GENERALIZED PREDICTIVE CONTROL .1. THE BASIC ALGORITHM
GENERALIZED PREDICTIVE CONTROL .1. THE BASIC ALGORITHM
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DOI：
10.1016/0005-1098(87)90087-2
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发表时间：
1987-03-01
期刊：
影响因子：
6.4
通讯作者：
TUFFS, PS
中科院分区：
文献类型：
--
作者：
CLARKE, DW;MOHTADI, C;TUFFS, PS
目前的自校正算法对滞后时间或模型阶数的先验选择缺乏稳健性。提出了一种新的方法--广义预测控制(GPC)，仿真研究表明，该方法优于广义最小方差和极点配置等公认的方法。这种滚动时间法依赖于基于对未来控制行动的假设，在几个步骤中预测工厂的产量。一个假设--存在一个“控制范围”，超过这个范围，所有的控制增量都变为零--被证明在稳健性和提供简化计算方面都是有益的。选择输出和控制范围的特定值作为该方法的子集会产生各种有用的算法，如GMV、EPSAC、Peterka的预测控制器(1984，Automatica，20，39-50)和Ydstie的扩展范围设计(1984，IFAC第9届世界大会，匈牙利布达佩斯)。因此，广义预测控制既可以用来控制先验知识很少的“简单”对象(如开环稳定)，也可以用来控制更复杂的对象，如非最小相位、开环不稳定和具有可变时滞的对象。特别是，如果对象模型被过度参数化，GPC似乎不会受到影响(与极点配置策略不同)。此外，由于假设CARIMA工厂模型的结果消除了偏差，GPC是一般自校正应用的竞争者。这一点得到了比较模拟研究的验证。
Current self-tuning algorithms lack robustness to prior choices of either dead-time or model order. A novel method—generalized predictive control or GPC—is developed which is shown by simulation studies to be superior to accepted techniques such as generalized minimum-variance and pole-placement. This receding-horizon method depends on predicting the plant's output over several steps based on assumptions about future control actions. One assumption—that there is a “control horizon” beyond which all control increments become zero—is shown to be beneficial both in terms of robustness and for providing simplified calculations. Choosing particular values of the output and control horizons produces as subsets of the method various useful algorithms such as GMV, EPSAC, Peterka's predictive controller (1984,Automatica,20, 39–50) and Ydstie's extended-horizon design (1984, IFAC 9th World Congress, Budapest, Hungary). Hence GPC can be used either to control a “simple” plant (e.g. open-loop stable) with little prior knowledge or a more complex plant such as nonminimum-phase, open-loop unstable and having variable dead-time. In particular GPC seems to be unaffected (unlike pole-placement strategies) if the plant model is overparameterized. Furthermore, as offsets are eliminated by the consequence of assuming a CARIMA plant model, GPC is a contender for general self-tuning applications. This is verified by a comparative simulation study.