Nandola Naresh N, Rivera Daniel E
Control Systems Engineering Laboratory, School for Engineering of Matter, Transport, and Energy, Arizona State University, Tempe, AZ 85287-6106, USA.
Proc IEEE Conf Decis Control. 2011 Feb 22;2010:6113-6118. doi: 10.1109/CDC.2010.5717296.
This paper presents a data-centric modeling and predictive control approach for nonlinear hybrid systems. System identification of hybrid systems represents a challenging problem because model parameters depend on the mode or operating point of the system. The proposed algorithm applies Model-on-Demand (MoD) estimation to generate a local linear approximation of the nonlinear hybrid system at each time step, using a small subset of data selected by an adaptive bandwidth selector. The appeal of the MoD approach lies in the fact that model parameters are estimated based on a current operating point; hence estimation of locations or modes governed by autonomous discrete events is achieved automatically. The local MoD model is then converted into a mixed logical dynamical (MLD) system representation which can be used directly in a model predictive control (MPC) law for hybrid systems using multiple-degree-of-freedom tuning. The effectiveness of the proposed MoD predictive control algorithm for nonlinear hybrid systems is demonstrated on a hypothetical adaptive behavioral intervention problem inspired by Fast Track, a real-life preventive intervention for improving parental function and reducing conduct disorder in at-risk children. Simulation results demonstrate that the proposed algorithm can be useful for adaptive intervention problems exhibiting both nonlinear and hybrid character.
本文提出了一种针对非线性混合系统的数据中心建模和预测控制方法。混合系统的系统辨识是一个具有挑战性的问题，因为模型参数取决于系统的模式或运行点。所提出的算法应用按需建模（MoD）估计，在每个时间步使用由自适应带宽选择器选择的一小部分数据，生成非线性混合系统的局部线性近似。MoD方法的吸引力在于，模型参数是基于当前运行点进行估计的；因此，由自主离散事件控制的位置或模式的估计可以自动实现。然后将局部MoD模型转换为混合逻辑动态（MLD）系统表示，该表示可直接用于使用多自由度调整的混合系统的模型预测控制（MPC）律中。在一个受“快车道”启发的假设自适应行为干预问题上，证明了所提出的MoD预测控制算法对非线性混合系统的有效性，“快车道”是一项现实生活中的预防性干预措施，旨在改善高危儿童的父母功能并减少品行障碍。仿真结果表明，所提出的算法对于表现出非线性和混合特性的自适应干预问题可能是有用的。
