Adaptive Control of Time-Varying Systems

时变系统的自适应控制

• 批准号: RGPIN-2017-04219
• 负责人: Miller, Daniel
• 金额: $ 1.75万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=637415
• 关键词: Adaptive; Control; Time; Varying; Systems

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.

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）以期望的方式动作，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被控对象的数学模型，然后设计一个控制器，该控制器由数学方程描述，通常用软件实现。在这个过程中，一个常见的障碍是工厂模型的不确定性，这可能是由建模错误，由于磨损而改变参数，改变操作条件（如飞机的高度或机器人系统中有效载荷的质量变化）或系统故障（常见于工业系统）等因素引起的。如果不确定性较大，则不能使用简单的比例-积分-微分（PID）控制器，而必须采用更复杂的方法。一种强大的方法是自适应控制，其中控制器随着时间的推移越来越多地了解工厂，从而使自己适应工厂。