Adaptive Control of Time-Varying Systems

在系统控制中，目标是通过使用(自动)控制器使物理系统(工厂)以期望的方式运行，例如在飞机(工厂)上使用自动驾驶仪(控制器)来保持速度、高度和方向。控制系统设计的第一步是获得对象的数学模型，然后设计由数学方程描述的控制器，该控制器通常用软件实现。在这一过程中的一个常见绊脚石是工厂模型中的不确定性，这可能是由建模错误、因磨损而改变参数、改变操作条件(例如，机器人系统中飞机的高度或有效载荷质量的改变)或系统故障(工业系统中常见的)引起的。如果不确定度较大，则不能采用简单的比例-积分-导数(PID)控制器，而可以采用更复杂的方法。一种有效的方法是自适应控制，控制器随着时间的推移对对象了解越来越多，从而使自己适应对象。

In systems control, the objective is to make a physical system (the plant) act in a desired manner through the use of an (automatic) controller, e.g. an autopilot (the controller) is used on an aircraft (the plant) to maintain speed, altitude and direction. The first step in control system design is to obtain a mathematical model of the plant, and then one designs a controller, described by a mathematical equation, which is typically implemented in software. A common stumbling block in this process is uncertainty in the plant model, which can be caused by such things as modelling error, changing parameters due to wear and tear, changing operating conditions (such as the altitude of an aircraft or the changing mass of a payload in a robotic system), or systems faults (common in industrial systems). If the uncertainty is large, then a simple proportional-integral-derivative (PID) controller cannot be used, and a more sophisticated approach muct be adopted. One powerful approach is that of adaptive control, wherein the controller adapts itself to the plant as it learns more and more about it over time.