概率鲁棒控制器整定的序贯学习算法
A Sequential Learning Algorithm for Probabilistically Robust Controller Tuning
摘要：
我们引入了一个顺序学习算法来解决一个鲁棒控制器的调整问题，这实际上，发现（以高概率）的候选解决方案，满足内部性能约束的机会约束的程序，具有黑盒功能。该算法利用了随机算法和有序优化领域的思想，并与场景方法进行了比较;这些方法以前都被应用于寻找困难设计问题的近似解。通过黑盒采样利用统计相关性，我们正式证明，我们的算法产生一个控制器满足规定的概率性能指标。此外，我们用算法的停时的概率下界来描述算法的计算要求。为了验证我们的工作，该算法，然后证明了调整模型预测控制器的柴油发动机的空气路径在车队的车辆。该算法成功地调整了一个单一的控制器，以满足所需的跟踪误差性能，即使在存在的植物固有的不确定性在整个舰队。此外，该算法被证明表现出一个样本的复杂性相媲美的场景的方法。
英文摘要：
We introduce a sequential learning algorithm to address a robust controller tuning problem, which in effect, finds (with high probability) a candidate solution satisfying the internal performance constraint to a chance-constrained program which has black-box functions. The algorithm leverages ideas from the areas of randomised algorithms and ordinal optimisation, and also draws comparisons with the scenario approach; these have all been previously applied to finding approximate solutions for difficult design problems. By exploiting statistical correlations through black-box sampling, we formally prove that our algorithm yields a controller meeting the prescribed probabilistic performance specification. Additionally, we characterise the computational requirement of the algorithm with a probabilistic lower bound on the algorithm's stopping time. To validate our work, the algorithm is then demonstrated for tuning model predictive controllers on a diesel engine air-path across a fleet of vehicles. The algorithm successfully tuned a single controller to meet a desired tracking error performance, even in the presence of the plant uncertainty inherent across the fleet. Moreover, the algorithm was shown to exhibit a sample complexity comparable to the scenario approach.
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