Developing neural-fuzzy adaptive controls with stability margins

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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.