闭环不稳定性）和平衡附近的低带宽（良好的噪声抑制特性）。重铸后的优化问题与多目标MPC控制器作为一个多参数多目标线性或二次规划，我们表明，它是可以计算每个帕累托最优解作为一个明确的分段仿射函数的状态向量和向量的权重分配到不同的目标，以获得特定的帕累托最优解。此外，我们提供的条件，选择Pareto最优解，使MPC控制回路是渐近稳定的，并显示该方法的有效性，在仿真例子。(C)2009爱思唯尔有限公司保留所有权利。
This paper proposes a novel model predictive control (MPC) scheme based on multiobjective optimization. At each sampling time, the MIPC 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 Paretooptimal solutions so that the MPC control loop is asymptotically stable, and show the effectiveness of the approach in simulation examples. (C) 2009 Elsevier Ltd. All rights reserved.