Developing neural-fuzzy adaptive controls with stability margins

开发具有稳定裕度的神经模糊自适应控制

• 批准号: RGPIN-2019-04831
• 负责人: Macnab, Chris
• 金额: $ 2.04万
• 依托单位: University of Calgary
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=715154
• 关键词: Developing; neural; fuzzy; adaptive; controls

My research program aims to develop machine learning algorithms, incorporating neural networks and fuzzy logic, that will improve the performance of control systems and guarantee their stability. I am currently endeavouring to find a universal replacement for the traditional PID feedback-loop controls ubiquitous in industrial and robotic automation systems (PID adds a Proportion, Integral, and Derivative of error). Although PIDs provide simple, robust controls that have served us well for a century, they will not meet the system requirements for many important systems of the future. A PID replacement should do better in terms of performance (less error and effort) while being guaranteed to do no worse in terms of stability. I am requesting NSERC Discovery funding in order to make some significant progress on this problem, specifically developing machine learning algorithms that incorporate neural network and fuzzy logic for some applications that will be of benefit to Canada. A promising research direction for this problem involves modifications to the Cerebellar Model Articulation Controller (CMAC) type of fuzzy neural network. The CMAC is capable of high performance in difficult situations, learning to compensate for nonlinearities, uncertainties, and periodic disturbances. I am currently working on modifying the CMAC control architecture so that it always has potential for much better performance without ever increasing the risk of instability. As part of the effort to maintain Canada's leadership in space robotics, my research group is working on the control system designs for a proposed surgical robot for the International Space Station. Since communication signals experience a theoretical round-trip time delay of up to one second, and in practice up to 7 seconds, we envision a safe model of operation where the surgeon interacts directly with a local 3D-printed model of the astronaut on Earth while the remote robot in space follows along safely a small time later. With the proposed NSERC Discovery grant, we would be looking at advanced solutions for the low-level feedback control loops to replace PID for both local and remote systems that would achieve (literally) surgical precision while still meeting the type of quantitative stability guarantees that would satisfy space-agency requirements. The main difficulty in the design is ensuring the small flexibilities in the robot do not cause excessive vibration in microgravity; I am in a strong position to tackle this problem as I have already developed a particular expertise controlling flexible-joint robots using CMAC. In previous work I also investigated replacing PID with CMAC in industrial plants like wastewater treatment, power-generating incinerators, and organic Rankine cycles (for turning waste heat into electricity) and will endeavour to validate my methods with similar applications. Thus, the research has the potential to benefit the environment and carbon-reduction efforts.

我的研究计划旨在开发机器学习算法，结合神经网络和模糊逻辑，以提高控制系统的性能并确保其稳定性。 目前，我正在努力找到对工业和机器人自动化系统中无处不在的传统PID反馈环控制器的通用替代品（PID增加了错误的比例，积分和派生词）。 尽管PID提供了一个简单，强大的控制，这些控制已为我们服务了一个世纪，但它们将无法满足未来许多重要系统的系统要求。替换PID在性能（较小的错误和精力）方面应该更好，同时保证在稳定性方面不会更糟。我要求NSERC发现资金以在此问题上取得重大进展，专门开发机器学习算法，这些算法将神经网络和模糊逻辑纳入某些对加拿大有益的应用程序。 这个问题的有希望的研究方向涉及对小脑模型关节控制器（CMAC）类型模糊神经网络的修改。 CMAC能够在困难的情况下具有高性能，学会弥补非线性，不确定性和周期性干扰。我目前正在努力修改CMAC控制体系结构，以便它始终具有更好的性能，而不会增加不稳定性的风险。 作为维持加拿大在太空机器人技术领导的努力的一部分，我的研究小组正在为国际空间站的拟议外科机器人设计控制系统设计。由于通信信号经历了理论上的往返时间延迟最多一秒钟，并且在实践中最多7秒，因此我们设想了一种安全的操作模型，外科医生直接与地球上宇航员的局部3D打印模型进行交互，而太空中的远程机器人则紧随其后的是小时后。借助拟议的NSERC Discovery Grant，我们将研究用于低级反馈控制循环的高级解决方案，以替换将（从字面上）（字面上）手术精度实现的本地和远程系统的PID，同时仍然满足可满足太空高度要求的定量稳定性的类型。设计的主要困难是确保机器人的少量挠性不会导致微重力的过度振动。我处于解决这个问题的强烈位置，因为我已经开发了一种使用CMAC来控制灵活接头机器人的特定专业知识。 在先前的工作中，我还研究了在废水处理，发电焚化炉和有机兰肯周期（用于将废热转换为电力的工业工厂）中用CMAC代替PID，并将努力通过类似应用来验证我的方法。 因此，这项研究有可能使环境和减少碳减少努力受益。