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、健壮的控制，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。PID替换应该在性能方面做得更好（更少的错误和努力），同时保证在稳定性方面不会做得更差。我正在申请NSERC Discovery资助，以便在这个问题上取得一些重大进展，特别是为一些对加拿大有益的应用开发结合神经网络和模糊逻辑的机器学习算法。******这个问题的一个有前途的研究方向是修改小脑模型衔接控制器（CMAC）类型的模糊神经网络。CMAC能够在困难的情况下表现优异，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，使其始终具有更好性能的潜力，而不会增加不稳定的风险。******为了保持加拿大在空间机器人领域的领导地位，我的研究小组正在为国际空间站的手术机器人设计控制系统。由于通信信号在理论上的往返时间延迟可达1秒，而在实践中可达7秒，因此我们设想了一种安全的手术模型，外科医生直接与地球上的本地3d打印宇航员模型进行交互，而太空中的远程机器人则在一小段时间后安全跟随。有了NSERC的发现拨款，我们将寻找低级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，在达到外科手术精度的同时，仍然满足满足航天局要求的定量稳定性保证。设计的主要难点是保证机器人的小柔性在微重力环境下不会造成过大的振动；我在解决这个问题上处于有利地位，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。******在之前的工作中，我还研究了用CMAC代替PID在工业工厂中的应用，如废水处理、发电焚烧炉和有机朗肯循环（将废热转化为电能），并将努力在类似的应用中验证我的方法。因此，这项研究有可能有益于环境和减少碳排放的努力。

***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.