Synthesis of Robust Controllers with Structured Real Uncertain Parameters

这一提议的动机来自于 存在结构干扰时的鲁棒控制问题 真实的不确定参数。 更具体地说，它涉及 只适用于由转移描述的线性系统 函数或状态空间模型，包括不确定的 参数进入各种设备系数。 研究的出发点是文学的主体 Kharitonov定理。 鉴于 这一系列的文献几乎只涉及 鲁棒稳定性分析， 研究了鲁棒镇定控制器的综合问题。 这一行的火花是由一些 最近的极值点结果（涉及PI） 区间对象的鲁棒镇定（ 区间多项式）与一阶控制器。 另外的动机也是由所谓的 价值集方法。 鉴于广泛使用的价值 在分析上下文中，开放的问题是： 在综合的情况下，价值集在多大程度上是有用的？ 这项建议的第二个重点是强有力的 性能 也就是说，目标是处理 强大的性能规格， 稳定 类似于鲁棒性的研究 稳定，打算开始工作， 研究低阶的鲁棒性能问题 控制器，然后跟进更高的订单 案子 在这一框架内， 提出了研究问题。 第三和最后 焦点是用生成这个词来描述的 机制 也就是说， 可以容纳的不确定性，它是 建议对“不确定”的人施加更多的结构 多项式和有理函数的出现， 分析.

The motivation for this proposal is derived from the problem of robust control in the presence of structured real uncertain parameters. More specifically, it deals exclusively with linear systems described by a transfer function or state space model which includes uncertain parameters entering into various plant coefficients. The takeoff point for research is the body of literature motivated by Kharitonov's Theorem. Given the fact that this body of literature deals almost exclusively with robust stability analysis, the first focal point of the research is synthesis of robust stabilizing controllers. A spark for this line of work is provided by some recent extreme point results (involving the PI) on robust stabilization of an interval plant (ratio of interval polynomials) with a first order controller. Additional motivation is also provided by the so-called value set approach. Given the widespread use of value sets in an analysis context, the open question is: To what extent are value sets useful in a synthesis context? The second focal point of this proposal is robust performance. That is, the objective is to deal with robust performance specifications over and above stability. Analogous to the investigation of robust stabilization, the intention is to begin the work by looking at robust performance problems with low order controllers and then to follow up with the higher order case. In this framework, a number of important research problems are proposed. The third and final focal point is described by the words generating mechanism. That is, to enlarge the class of uncertainties which can be accommodated, it is proposed to impose more structure on the "uncertain" polynomials and rational functions which arise in the analysis.