Developing neural-fuzzy adaptive controls with stability margins

* 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，这将提高控制系统的性能并保证其稳定性。 我目前正在努力寻找一种通用的替代品，以取代工业和机器人自动化系统中普遍存在的传统PID反馈回路控制（PID增加了误差的比例、积分和导数）。 尽管PID提供了简单、鲁棒的控制，并在世纪里为我们提供了良好的服务，但它们将不能满足未来许多重要系统的系统要求。一个PID替换应该在性能方面做得更好（更少的错误和工作），同时保证在稳定性方面不会做得更差。我请求NSERC Discovery资助，以便在这个问题上取得一些重大进展，特别是开发机器学习算法，将神经网络和模糊逻辑用于一些对加拿大有益的应用。一个有前途的研究方向，这个问题涉及修改小脑模型关节控制器（CMAC）类型的模糊神经网络。CMAC能够在困难的情况下实现高性能，学习补偿非线性、不确定性和周期性干扰。我目前正在修改CMAC控制架构，以便它始终具有更好的性能潜力，而不会增加不稳定的风险。作为保持加拿大在空间机器人技术方面领导地位的努力的一部分，我的研究小组正在为国际空间站拟议中的手术机器人设计控制系统。由于通信信号的理论往返时间延迟长达1秒，实际上长达7秒，我们设想了一种安全的操作模型，其中外科医生直接与地球上宇航员的本地3D打印模型进行交互，而太空中的远程机器人在一小段时间后安全地跟随沿着。通过拟议的NSERC发现补助金，我们将研究用于低级别反馈控制回路的先进解决方案，以取代本地和远程系统的PID，这些系统将实现（字面上）手术精度，同时仍然满足满足空间机构要求的定量稳定性保证。设计中的主要困难是确保机器人的小灵活性不会在微重力下引起过度振动;我在解决这个问题方面处于有利地位，因为我已经开发了使用CMAC控制柔性关节机器人的特殊专业知识。在以前的工作中，我还研究了在工业工厂中用CMAC代替PID，如废水处理，发电焚烧炉和有机朗肯循环（用于将废热转化为电力），并将努力验证我的方法与类似的应用。 因此，这项研究有可能有益于环境和碳减排工作。

***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.