This article suggests a collection of model-based and model-free output-feedback optimal solutions to a general H∞{H_{\infty }} control design criterion of a continuous-time linear system. The goal is to obtain a static output-feedback controller while the design criterion is formulated with an exponential term, divergent or convergent, depending on the designer’s choice. Two offline policy-iteration algorithms are presented first, which form the foundations for a family of online off-policy designs. These algorithms cover all different cases of partial or complete model knowledge and provide the designer with a collection of design alternatives. It is shown that such a design for partial model knowledge can reduce the number of unknown matrices to be solved online. In particular, if the disturbance input matrix of the model is given, off-policy learning can be done with no disturbance excitation. This alternative is useful in situations where a measurable disturbance is not available in the learning phase. The utility of these design procedures is demonstrated for the case of an optimal lane tracking controller of an automated car.

中文翻译：

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具有不同模型知识水平的连续时间系统的静态输出反馈 H — 控制设计程序


本文针对连续时间线性系统的一般 H∞{H_{\infty }} 控制设计准则提出了一系列基于模型和无模型的输出反馈最优解。目标是获得静态输出反馈控制器，同时设计标准用指数项（发散或收敛）来制定，具体取决于设计者的选择。首先提出了两种离线策略迭代算法，它们构成了一系列在线离线策略设计的基础。这些算法涵盖了部分或完整模型知识的所有不同情况，并为设计者提供了一系列设计替代方案。结果表明，这种针对部分模型知识的设计可以减少在线求解的未知矩阵的数量。特别是，如果给定模型的扰动输入矩阵，则可以在没有扰动激励的情况下进行离策略学习。这种替代方案在学习阶段无法测量干扰的情况下非常有用。这些设计程序的实用性在自动汽车的最佳车道跟踪控制器的情况下得到了证明。