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: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=659796
• 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.******This approach has its roots in the 1950s, and it has grown more and more sophisticated over the years. The increased computational power of computers allows more complicated control algorithms to be implemented in real-time, so it is making inroads in areas like robotics and aerospace. However, there are still unanswered questions in the field, and in my proposed research the goal is to answer several important ones:***(i) Is it possible to design adaptive controllers which not only provide good performance asymptotically, but also provide it in the short-run, while the adaptive controller is still learning about the plant?***(ii) Is it possible to handle rapidly time-varying parameters as well as the more common case of very slow time-varying parameters?***(iii) Is it possible to redesign classical adaptive controllers to make them more powerful: more robust, more noise tolerant, and better at tolerating time-varying parameters?******The answers to these questions will enhance the state-of-the-art of adaptive control, and reduce the gap between theory and practise. It will provide control engineers with new algorithms; it will benefit a team of graduate students by engaging in advanced technological training; and it will enhance Canada's position as a leading proponent of advanced automation.

在系统控制中，目标是通过使用（自动）控制器（例如控制器）使物理系统（工厂）以所需的方式运行。自动驾驶仪（控制器）用于飞机（设备）上以保持速度、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，然后设计一个由数学方程描述的控制器，通常用软件实现。此过程中常见的障碍是工​​厂模型的不确定性，这可能是由建模错误、由于磨损而导致的参数变化、操作条件变化（例如飞机的高度或机器人系统中有效负载的质量变化）或系统故障（在工业系统中常见）等因素引起的。如果不确定性很大，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对植物了解越来越多，从而适应植物。******这种方法起源于 20 世纪 50 年代，多年来变得越来越复杂。计算机计算能力的增强使得可以实时实现更复杂的控制算法，因此它正在机器人和航空航天等领域取得进展。然而，该领域仍然存在未解答的问题，在我提出的研究中，目标是回答几个重要的问题：***（i）是否有可能设计出自适应控制器，不仅渐近地提供良好的性能，而且在自适应控制器仍在学习对象的同时在短期内提供它？***（ii）是否有可能处理快速时变参数以及非常慢时变的更常见情况 ***(iii) 是否有可能重新设计经典的自适应控制器，使其变得更强大：更鲁棒，更能容忍噪声，并且更好地容忍时变参数？******这些问题的答案将提高自适应控制的最先进水平，并缩小理论与实践之间的差距。它将为控制工程师提供新的算法； 通过参与先进的技术培训，将使研究生团队受益；它将增强加拿大作为先进自动化主要支持者的地位。