Structured adaptive estimation: reliable "grey box" adaptation

状态观测器用于估计系统的内部状态，这些状态不能被物理测量。在控制工程中，使用观测器的基本原理已经很好地建立起来。原则上，观测器需要一个精确的数学模型，其中系统的动态方程是已知的，只需要估计内部状态。事实上，当对系统动态特性知之甚少时，观测器的估计性能可能很差。自适应观测器可用于估计系统状态和系统的动态;原则上，它们可用于仅从数据学习关于系统的“一切”。不幸的是，目前的自适应观测器只能在系统满足一些非常严格的假设时才能可靠地工作，这使得它们不适合于许多实际的现实问题。通常情况下，ad-hoc自适应机制的设计，以增加观察员时，这些严格的假设是不满足的，但结果是虚假的，往往很差。该建议试图通过两种关键方法来扩展系统的类别，对于这些系统，人们可以期望可靠的自适应估计。第一个涉及降低自适应估计器的野心：工厂将被划分的方式，使麻烦的元素的动态，已知的适应问题，将被排除在适应机制和处理使用的技术，从广义上说，鲁棒控制（即不确定系统的控制）。因此，自适应观测器将利用系统结构，只更新那些组件，它是“安全”这样做。换句话说，为了保证可靠性，将牺牲一些潜在的性能。第二种扩展系统类的方法是放松对系统被观测部分无源性的严格要求（不产生内能）。这条道路将利用一个放松的无源性条件，从一个更一般的方法来分析非线性系统，有时被称为“圆锥扇区”的方法，这在最近几年已经成熟，其中无源性是一个特殊的情况。该项目也有一个强烈的重视可用性。事实上，与所有控制系统设计方法一样，自适应观测器需要正确调谐以提供期望的结果。一般来说，目前的自适应控制方法的整定过程是明显困难的，缺乏直观性。这是自适应控制的潜力尚未被挖掘的另一个原因。因此，该项目将处理两个真实世界的案例研究，并为有效调整自适应观测器建立专门知识和指导方针。这样的调整方法是至关重要的未来应用的研究在这里开发。第一个案例研究将集中在航空发动机液压机械伺服系统的控制系统的设计，旨在证明相对于系统中实施的当前工业解决方案的改进。第二个案例研究将使用来自神经生理学记录的实验室数据来估计神经元之间信息传递的分子机制中的隐藏状态。

State observers are used to estimate systems' internal states which cannot be physically measured. In control engineering, the rationale behind the use of observers is well established. In principle, observers require an accurate mathematical model, in which the system's dynamic equations are known and only the internal states need to be estimated. In fact, observers' estimation performance may be poor when not much is known about the system dynamics. Adaptive observers may be used to estimate the system states and the dynamics of the system; in principle they can be used to learn "everything" about a system from just data. Unfortunately, current adaptive observers only work reliably when the system satisfies some very restrictive assumptions, making them unsuitable for many practical real-world problems. Often, ad-hoc adaptive mechanisms are devised to augment observers when these strict assumptions are not satisfied, but results are spurious and often poor. This proposal attempts to expand the class of systems for which one can expect reliable adaptive estimation by two key approaches. The first involves a lowering in ambition of the adaptive estimator: the plant will be partitioned in a way such that troublesome elements of the dynamics, known to cause problems in adaptation, will be excluded from the adaptation mechanism and dealt with using techniques from, broadly speaking, robust control (i.e. the control of uncertain systems). The adaptive observer will therefore exploit system structure and only update those components for which it is "safe" to do so. In other words, some potential performance will be sacrificed for guarantees of reliability. The second path to expanding the class of systems involves loosening the standard strict requirement of passivity of the observed part of the system (no generation of internal energy). This path will exploit a relaxation of the passivity condition which follows from a more general approach to the analysis of nonlinear systems, sometimes referred to as the "conic sector" approach, which has matured in recent years, and for which passivity is a special case.The project has also a strong emphasis on usability. In fact, as with all control system design methods, adaptive observers need to be tuned correctly in order to provide desirable results. In general, tuning procedures for current adaptive control methods are conspicuously difficult and lack intuition. This is one additional reason why the potential of adaptive control has not yet been harvested. The project will therefore address two real-world case studies and establish know-how and guidelines for effective tuning of the adaptive observers. Such a tuning approach is vital for future application of the research developed here. The first case study will focus on the design of the control system for an aero-engine hydro-mechanical servo system aiming at demonstrating improvement with respect to the current industrial solution implemented in the system. The second case study will use lab data from neurophysiology recordings to estimate hidden states in the molecular mechanism of information transfer between neurons.